Contemporary aspects of embryonic development, drawing from current literature. Emphasis on the use of experimental approaches to address topics such as polarity in the egg, body axis specification, embryonic patterning, and organogenesis. Students must register for two sections: lecture and discussion.

Credits: 4; LEC: (A1) Tues/Weds/Thurs 10:30 am – 1:00 pm 

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: CAS MA225 OR CAS MA230 OR CDS DS 122.

Credits: 4; LEC: (A1) Mon/Tues/Weds/Thurs 1:00 pm – 3:00 pm; (A2) Mon/Tues/Thurs 6:00 pm – 8:30 pm 

Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.

Prereq: (CAS MA 381 or CAS MA 581)

Credits: 4; LEC: (A1) Mon/Tu/W/Th 9:00 am – 11:00 am

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: CAS MA225 OR CAS MA230.

Credits: 4; LEC: (B1) Mon/Tues/Weds/Thurs 1:00 pm – 3:00 pm 

Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.

Prereq: (CAS MA 381 or CAS MA 581)

Credits: 4; (B1) Mon/Tu/Wed/Th 1:00 pm – 3:00 pm

Basic concepts and techniques of stochastic process as they are most often used to construct models for a variety of problems of practical interest. Topics include: Markov chains, Poisson process, birth and death processes, queuing theory, renewal processes, and reliability.

Prereq: (CAS MA 381 or CAS MA 581)

Credits: 4; (B1) Mon/Tu/Wed/Th 9:00 am – 11:00 am

The field of bioinformatics involves managing and analyzing large biological datasets to better understand complex living systems and improve human health. Key bioinformatics tasks include analyzing genomic sequences and structures, studying evolutionary relationships, interpreting omics experiments, and analyzing gene networks. This course introduces graduate students to foundational problems in bioinformatics and provides hands-on experience with the databases and computational tools used to address these challenges.

The course emphasizes practical skill development in computer labs, enabling students to apply bioinformatics methods in their research. Familiarity with basic molecular biology is recommended. While prior programming knowledge is beneficial, it is not required. Enrollment is currently limited to master’s students in the Bioinformatics Program.

Prereq: M.S. program standing in Bioinformatics.

Instructor: Leyfer; Credits: 4; Tues/Thurs 3:30 pm – 5:15 pm

Description: Bioinformatics is an interdisciplinary field devoted to managing and analyzing large-scale biological data, such as the DNA sequence of the entire human genome, and has revolutionized our understanding of molecular biology. This course will expose students to modern translational bioinformatics studies with a specific focus on Next Generation Sequencing (NGS) technologies. The analysis of the data produced by these techniques requires both the computational skills to develop and apply bioinformatics algorithms along with the biological knowledge to translate the results into clinically relevant findings.  Lecture topics include but are not limited to, genome editing techniques, gene sets and gene set enrichment, whole genome sequencing, and transcriptomics. This course emphasizes practical and hands-on experience developing Nextflow pipelines that perform end-to-end analysis of sequencing data. All work in the course is project-based and will involve replicating key findings from published RNA-sequencing, ChIP-sequencing, and Single Cell RNA-sequencing experiments. The class will also introduce a set of best practices and tools for the development of reproducible and portable computational workflows including version control, and environment management.

Prereq: Basic understanding of biology and genomics. Any of these courses are adequate prerequisites for this course: BF527, BE505/BE605. Students should have some experience programming in a modern programming language (R, python, C, Java, etc).  Students must bring laptop to each class.

Instructor: Orofino; Credits: 4; LEC Section (A1): Mon/Fri 10:10 am – 11:55 am; Lab Sections (A2) Wed 9:05 am – 9:55 am. Students must register for the lecture & lab sections.

This course is for students trained in life sciences with minimal exposure to programming, statistics, and data analysis. The goal of the course is to develop both practical skills and theoretical foundations in handling data sets and developing simple computational solutions to problems arising in biomedical research.

Instructor: TBD; Credits: 4; LEC: Mon/Weds 12:20 pm – 2:05 pm; LAB: Fri 12:20 pm – 2:05 pm. Only one course section so students will automatically be enrolled in the lab when they register for the lecture.

Description: Modern research in the life sciences is an increasingly interdisciplinary endeavor where new fields of study have developed at the interfaces of biology, chemistry, physics, mathematics, and computer science.  In many instances, the development of these new fields goes hand in hand with development of new experimental approaches to surveying the content of the cell.  This course is a tour of experimental methods utilized in the generation of many types of biological data relevant to bioinformatic analyses.  We cover experimental design, methodological limitations, and data quality concerns for widely used techniques for collecting genomic, metagenomic, transcriptomic, proteomic, and metabolomic data (and others).  This includes reiterating the biochemical principles and properties of biomolecules that directly affect data collection and analysis, developing familiarity with the critical reading of primary literature across bioinformatic subdisciplines, and through the final project, in depth research and scientific communication about the experimental generation of data for specific analysis techniques.  This course expands on undergraduate learning to provide an advanced and in-depth consideration of the central dogma and its machines, including topics such as epigenetic regulation, stochasticity in gene expression patterns, and human genomics and genome wide association studies.

Prereq: M.S. or Ph.D. program standing in Bioinformatics.

Instructor: Osborne; Credits: 4; LEC: Tues/Thurs, 9:00 am – 10:45 am

Description: In this course we cover the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design and foundational methods in machine learning. We provide an introduction to important problems in computational biology, and we provide hands on experience analyzing large-scale biological data sets.

Prereq: Fundamentals of programming and algorithm design (EK 127 or equivalent), basic molecular biology (BE 209 or equivalent), statistics and probability (BE 200 or equivalent), or consent of instructor.

Instructor: Galagan; Credits: 4; LEC Section (A1) Tues/Thurs, 1:30 pm – 3:15 pm; Lab Section (B1) Fri, 9:05 am – 9:55 am.  Students must register for both sections.

Description: Introduction to statistical methods relevant to research in the computational sciences. Core topics include probability theory, estimation theory, hypothesis testing, linear models, GLMs, and experimental design. Emphasis on developing a firm conceptual understanding of the statistical paradigm through data analyses.  Required for entering Bioinformatics Ph.D. students.

Prereq: MA 225 & MA 242 or their equivalents.

Instructor: Mukherjee; Credits: 4; LEC (A1) Tues/Thurs, 9:30 am – 10:45 am; DIS Sections: (A2) Mon, 1:25-2:15 pm or (A3) 2:30-3:20 pm

Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: CAS CS 112 or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: TBA; Credits: 4; Students registering for CAS CS 542 must register for two sections: a Lec section, and a Lab section.

