Advanced Topics in Biomedical Informatics

BMIM-GA 0001 - 2 Credits
Monday 4:00-5:30PM and Thursday 4:00-5:30PM 

Zoom:

• https://nyu.zoom.us/j/97858888063

Presenter and Discussion assignments

Course Directors: 

• David Fenyo (david@fenyolab.org)

• Paawan Punjabi (paawan.punjabi@nyulangone.org)

• Kelly Ruggles (kelly.ruggles@nyumc.org)

Learning Objectives:

• To develop a general understanding of the techniques used in biomedical informatics and computational biology research.

• Gain a sense of the current state of the field.

• Develop and improve presentation skills and dissection of scientific literature. 

• Increase your understanding of study design and available experimental methods and tools. 

This course serves to review many of the key methodologies used in biomedical informatics. During this course we will cover a range of topics including systems biology, multi-omics analysis, medical imaging, artificial intelligence and natural language processing. We will spend approximately 15 minutes at the start of  class discussing methodologies and general concepts. The last portion of the class  will be spent with student-led presentations of journal articles assigned for that week.  Specific students will be assigned for that session but all students are expected to thoroughly review the papers and research background questions prior to class.  Discussions are meant to foster conversation and critical thinking in the context of biology and differences in background knowledge will be taken into account in grading.

Grade Distribution

Grades will consist of 50% of class participation and 50% from assigned paper presentations.

Course Schedule

Week 1: Introduction and Histopathology

Thursday 7/6: Histopathology

• Lecturer: Dr. David Fenyo

• Discussion reading: 

• Coudray, Nicolas, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning.  (2018). Nature medicine 

• Fu, Yu, et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. (2020) Nature Cancer 

Week 2: Imaging

Monday 7/10: Imaging in Medicine I

• Lecturer: Dr. Lavanya Umapathy

• Discussion reading: 

• Zhu et al.: Image reconstruction by domain-transform manifold learning, (2018) Nature. 

• Chaitanya et al.: Contrastive learning of global and local features for medical image segmentation with limited annotations, (2020) NIPS.

Thursday 7/13: Imaging in Biomedicine II

• ​​Lecturer: TBD

• Discussion reading: 

• Mederios et al., Detection of Progressive Glaucomatous Optic Nerve Damage on Fundus Photographs with Deep Learning. (2020) American Academy of Opthalmology.

• Mederios et al., An OCT-Trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographs. (2018) American Academy of Opthalmology.

Week 3: Clinical Decision Support

Monday 7/17: Predictive Analytics in Medicine

• Lecturer: Dr. Yindalon Aphinyanaphongs

• Discussion readings:

• DECIDE-AI: new reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence (2021) Nature Medicine

• Liu et al., Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension (2020) Nature Medicine

Thursday 7/20: AI in Clinical Medicine

• Lecturer: Dr. Paawan Punjabi 

• Background reading: Haug et al., Artificial Intelligence and Machine Learning in Clinical Medicine, 2023 (2023) NEJM

• Discussion readings:

• Lee et al., Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine (2023) NEJM

• Jiang et al., Health system-scale language models are all-purpose prediction engines (2023) Nature

Week 4: Natural Language Processing

Monday 7/24: Overview of Natural Language Processing

• Lecturer: Stephen Johnson

• Discussion readings:

• Otter DW, Medina JR, Kalita JK. A Survey of the Usages of Deep Learning for Natural Language Processing. IEEE Trans Neural Netw Learn Syst. 2021 Feb;32(2):604-624. doi: 10.1109/TNNLS.2020.2979670. Epub 2021 Feb 4. PMID: 32324570.

• Kalyan KS, Rajasekharan A, Sangeetha S. AMMU: A survey of transformer-based biomedical pretrained language models. J Biomed Inform. 2022 Feb;126:103982. doi: 10.1016/j.jbi.2021.103982. Epub 2021 Dec 31. PMID: 34974190.

Thursday 7/27: Clinical Natural Language Processing

• Lecturer: Dr. Stephen Johnson

• Discussion readings:

• Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, Turner K. Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review. Comput Biol Med. 2023 Mar;155:106649. doi: 10.1016/j.compbiomed.2023.106649. Epub 2023 Feb 10. PMID: 36805219.

