Vineet Raghu

Lecturer

Title Lecturer
Email vraghu@bu.edu
Education BS and PhD in Computer Science, University of Pittsburgh

Vineet is a part-time lecturer at Boston University Metropolitan College and a postdoctoral research fellow at Massachusetts General Hospital and Harvard Medical School. He teaches courses in data analysis, data mining , and machine learning. His research focuses on the application of machine learning to medical imaging and genomics. His current projects aim to predict long-term risk of cardiovascular disease and cancer from chest radiograph images, and he is especially interested in how researchers can use deep learning models to identify early biomarkers of future disease.

Courses

Research Interests

Scholarly Works

Journal Papers

Raghu, VK, Ge, X, Balajee, A, Shirer, DJ, Das, I, Benos, PV, and Chrysanthis, PK. A pipeline for integrated theory- and data-driven modeling of genomic and clinical data. ACM Transactions in Computational Biology and Bioinformatics. 2020 Aug 25. Epub ahead of print.
Lu, MT*, Raghu, VK*, Mayrhofer, T, Aerts, HJWL, and Hoffmann, U. Deep learning using chest radiographs to identify high-risk smokers for lung cancer screening CT: Development and validation of a prediction model. Annals of Internal Medicine. 2020 Sep 1;173(9): 704-713.
Raghu, VK*, Weiss, J*, Hoffmann, U, Aerts, HJWL, and Lu, MT. Deep learning to estimate biological age from chest radiographs. JACC: Cardiovascular Imaging. 2021 Mar 10. Epub ahead of print.
Raghu, VK, Horvat, CM, Kochanek, PM, Fink, EL, Clark, RSB, Benos, PV, and Au, AK. Neurological complications acquired during pediatric critical illness: Exploratory mixed graphical modeling analysis using serum biomarker levels. Pediatric Critical Care Medicine. 2021 Apr 5. Epub ahead of print.
Ge, X*, Raghu, VK*, Chrysanthis, PK, and Benos, PV (2020). CausalMGM: A webserver for integrative causal analysis of multi-modal biomedical data. Nucleic Acids Research. Epub ahead of print.
Raghu, VK, Wang, X, Pu, J, Leader, JK, Wang, R, Herman, J, Diergaarde, B, Yuan, J, Benos, PV, and Wilson, DO (2019). Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models. Thorax. 74(7), pp. 643-649.
Manatakis, DV*, Raghu, VK*, and Benos, PV (2018). piMGM: Incorporating multi-source priors in mixed graphical models for learning disease networks. Bioinformatics Special Issue for European Conference on Computational Biology (ECCB 2018). 34(17). pp. 848-856
Raghu, VK, Beckwitt, C, Warita, K, Wells, A, Benos, PV, and Oltvai, ZN. (2018). Biomarker Identification for statin sensitivity of cancer cell lines. Biochemical and Biophysical Research Communications. 495(1), pp. 659-665.
Raghu, VK, Ramsey, JD, Morris, A, Manatakis, DV, Spirtes, P, Chrysanthis, PK, Glymour, C, and Benos, PV (2018). Comparison of Strategies for Scalable Causal Discovery of Latent Variable Models from Mixed Data. Springer International Journal of Data Science and Analytics. pp. 1-13

Conference and Workshop Papers

Raghu, VK, Ge, X, Balajee, A, Shirer, DJ, Das, I, Benos, PV, and Chrysanthis, PK (2019). A pipeline for integrated theory and data-driven modeling of genomic and clinical data. BioKDD 2019 (Held in Conj. With KDD 2019).
Raghu, VK, Poon, A, and Benos, PV (2018). Evaluation of causal structure learning methods on mixed data types. In Proceedings of Machine Learning Research for Causal Discovery 2018 (Held in Conj. With KDD 2018).
Raghu, VK, Ge, X, Chrysanthis, PK, and Benos, PV (2017). Integrated Theory and Data-Driven Feature Selection for Gene Expression Data Analysis. Proc. Of 2nd International Workshop on Health Data Management and Mining (HDMM 2017 held in conjunction with ICDE 2017). pp. 1525-1532.

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