LEC Section: (A1) Mon/Weds 2:30 pm – 3:45 pm; LAB Sections: (A2) Weds 4:40 pm – 5:30 pm; (A4) Th 3:35 pm – 4:25 pm; (A5) Th 5:00 pm – 5:50 pm

Project course for first year Bioinformatics Ph.D. students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.

Instructor: Benson; Credits: 2; Wed, 12:20 pm – 3:20 pm; Students can register for any of the 3 sections offered. Meeting times will be determined once groups have been formed.

Description: Modern research in the life sciences is an increasingly interdisciplinary endeavor where new fields of study have developed at the interfaces of biology, chemistry, physics, mathematics, and computer science.  In many instances, the development of these new fields goes hand in hand with development of new experimental approaches to surveying the content of the cell.  This course is a tour of experimental methods utilized in the generation of many types of biological data relevant to bioinformatic analyses.  We cover experimental design, methodological limitations, and data quality concerns for widely used techniques for collecting genomic, metagenomic, transcriptomic, proteomic, and metabolomic data (and others).  This includes reiterating the biochemical principles and properties of biomolecules that directly affect data collection and analysis, developing familiarity with the critical reading of primary literature across bioinformatic subdisciplines, and through the final project, in depth research and scientific communication about the experimental generation of data for specific analysis techniques.  This course expands on undergraduate learning to provide an advanced and in-depth consideration of the central dogma and its machines, including topics such as epigenetic regulation, stochasticity in gene expression patterns, and human genomics and genome wide association studies.

Prereq: M.S. or Ph.D. program standing in Bioinformatics.

Instructor: Osborne; Credits: 4; LEC: Tues/Thurs, 9:00 am – 10:45 am

This course is for PhD students to take part in a laboratory rotation system. Students will become familiar with research activity in Bioinformatics labs. These rotations will help students identify the laboratory in which they will perform their dissertation research. PhD students must complete one 9-week rotation in their first semester of matriculation and two in their second semester.

Instructor: Campbell; Credit: 1; ARR

Required for entering Bioinformatics Ph.D. students. The course will consist of a series of presentations by Bioinformatics faculty that focus on research projects being investigated in their laboratories. Emphasis is placed on the description of collaborative projects involving experimental and computational approaches to Bioinformatics research problems.

Instructor: TBA; Credits: 1; Fri, 12:20 pm – 2:05 pm 

Historically, pseudoscientific theories have provided the justification for establishing and maintaining racial hierarchies, which resulted in centuries of dehumanizing and unethical practices meted out to Blacks, Indigenous, and People of Color (BIPOC). Unfortunately, many of these pernicious ideas persist, such that they hinder BIPOC’s opportunities in Science and exacerbate their health outcomes.

This course traces the historical roots (e.g. mischaracterization of race as a biological construct) and physiological manifestations of racism in science, and examines harmful consequences on victims’ health outcomes.

Prereq: MSc./PhD. program standing in Bioinformatics, Biology, BU Wheelock MSc. in Education for Equity and Democracy, or consent of the instructor.

Instructor: Osborne; Credits: 4; LEC: (A1) Tues/Thurs 12:30 pm – 3:15 pm 

Bioinformatics is an interdisciplinary filed combing data science, computing, algorithms, and programming with biology. This course teaches the fundamental algorithms that form the backbone of modern bioinformatics as well as their implementations and applications to data. Topics covered include genome assembly, sequence alignment, phylogenetic trees, gene regulation, and large-scale genomics data as well as associated computational methods including graph algorithms, dynamic programming, combinatorial pattern matching, tree algorithms, and machine learning.

Prereq: DS 320 (or equivalent introduction to algorithms) is required. Prior knowledge of Python is required. No background in biology is required.

Instructor: Przytycki; Credits: 4; LEC: (A1) Tues/Thurs 11:00 am – 12:15 pm; DISC (A2): Wed 10:10 am – 11:00 am

Introduction to R, the computer language written by and for statisticians. Emphasis on data exploration, statistical analysis, problem solving, reproducibility, and multimedia delivery.

Prereq: (CASCS111) (or equivalent), and at least one course in statistics.

Instructor: TBA; Credits: 4; Students registering for MA615 A1 must also register for a discussion section. LEC: (B1) Mon/Wed/Fri 2:30 pm – 3:20 pm; Discussion Sections: (B2) Mon 3:35 – 4:25; (B3) Mon 10:10 – 11:00

Covers practical skills in working with data and introduces a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. Emphasizes hands-on application of methods via programming.

Instructors: Galletti; Credits: 4; Students registering for CAS CS 506 must register for two sections: a Lec section and a Lab section.

Lecture Section (A1) Mon/Wed 4:30 pm – 6:15 pm; Lab Sections: (A5) Weds 10:10 am – 11:00 am; (A6) Weds 11:15 am – 12:05 pm; (A7) Wed 12:20 pm – 1:10 pm; (A8) Wed 1:25 pm – 2:15 pm; (A9) Weds 2:30 pm – 3:20 pm

Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: CAS CS 112 or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: TBA; Credits: 4; Students registering for CAS CS 542 must register for two sections: a Lec section, and a Lab section.

LEC Section: (A1) Mon/Weds 2:30 pm – 3:45 pm; LAB Sections: (A2) Weds 4:40 pm – 5:30 pm; (A4) Th 3:35 pm – 4:25 pm; (A5) Th 5:00 pm – 5:50 pm

Students will learn how to conduct statistical analysis using the public domain and free statistical software, R. Many public, private, and international organizations use R to conduct analysis, thus experience with R is a great skill to add to one’s credentials. R offers flexibility, ranging from ease of writing code for simple tasks (e.g. using R as a calculator) to implementing complex analyses using cutting-edge statistical methods and models. Additionally, the R language provides a rich environment for working with data, especially for statistical modeling, graphics, and data visualization. This course will emphasize data manipulation and basic statistical analysis including exploratory data analysis, classical statistical tests, categorical data analysis, and regression. Students will be able to identify appropriate statistical methods for the data or problems and conduct their own analysis using the R environment. This hands-on and project-based course will enable students to develop skills to solve statistical problems using R. R can be used as an alternative or in addition to SAS (BS723). R is compatible with Apple OS, Windows, and Unix environments.

Prereq: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800 or consent of instructor.

Instructor: Lee; Credits: 4; LEC: (A1) Fri 2:00 pm – 4:50 pm; LEC (B1): Thurs 2:00 pm – 4:50 pm

This course covers applications of modern statistical methods using R, a free and open source statistical computing package with powerful yet intuitive graphic tools. R is under more active development for new methods than other packages. We will first review data manipulation and programming in R, then cover theory and applications in R for topics such as linear and smooth regressions, survival analysis, mixed effects model, tree based methods, multivariate analysis, boot strapping and permutation.