• Digan W, Névéol A, Neuraz A, Wack M, Baudoin D, Burgun A, Rance B. Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites. J Am Med Inform Assoc. 2021 Mar 1;28(3):504-515. doi: 10.1093/jamia/ocaa261. PMID: 33319904; PMCID: PMC7936396.

Week 5: Precision Medicine

Monday 7/31: Single Cell RNA-Seq

• Lecturer: Dr. Bettina Nadorp

• Background reading: 

• Nath A, Bild AH. Leveraging Single-Cell Approaches in Cancer Precision Medicine. Trends Cancer. 2021;7(4):359-372. doi:10.1016/j.trecan.2021.01.007

• Heumos, L., Schaar, A.C., Lance, C. et al. Best practices for single-cell analysis across modalities. Nat Rev Genet (2023).

• Discussion reading

• Kamimoto K, Stringa B, Hoffmann CM, Jindal K, Solnica-Krezel L, Morris SA. Dissecting cell identity via network inference and in silico gene perturbation. Nature. 2023;614(7949):742-751. doi:10.1038/s41586-022-05688-9

Thursday 8/3: Digital Precision Medicine

• Lecturer: Dr. Souptik Barua

• Background reading:

• Mortazavi, Bobak J., and Ricardo Gutierrez-Osuna. "A review of digital innovations for diet monitoring and precision nutrition." Journal of diabetes science and technology 17.1 (2023): 217-223. https://journals.sagepub.com/doi/10.1177/19322968211041356 

• Discussion reading

• Zeevi, David, et al. "Personalized nutrition by prediction of glycemic responses." Cell 163.5 (2015): 1079-1094. https://www.cell.com/fulltext/S0092-8674(15)01481-6 

• Barua, Souptik, et al. "Discordance between postprandial plasma glucose measurement and continuous glucose monitoring." The American Journal of Clinical Nutrition 116.4 (2022):1059-1069.https://www.sciencedirect.com/science/article/abs/pii/S0002916523036249?via%3Dihub 

Week 6: Ethics

Monday 8/7: Data Ethics in Biomedicine

• Lecturer: Nicole Contaxis

• Discussion reading

• Mittelstadt BD, Floridi L. The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts. Sci Eng Ethics. 2016 Apr;22(2):303-41. doi: 10.1007/s11948-015-9652-2. Epub 2015 May 23. PMID: 26002496.

• Price, W. Nicholson, and I. Glenn Cohen. “Privacy in the Age of Medical Big Data.” Nature Medicine 25, no. 1 (January 2019): 37–43. https://doi.org/10.1038/s41591-018-0272-7.

• Case Study: Project Nightingale

• Copeland, R., & Needleman, S. E. (2019, Nov 13). Google's health deal spurs inquiry into privacy of data. Wall Street Journal

Thursday 8/10: Ethical Issues in Data-centric Research 

• Lecturer: Dr. Carolyn Chapman

• Discussion reading

• Faden, Ruth R., Nancy E. Kass, Steven N. Goodman, Peter Pronovost, Sean Tunis, and Tom L. Beauchamp. "An ethics framework for a learning health care system: a departure from traditional research ethics and clinical ethics." Hastings Center Report 43, no. s1 (2013): S16-S27.

• The White House. Blueprint for AI Bill of Rights. Available at https://www.whitehouse.gov/ostp/ai-bill-of-rights/

• Vollmer, Sebastian, Bilal A. Mateen, Gergo Bohner, Franz J. Király, Rayid Ghani, Pall Jonsson, Sarah Cumbers et al. "Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness." bmj 368 (2020).

Week 7: Integrative Omics

Monday 8/14: Precision Medicine and Omics

• Lecturer: Dr. Kelly Ruggles

• Background reading: 

• Karczewski & Snyder. Integrative Omics for Health and Disease. (2018) Nature Reviews Genetics 

• Discussion reading: 

Ahadi et al.,Personal aging markers and ageotypes revealed by deep longitudinal profiling. (2020) Nature Medicine