Prereq: SPH BS 723 or SPH BS 730 or consent of instructor.

Instructor: Yang; Credits: 4; LEC: (A1) Fri 10:00 am – 12:50 pm

Examines critical components of systems biology, including design principles of biological systems (e.g., feedback, synergy, cooperativity), and the generation and analysis of large-scale datasets (e.g., protein- protein interaction, mRNA expression).

Prereq: (CASBI552) or consent of the instructor.

Instructor: Fuxman-Bass; Credits: 4; LEC: (A1) Mon/Weds 2:30 pm – 4:15 pm

Synthesis, structure, function, regulation of macromolecules (DNA, RNA, protein). Prokaryotic and eukaryotic molecular biology. Topics include: replication, repair, recombination, transcription, translation, 5-methylcytosine, transcription factors, DNA looping (enhancer- promoter, insulator, etc.), histone modification/chromatin remodeling, non-coding RNA. Discussion of genetic and recombinant DNA techniques, including CRISPR/Cas9.

Instructor: Loechler; Credits: 4; Students registering for CAS BI 552 must register for two sections: a Lec section and a Dis section. Thur. 6:30-10:30pm reserved for exams. LEC: (A1): Tues/Thurs 11:00 am – 12:15 pm & Thurs 6:30 pm – 10:30 pm (for exams); (A2): Tues/Thurs 3:30 pm – 4:45 pm & Thurs 6:30 pm – 10:30 pm (for exams); DISC Sections: (B1) Tues 5:00 pm – 5:50 pm; (B2) Wed 10:10 am – 11:00 am; (B3) Wed 1:25 pm – 2:15 pm; (B4) Weds 2:30 pm – 3:20 pm; (B5) Wed 3:35 pm – 4:25 pm; (B6) Thurs 5:00 pm – 5:50 pm

Contemporary aspects of development, drawing from current literature. Emphasis on the use of experimental approaches to address topics such as polarity in the egg, body axis specification, embryonic patterning and organogenesis. Three hours lecture, one hour discussion.

Instructor: Bradham; Credits: 4; Students registering for CAS BI 610 must register for two sections: a Lecture section, and a Discussion section.  LEC: (A1) Tues/Thurs 2:00 pm – 3:15 pm, DIS: (B1) Wed 1:25 pm – 2:15 pm; (B2): Wed 2:30 – 3:20

Survey course in neurobiology. Topics covered include cell biology of the neuron, development of the nervous system, synaptic plasticity, learning and behavior, and network modeling. Three hours lecture, one hour discussion.

Instructor: Rosene; Credits: 4; Students registering for CAS BI 755 must register for two sections: Lecture section and Discussion section.  LEC: (A1) Tues 3:30 pm – 6:00 pm & DIS (B1) Thurs 3:30 pm – 5:50 pm 

This course covers a variety of statistical applications to human genetic data, including collection and data management of genetic and family history information, and statistical techniques used to identify genes contributing to disease and quantitative traits in humans. Specific topics include basic population genetics, linkage analysis and genetic association analyses with related and unrelated individuals.

Prereq: SPH BS 723, BS 730 or equivalent as determined by instructor.

Instructor: Choi; Credits: 4; LEC: (A1) Mon 2:00 pm – 4:50 pm

Post-introductory course in linear models, with focus on both principles and practice. Simple and multiple linear regression, weighted and generalized least squares, polynomials and factors, transformations, regression diagnostics, variable selection, and a selection from topics on extensions of linear models.

Prereq: CAS MA 214 & CAS MA 242 & CAS MA 581 or consent of instructor.

Instructor: Castrillon; Credits: 4; Students registering for CAS MA 575 must register for three sections: a Lec section, and a Dis section, and a Lab section. LEC: (A1) Tues/Thurs 8:00 am – 9:15 am; DISC: (B1) Mon 10:10 am – 11:00 am; (B2) Mon 12:20 pm – 1:10 pm, (B3) Mon 1:25 pm – 2:15 pm; (B4) Mon 3:35 pm – 4:25 pm; LAB (C1) Wed 10:10 am – 11:00 am; (C2) Wed 12:20 pm – 1:10 pm; (C3) Wed 2:30 pm – 3:20 pm; (C4) Wed 3:35 pm – 4:25 pm 

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: CAS MA 225 OR CAS MA 230 or consent of instructor

Instructor: Ganguly; Credits: 4; Students registering for MA581 must register for a lecture and discussion section. LEC: (A1) Mon/Wed 12:20 pm – 2:05 pm; DISC: (A2) Mon 1:25 pm – 2:15 pm, (A3) Mon 3:35 pm – 4:25 pm, (A4) Tues 8:00 am – 8:50 am, (A5) Tues 9:05 am – 9:55 pm or (A6) Tues 3:35 pm – 4:25 pm

Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.

Prereq: CAS MA 581

Instructor: Weiner; Credits: 4; Students registering for MA581 must register for a lecture and discussion section. LEC: (A1) Mon/Wed/Fri 10:10 am – 11:00 am & DIS Section: (A2) Mon 2:30 pm – 3:20 pm, (A3) Mon 3:35 – 4:25; (A4) Mon 4:40 – 5:30; or (A5) Tues 3:35 pm – 4:25 pm

Second course in statistics, embodying basic statistical methods used in educational and social science research. Reviews all basic concepts covered in a first statistics course and presents, in detail, more advanced topics such as analysis of variance, covariance, experimental design, correlation, regression, and selected nonparametric techniques. A problem-solving course; students carry out analysis of data taken from educational and other social science sources.

Prereq: Graduate standing in education or in the social sciences.

Instructor: Moore; Credits: 4; LEC: (A1) Mon 2:30 pm – 5:15 pm

Measure theory and integration on measure spaces, specialization to integration on locally compact spaces, and the Haar integral. Lp spaces, duality, and representation theorems. Introduction to Banach and Hilbert spaces, open mapping theorem, spectral theorem for Hermitian operators, and compact and Fredholm operators.

Graduate Prerequisites: (CASMA512) or substantial mathematical experience.

Instructor: Kon; Credits: 4; LEC: (A1) Tues/Thurs 12:30 pm – 1:45 pm

Advanced seminar in topics in statistics of current research interest.

Prereq: CAS MA782

Instructor: TBA; Credits: 4; LEC: (A1) Tues/Thurs 9:30 am – 10:45 am

Introductory biochemistry course that in one semester covers the major principles of biochemistry; proteins, nucleic acids, carbohydrates, lipids, and metabolism. Emphasis on how knowledge was derived and the theoretical principles governing biochemistry.

Prereq: Phd Students Only.

Instructor: Waxman; Credits: 4; LEC: (A1) Tues/Thurs 2:00 pm – 3:15 pm; DISC: (A2) Wed 4:30 pm – 6:15 pm

Current topics of research in physical chemistry. The course content varies with instructor.

Prereq: CAS CH652 or equivalent or consent of instructor..

Instructor: Son; Credits: 4; LEC: (A1) Tues/Thurs 2:00 pm – 3:15 pm

Current topics of research in physical chemistry. The course content varies with instructor.

Prereq:  CASCH 651 or equivalent or consent of instructor.

Instructor: Sharifzadeh; Credits: 4; LEC: (A1) Tues/Thurs 3:30 pm – 5:15 pm

Provides engineering perspectives on the building blocks of living cells and materials for biotechnology. Focuses on origins and synthesis in life and the laboratory, including biological pathways for synthesis of DNA, RNA and proteins; transduction, transmission, storage and retrieval of biological information by macromolecules; polymerase chain reaction, restriction enzymes, DNA sequencing; energetics of protein folding and trafficking; mechanisms of enzymatic catalysts and receptor-ligand binding; cooperative proteins, multi- protein complexes and control of metabolic pathways; generation, storage, transmission and release of biomolecular energy; and methods for study and manipulation of molecules which will include isolation, purification, detection, chemical characterization, imaging and visualization of structure.

Instructor: Banskota; Credits: 4; Students registering for BE 605 must register for a lecture and lab section. LEC: (A1) Tues/Thurs 9:00 am – 10:45 am; LAB Sections: (B1) Wed 2:30 – 5:15; (B2) Thurs 12:30 – 3:15; (B3) Fri 10:10 – 11:55 

Selected topics in discrete mathematics. Formal systems. Mathematical deduction. Logical concepts. Theorem proving. Sets, relations on sets, operations on sets. Functions, graphs, mathematical structures, morphisms, algebraic structures, semigroups, quotient groups, finite-state machines, their homomorphism, and simulation. Machines as recognizers, regular sets. Kleene theorem.

Prereq: (CASMA124) or equivalent

Instructor: Levitin; Credits: 4; LEC: (A1) Mon/Weds 2:30 pm – 4:15 pm 

Advanced study of a specific research topic in biomedical engineering. Intended primarily for advanced graduate students. In this class students will:

– Apply AI/ML to Systems-level Disease biology: from classical ML methods to cutting-edge large language models.

 – Integrate multimodal biomedical data (omics, imaging, EHR, wearables).

– Use generative AI for drug discovery, protein engineering, and hypothesis generation.

– Explore advanced AI paradigms (Hopfield networks, reinforcement learning, neuro-symbolic reasoning).

– Critically assess AI safety, bias, interpretability, and reproducibility in biomed research

Prereq: Graduate standing or consent of instructor.

Instructor: Kasif; Credits: 4; LEC: (A1) Tues/Thurs 1:30 pm – 3:15 pm 

The process of drug discovery is complex especially when a drug is intended to treat a neurological disease. This discussion-heavy course examines the specific challenges of modern neuroscience drug discovery, including: target selection, pharmacokinetics, pharmacodynamics, modeling of disease states within the context of the drivers and limitations of the Drug Discovery Industry.

Instructor: Bushell; Credits: 4; LEC: (A1) Mon/Weds 10:10 am – 11:55 am 

The microbial community – referred to as “microbiome” – that colonizes the human body plays an important role in our health. Topics include (1) the human microbiome; and (2) fundamental aspects of the interactions between animals and the microorganisms that reside with them. Three hours lecture; one hour discussion.

Graduate Prerequisites: CASBI203 (or equivalent) & CASBI206 (or equivalent) or consent of instructor.

Instructor: Frydman; Credits: 4; LEC: (A1) Tues/Thurs 3:30 pm – 4:45 pm; DISC (A2) Thurs 5:00 pm – 5:50 pm

In-depth analysis of current topics in molecular biology regarding the flow of information in the nucleus of eukaryotic cells. Focus on primary literature. Includes genomic flexibility, signal transduction to the nucleus, chromatin structure, gene expression, cell cycle checkpoints, health-related topics.

Graduate Prerequisites: CAS BI553 or consent of instructor.

Instructor: Fiszbein; Credits: 4; LEC: (A1) Tues/Thurs 12:30 pm – 1:45 pm; DISC (A2) Mon 2:30 pm – 3:20 pm

The field of bioinformatics involves managing and analyzing large biological datasets to better understand complex living systems and improve human health. Key bioinformatics tasks include analyzing genomic sequences and structures, studying evolutionary relationships, interpreting omics experiments, and analyzing gene networks. This course introduces graduate students to foundational problems in bioinformatics and provides hands-on experience with the databases and computational tools used to address these challenges.

The course emphasizes practical skill development in computer labs, enabling students to apply bioinformatics methods in their research. Familiarity with basic molecular biology is recommended. While prior programming knowledge is beneficial, it is not required. Enrollment is currently limited to master’s students in the Bioinformatics Program.

Instructor: Leyfer; Credits: 4; LEC/LAB: Tues/Thurs 1:30 pm – 3:15 pm

Students must bring laptop to each class.

Description: Describes relational data models and database management systems. Teaches the theories and techniques of constructing relational databases with emphasis on those aspects needed for various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL. Summarizes currently existing biological databases and the Web-based programming tools for their access. Object-oriented modeling is introduced primarily as a design aid for dealing with the particular complexities of biological information in standard RDB design. Emphasis will be on those problems associated with dealing with data whose nomenclature and interrelationships are undergoing rapid change.

Instructor: Benson; Credits 4; Tues/Thurs 3:30 pm – 6:15 pm

This course enrolls students who intend to pursue careers in medicine, dental medicine and/or medical research (either academic or industrial) – in particular, students in the BF for Translational Medicine track of the Bioinformatics MS program. After commencing briefly with general introductory material (published reviews and other relevant back-ground information), students will proceed to examine, discuss and evaluate recent papers that directly illustrate the use of bioinformatics either in pre-clinical or clinical research settings. Papers will be drawn from high-impact journals such as Nature, Science, PNAS, Cell, and Science Translational Medicine. Students will take turns presenting the papers to the class and provide a critical review of each, both orally and in writing. They will also complete a term paper in the form of a research proposal directed to the goal of using bioinformatics to advance a medical procedure – either diagnostic or therapeutic. Brief guest presentations by researchers in BUSM laboratories will be arranged as appropriate.

Instructor: Steiling; Credits: 2; Day/Time: Thurs 9:00 am – 10:45 am

Internships provide the bridge between classroom/laboratory study and real- world employment. Each student must complete an internship with a minimum of 400 hours of on-the-job experience (e.g., 10 weeks full-time work in the summer). The format is very flexible, and part-time internships running concurrently with classes or employment are acceptable.

At the conclusion of the internship the student must submit a report that summarizes (a) the project he/she worked on (in general terms), (b) work accomplished (with very specific emphasis on the student’s contribution), and (c) description of the impact of the experience on the student’s professional development. Reports need not be more than two double-spaced text pages in length, though longer reports are acceptable. Append any detailed material that supports the narrative (tables, figures, publications, progress reports etc.). In cases where confidentiality agreements restrict release of pertinent project details, the report can describe the work in terms sufficiently general as to be acceptable to the company in which the work was done.

Prereq: Students must receive approval prior to registering by submitting the MS Internship Approval Form to this dropbox.

Instructor: Labadorf; Credits: variable (typically 2); This course is required for internship credit and does not have class meetings.

Description: The course focuses on mathematical models for exploring the organization, dynamics, and evolution of biochemical and genetic networks. Topics include: introductions to metabolic and genetic networks, deterministic and stochastic kinetics of biochemical pathways; genome-scale models of metabolic reaction fluxes; models of regulatory networks; modular architecture of biological networks.

Prereq: CAS MA 226 & CAS MA 242. ENG EK 102 can be used in lieu of the CAS MA 242 pre-req. Familiarity with differential equations and linear algebra at equivalent levels and the consent of instructor can be used in lieu of both pre-reqs.

Instructors: Segre; Credits: 4; Mon/Wed 10:10 am – 11:55am

Description: Describes relational data models and database management systems. Teaches the theories and techniques of constructing relational databases with emphasis on those aspects needed for various biological data, including sequences, structures, genetic linkages and maps, and signal pathways. Introduces relational database query language SQL. Summarizes currently existing biological databases and the Web-based programming tools for their access. Object-oriented modeling is introduced primarily as a design aid for dealing with the particular complexities of biological information in standard RDB design. Emphasis will be on those problems associated with dealing with data whose nomenclature and interrelationships are undergoing rapid change.

Instructor: Benson; Credits 4; Tues/Thurs 3:30 pm – 6:15 pm

Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: CAS CS 112; or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Considine; Credits: 4; Students registering for CAS CS542 must register for two sections: a Lec section, and a Lab section. LEC: (A1) Mon/Weds 2:30 pm – 3:45 pm; LAB: (B1) Fri 1:25 pm – 2:15 pm OR (B2) Fri 2:30 pm – 3:20 pm OR (B3) Fri 3:35 pm – 4:25 pm

Mathematical and statistical bases of bioinformatics methods and their applications. Hidden Markov models, kernel methods, mathematics of machine learning approaches, probabilistic sequence alignment, Markov chain Monte Carlo and Gibbs sampling, mathematics of phylogenetic trees, and statistical methods in microarray analysis.

Prereq: Graduate standing.

Instructor: Kon; Credits 4; Tues/Thurs 2:00 pm – 3:15 pm

Description: This paper- and problem-based course focuses on functional genomics topics such as genetic variation, genome organization, and mechanisms of transcriptional and post-transcriptional gene regulation. Up-to-date methods include NGS, genome editing, ChIP-seq, chromatin accessibility assays, transcriptomics, and proteomics.

Prereq: Background in molecular biology.

Instructor: Fuxman Bass; Credits 4; Tues/Thurs 9:00 am – 10:45 am

Description: This course will address the ethical, legal and scientific aspects of the new genetics. Students in bioinformatics will discuss the questions raised from another view that they normally would not see. As part of the new technologies, individuals, families and society as a whole will have to make decisions that will affect everyone.  We will analyze cases, question the legal system’s role in regulating this field, discuss options for now and analyze cases. Gene therapy, DNA forensics, new reproductive techniques and cloning are only a few of the topics that will be addressed.

Instructor: Garland; Credits: 4; Date/time Tues 3:30 pm – 6:15 pm 

Description: Project course for first year Bioinformatics graduate students. Open-ended problems will involve bioinformatics as a key element, typically requiring the use of large data sets and computational analysis to make predictions about molecular function, molecular interactions, regulation, etc. Projects will be proposed by the Bioinformatics program faculty and selected by student in teams of three. The end result will be a set of predictions, some of which can be validated retrospectively using data available through online sources and some of which will require experimental validation. During the last 2 months of the academic year, teams will design feasible validation experiments in consultation with the experimental faculty.  1^st year Bioinformatics PhD students only.

Instructor: TBA; Credits: 2; Thurs 10:30 am – 1:00 pm

Description: BF821 is a graduate seminar covering current topics in bioinformatics. This is achieved through the critical reading, presentation, and discussion of recent literature. Additionally, the course is intended to give students the opportunity to practice and improve their scientific presentation abilities. As such, peer feedback on presentations is an integral aspect of the course. Students will present twice during the semester so that they may improve upon their presentation skills based on peer comments.

Instructor: TBD; Credits: 2; Fri 10:10 am – 11:55 am

Description: Three laboratory rotations are required during a Bioinformatics Ph.D. student’s first year. Rotations typically last for a minimum of nine weeks. It is expected that the student will participate in the lab full time except for time spent on courses. One rotation must be experimental, one computational, and the third can be either. Students who participate in the Summer Wet-Lab Experience prior entering the program receive credit toward one of the required rotations.

Instructor: Campbell; Credits: 1 per rotation (3 total); Day: To be arranged

Description: The RCR course is a 1 credit, non-tuition bearing course open to all PhD students and postdoctoral scholars. This 10-week course will meet once weekly for 50-minute sessions and fulfills the new National Science Foundation requirements as well as existing National Institutes of Health requirements. The course will be taught each term by a faculty member, alongside rotating co-instructors.

Doctoral and postdoctoral researchers who will be taking advanced RCR training sign up and complete the course within two years of requirement notification. Training must be performed once every four years.

Instructor: Easterling; Credits: 1; (A1) January 23 – February Fri 9:00 am – 12:20 pm OR  (A2) March 19 – April 2Thurs 1:00 pm – 4:20

Description: This course introduces the R programming language through the lens of practitioners in the biological sciences, particularly biology and bioinformatics. Key concepts and patterns of the language are covered, including:

–   RStudio

–   Data wrangling with tidyverse

–   Data visualization with ggplot

–   Essential biological data shapes and formats

–   Core bioconductor packages

–   Basic data exploration, including elementary statistical modeling

    and summarization

–   Elementary Data Science concepts

–   “Toolifying” R scripts

–   Communicating R code and results with RMarkdown

–   Building R packages and unit testing strategies

–   Building interactive tools with RShiny

About 1/3 of the materials are inspired by the online textbook [R for Data Science] (https://r4ds.had.co.nz), while the rest has been developed by practicing bioinformaticians based on their experiences.

Weekly programming assignments will help students apply these techniques to realistic problems involving analysis and visualization of biological data. Students will be introduced to a unit testing paradigm that will help them write correct code and deposit all their code into github for evaluation. Students will implement an end-to-end project that begins with one of a set of provided datasets, implements a set of data summarization and exploration operations on it, and allows interaction with an RShiny app.

Relevant links (zoom and textbook) are available on Blackboard as a reference. The blackboard site will only be used for announcements and to host links.

Instructor: Orofino; Credits: 4; LEC/LAB: Mon/Fri 10:10 am – 11:55 am and Wed 9:05 am – 9:55 am

In this course, students will gain an understanding of the fundamentals in deep learning and then apply those concepts in exercises and applications in python. We’ll start with the origins of artificial neural networks, learn about loss functions, understand gradient descent, back propagation and various training optimization techniques. Students will be familiar with canonical network architecture such as multi-layer perceptions, convolutional neural networks, recursive neural networks, LSTMs and GRU, attention and transformers. Through explanations, examples and exercises students will build intuition on how deep learning algorithms work and how they are implemented in popular deep learning frameworks such as PyTorch. Students will be able to define, train and evaluate deep learning models as well as adapt deep learning frameworks to new functionality. Students will gain exposure to pre-trained large language models and other foundation models and the concepts of few-shot learning and reasoning. Finally, students will be able to apply many of the techniques they learned in a final class project.

Instructor: Grades; Credits: 4; Students registering for CDS DS542 must register for two sections: a Lec section, and a Disc section. LEC: (A1) Tues/Thurs 3:30 pm – 4:45 pm; DISC: (A2) Fri 10:10 am – 11:00 am OR (A3) Fri 11:15 am – 12:05 pm OR (A4) 12:20 pm – 1:10 pm OR (A5) 1:25 – 2:15 pm

We are living in the age of large-scale biological data. Over the last two decades, the cost of genetic sequencing has decreased faster than Moore’s law, meaning that the abundance of data is outpacing the improvement in computational power to analyze it. So while these data are incredibly exciting, extracting biological meaning from the sheer quantity of heterogeneous data being generated requires cutting-edge computational methods. In this course we will study the modern algorithms and machine learning tools that have been developed to extract biological insights from these data including deep learning approaches such as for protein structure prediction and learning from sequence data, Bayesian approaches for gene function prediction and network construction, and graph-based approaches. We will examine these through a lens of common pitfalls in learning from biological data and how to best avoid them. By the end of this course students will understand both the contexts in which it is appropriate to use such tools as well as their limitations.

Prereq: One of CDS DS430/630, CDS DS436/636, ENG BE562, CDS DS526, or equivalent, or prior experience with computational biology.

Instructor: Przytycki; Credits: 4; LEC: (A1) Mon/Wed 10:10 am – 11:55 am

Covers practical skills in working with data and introduces a wide range of techniques that are commonly used in the analysis of data, such as clustering, classification, regression, and network analysis. Emphasizes hands-on application of methods via programming.

Prereq: CAS CS 108 or CAS CS 111; CAS CS 132 or CAS MA 242 or CAS MA 442. CASCS 112 is recommended.

Instructor: Galletti; Credits: 4; Students registering for CAS CS506 must register for two sections: a Lec section and a Lab section. LEC Section (A1) Mon/Wed 4:30 – 6:15; and LAB (B1) Wed 9:05 – 9:55 OR (B2) Wed 10:10 – 11:00 OR (B3) Wed 11:15 – 12:05 OR (B4) Wed 12:20 – 1:10 OR (B5) Wed 1:25 – 2:15 OR (B6) Wed 2:30 – 3:20

Introduction to modern machine learning concepts, techniques, and algorithms. Topics include regression, kernels, support vector machines, feature selection, boosting, clustering, hidden Markov models, and Bayesian networks. Programming assignments emphasize taking theory into practice, through applications on real-world data sets.

Prereq: CAS CS 112; or equivalent programming experience, and familiarity with linear algebra, probability, and statistics.

Instructor: Considine; Credits: 4; Students registering for CAS CS542 must register for two sections: a Lec section, and a Lab section. LEC: (A1) Mon/Weds 2:30 pm – 3:45 pm; LAB: (B1) Fri 1:25 pm – 2:15 pm OR (B2) Fri 2:30 pm – 3:20 pm OR (B3) Fri 3:35 pm – 4:25 pm

Introduction to R, the computer language written by and for statisticians. Emphasis on data exploration, statistical analysis, problem solving, reproducibility, and multimedia delivery.

Prereq: CAS CS111 or equivalent, and at least one course in statistics.

Instructor: Chou; Credits: 4; Students registering for CAS MA 615 must register for two sections: a Lec section and a Discussion section.  LEC Section: (A1) Mon/Wed/Fri 2:30 pm – 3:20 pm; DISC Section: (A2) Wed 3:35 – 4:25 OR (A3) Wed 4:40 – 5:30; LEC Section (B1) Tues/Thurs 2:00 – 3:15; DISC Section: (B2) Wed 2:30 – 3:20 OR (B3) Wed 3:35 – 4:25

Students will learn how to conduct statistical analysis using the public domain and free statistical software, R. Many public, private, and international organizations use R to conduct analysis, thus experience with R is a great skill to add to one’s credentials. R offers flexibility, ranging from ease of writing code for simple tasks (e.g. using R as a calculator) to implementing complex analyses using cutting-edge statistical methods and models. Additionally, the R language provides a rich environment for working with data, especially for statistical modeling, graphics, and data visualization. This course will emphasize data manipulation and basic statistical analysis including exploratory data analysis, classical statistical tests, categorical data analysis, and regression. Students will be able to identify appropriate statistical methods for the data or problems and conduct their own analysis using the R environment. This hands-on and project-based course will enable students to develop skills to solve statistical problems using R. R can be used as an alternative or in addition to SAS (BS723). R is compatible with Apple OS, Windows, and Unix environments.

Prereq: SPH PH 717 or SPH BS 704 or SPH BS 700 or SPH BS 800 or consent of instructor.

Instructor: Cheng; Credits: 4; Mon 10:00 am – 12:50 pm; Held at the MED Campus

The goal of this course is for the students to develop a good understanding and hands-on skills in the design and analysis of ‘omics’ data from microarray and high-throughput sequencing experiments, including data collection and management, statistical techniques for the identification of genes that have differential expression in different biological conditions, development of prognostic and diagnostic models for molecular classification, and the identification of new disease taxonomies based on their molecular profile. These topics will be taught using real examples, extensively documented hands- on work, class discussion and critical reading. Students will be asked to analyze real gene expression data sets in their homework and final project. Principles of reproducible research will be emphasized, and students will become proficient in the use of the statistical language R (an advanced beginners knowledge of the language is expected of the students entering the class) and associated packages (including Bioconductor), and in the use of R markdown (and/or electronic notebooks) for the redaction of analysis reports.

Prereq: Knowledge of basic statistics techniques (SPHBS704 or SPHPH717 or equivalent) and basic statistical computing skills using R (SPHBS730 or e equivalent) or consent of instructor.

Instructor: Weber; Credits: 2; Fri 2:00 pm – 4:50 pm; Only meets Mar 18 – May 6; Held at the MED Campus

This course covers applications of modern statistical methods using R, a free and open source statistical computing package with powerful yet intuitive graphic tools. R is under more active development for new methods than other packages. We will first review data manipulation and programming in R, then cover theory and applications in R for topics such as linear and smooth regressions, survival analysis, mixed effects model, tree based methods, multivariate analysis, boot strapping and permutation.

Prereq: (SPH BS 730) or consent of instructor.

Instructor: Patil; Credits 4; Fri 10:00 am – 12:50 pm; Held at the MED Campus

The purpose of this course is to present Bayesian modeling techniques in a variety of data analysis applications, including both hypothesis and data driven modeling. The course will start with an overview of Bayesian principles through simple statistical models that will be used to introduce the concept of marginal and conditional independence, graphical modeling and stochastic computations. The course will proceed with the description of advanced Bayesian methods for estimation of odds and risk in observational studies, multiple regression modeling, loglinear and logistic regression, hierarchical models, and latent class modeling including hidden Markov models and application to model-based clustering. Applications from genetics, genomics, and observational studies will be included. These topics will be taught using real examples, class discussion and critical reading. Students will be asked to analyze real data sets in their homework and final paper.

Prereq: At least one course of statistics to cover principles of probability and statistical inference, linear and logistic regression. Knowledge of R.

Instructor: Doros; Credits: 2; Fri 2:00 pm – 4:50 pm; Only meets Jan 20 – Mar 17; Held at the MED Campus

Statistical tools used to perform genetic association analysis are used to help unravel the genetic component of complex diseases. Investigators interested in the genetic analysis of complex traits need a basic understanding of the strengths and weaknesses of these methodologies. This course will provide the student with practical, applied experience in performing genome wide association analyses (GWAS) and in using the results of GWAS to better understand the biologic basis of disease. Additional special topics may include analysis of mitochondrial DNA and genetic methylation. Special emphasis is placed on understanding assumptions and issues related to statistical methodologies. The course is taught in a computer lab; in-class time will include didactic lecture and hands on applications using the linux BU shared computing cluster (SCC), R, and specialized genetics software for homework assignments.

Prereq: SPH BS 723 or SPH BS 730; or consent of instructor.

Instructor: Lunetta; Credits: 4; Wed 2:00 pm – 4:50 pm; Held at the MED Campus

The Cancer Bioinformatics course provides an in-depth exploration of advanced computational methods and analysis in cancer research. Students will analyze high-throughput sequencing data, including mutation calling, copy number alterations, discovery of mutations that drive tumor development and tumor phylogenies, while learning statistical modeling and machine learning techniques. The course integrates computational tools such as Linux, Python, and R alongside cloud platforms like Terra and Docker, to process and visualize large-scale datasets. Topics include next-generation sequencing (NGS), tumor biology, RNA-Seq, single-cell RNA-Seq, DNA repair signatures, proteomics, and epigenetics. Emphasizing both theoretical foundations and practical skills, the course prepares students to tackle complex challenges in cancer genomics and clinical bioinformatics.

Instructor: Leshchiner; Credits: 3; Thur 2:40 pm – 5:40 pm (NOTE NEW START TIME); Held at the MED Campus

Provides an introduction to the molecular building blocks and the structure of three major components of the living cells: the nucleic acids, the phospho- lipids membrane, and the proteins. The nucleic acids, DNA and RNA, linear information storing structure as well as their three-dimensional structure are covered in relationship to their function. This includes an introduction to information and coding theory. The analysis tools used in pattern identification representation and functional association are introduced and used to discuss the patterns characteristic of DNA and protein structure and biochemical function. The problems and current approaches to predicting protein structure including those using homology, energy minimization, and modeling are introduced. The future implications of our expanding biomolecular knowledge and of rational drug design are also discussed.

Prereq: (CAS PY212 & CAS CH131) or CAS CH102

Instructor: Vajda; Credits: 4; LEC (A1)Tues/Thurs 3:30 – 5:15 and LAB (C1) TBA

This course focuses on eukaryotic gene regulation. Course topics include genome organization and DNA rearrangement, RNA interference and noncoding RNAs, gene editing, mouse transgenic approaches, signal transduction pathways, chromatin structure, and cell cycle. Research articles will be discussed.

Prereq: CAS BI 552; (CAS BI/CH 421/422 recommended.)

Instructor: Naya; Credits: 4; LEC: Tues/Thurs 9:00 am – 10:45 am.

The process of drug discovery is complex especially when a drug is intended to treat a neurological disease. This discussion-heavy course examines the specific challenges of modern neuroscience drug discovery, including: target selection, pharmacokinetics, pharmacodynamics, modeling of disease states within the context of the drivers and limitations of the Drug Discovery Industry.

Instructor: Bushell; Credits: 4; LEC: Mon/Wed 12:20 pm – 2:05 am.

The microbial community – referred to as “microbiome” – that colonizes the human body plays an important role in our health. Topics include (1) the human microbiome; and (2) fundamental aspects of the interactions between animals and the microorganisms that reside with them. Three hours lecture; one hour discussion.

Prereq: CASBI203 (or equivalent) & CASBI206 (or equivalent) or consent of instructor.

Instructor: Frydman; Credits: 4; LEC: Tues/Thurs 3:30 pm – 4:45 pm and Fri 5:00 pm – 5:50 pm

Current understanding of essential topics and important problems in modern cell biology, with emphasis on recent experimental findings, research strategies and approaches, and new techniques for investigating how cells work. Three hours lecture, one hour discussion.

Instructor: Bradham; Credits: 4; Students registering for CAS BI735 must register for two sections: a Lec section, and a Dis section. LEC: (A1) Tues/Thurs 11:00 pm – 12:15 pm, DISC: (B1) Wed 12:20 pm – 2:05 pm

Introduction to physical chemical principles with topics in biochemistry, solution and solid phase chemistry of biomolecules as studied by equilibrium, hydrodynamics, and spectroscopic/quantum mechanical methods.

Instructor: Szymczyna; Credits: 4; Students registering for CAS CH525 must register for two sections: a Lec section, and a Dis section. LEC: (A1) Mon/Wed 5:30 pm – 6:45 pm, DISC (B1) Tues 3:45 pm – 4:35 pm OR (B2) Wed 12:20 pm – 1:10 pm OR (B3) Wed 3:35 pm – 4:25 pm OR (B4) Tues 5:00 pm – 5:50 pm.

Current topics of research in chemical physics.

Instructor: Coker; Credits: 4; LEC: (A1) Tues/Thurs 11:00 am – 12:15 am

Numerical solutions of equations, iterative methods, analysis of sequences. Theory of interpolation and functional approximation, divided differences. Numerical differentiation and integration. Polynomial theory. Ordinary differential equations.

Prereq: CAS MA225 OR CAS MA230

Instructor: Fried; Credits: 4; Mon/Wed/Fri 3:45 pm – 4:25 pm

Post-introductory course on linear models. Topics to be covered include simple and multiple linear regression, regression with polynomials or factors, analysis of variance, weighted and generalized least squares, transformations, regression diagnostics, variable selection, and extensions of linear models. Effective Fall 2019, this course fulfills a single unit in the following BU Hub area: Quantitative Reasoning II, Teamwork/Collaboration.

Prereq: (CAS MA 214 & CAS MA 242 & CAS MA 581) or consent of instructor.

Instructor: Castrillon; Credits 4; Students registering for CAS MA575 must register for three sections: a Lec section, and a Dis section, and a Lab section. LEC (A1) Tues/Thurs 12:30 pm – 1:45 pm; and DISC (B1) Mon 9:05 am– 9:55 am OR (B2) Mon 10:10 am – 11:00 am; and LAB (C1) Fri 11:15 am – 12:05 pm OR (C2) Fri 12:20 pm – 1:10 pm

Covers topics in linear models beyond MA 575: generalized linear models, analysis of binary and polytomous data, log-linear models, multivariate response models, non-linear models, graphical models, and relevant model selection techniques. Additional topics in modern regression as time allows.

Prereq: CAS MA575 or consent of instructor.

Instructor: Mukherjee; Credits: 4; (A1) Tues/Thurs 9:30 am – 10:45 am

Basic probability, conditional probability, independence. Discrete and continuous random variables, mean and variance, functions of random variables, moment generating function. Jointly distributed random variables, conditional distributions, independent random variables. Methods of transformations, law of large numbers, central limit theorem.

Prereq: CAS MA225 OR CAS MA230.

Instructor: Chou; Credits: 4; Students registering for MA 581 must also register for a discussion section A2-A6.  LEC: (A1) Mon/Wed/Fri 10:10 am – 11:00 am; DIS: (A2) Mon 2:30 – 3:20 OR (A3) Mon 3:35 pm – 4:25 OR (A4) Mon 4:40 pm – 5:30 pm OR (A5) Tues 11:15 am – 12:05 pm OR (A6) Tues 3:35 pm – 4:25 pm

Point estimation including unbiasedness, efficiency, consistency, sufficiency, minimum variance unbiased estimator, Rao-Blackwell theorem, and Rao-Cramer inequality. Maximum likelihood and method of moment estimations; interval estimation; tests of hypothesis, uniformly most powerful tests, uniformly most powerful unbiased tests, likelihood ratio test, and chi-square test.

Prereq: CAS MA581

Instructor: Weiner; Credits: 4; Students registering for CAS MA582 A1 must register for a discussion section A2-A5.  LEC: (A1) Mon/Wed/Fri 11:15 am – 12:05 pm; DIS: (A2) Tues 3:35 pm – 4:25 OR (A3) Tues 5:00 pm – 5:50 pm OR (A4) Mon 12:20 pm – 1:10 pm OR (A5) Mon 1:25 am – 2:15 pm

Basic concepts and techniques of stochastic process as they are most often used to construct models for a variety of problems of practical interest. Topics include Markov chains, Poisson process, birth and death processes, queuing theory, renewal processes, and reliability.

Prereq: CAS MA581 or consent of instructor.

Instructor: Salins; Credits: 4; Students registering for MA583 A1 must also register for a discussion section A2-A5.  LEC: (A1) Mon/Wed/Fri 12:20 pm – 1:10 pm; DIS: (A2) Wed 1:25 – 2:15 pm OR (A3) Wed 2:30 pm – 3:20 pm OR (A4) Wed 3:35 am – 4:25 pm OR (A5) Wed 4:40 pm – 5:30 pm

Application of multivariate data analytic techniques. Multiple regression and correlation, confounding and interaction, variable selection, categorical predictors and outcomes, logistic regression, factor analysis, MANOVA, discriminant analysis, regression with longitudinal data, repeated measures, ANOVA.

Prereq: one year of statistics.

Instructor: Gangopadhyay; Credits: 4; Students registering for CAS MA684 must register for two sections: a Lec section, and a Dis section. LEC (A1): Tues/Thurs 11:00 am – 12:15 pm; and DISC (A2) Mon 1:25 – 2:15 OR (A3) Mon 2:30 – 3:20 OR (A4) Mon 3:35 – 4:25 OR (A5) Mon 4:40 – 5:30

Markov chains, Chapman-Kolmogorov equation. Classification of states, limiting probabilities, Poisson process and its generalization, continuous-time Markov chains, queuing theory, reliability theory.

Prereq: ENG EC 381 OR ENG EK 500

Instructor: Levitin; Credits: 4; Mon/Weds 2:30 – 4:15

This course will serve as a foundation for understanding the heritable basis of numerous biological traits, the relationships among genes, and the regulation of their expression. Focus on the ability to use genetic systems to probe these problems, and therefore will heavily explore the experimental aspects of these investigations. Includes discussion of the impact of the genome sequences’ availability on the practice of modern science. Use of case study approach to investigate the rich variety of scientific insights gained through genetic studies of aging, addiction, obesity, and others.

Prereq: Consent of instructor

Instructor: Dasgupta; Credits: 4; Mon/Weds 1:00 – 3:00

Lectures and interactive auto-tutorial case studies presenting the basic morphologic and functional changes associated with cell injury and death, inflammation, response to microorganisms, atherosclerosis, cancer, and organ system pathology.

Prereq: GMS FC701, FC702, FC703 or GMS BI751 & PH730

Instructor: Slayter; Credits: 4; Tues/Thurs 1:00 – 2:20

Introduction to techniques of molecular biology research, including analysis of DNA, RNA, and protein molecules by techniques such as restriction enzyme digestions, PCR, subcloning, DNA sequencing and analysis, reporter gene assays, protein-protein interactions, and culturing and yeast molecular biology.

Instructor: Gilmore; Credits: 4; Tues/Thurs 1:00 – 5:00