"Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications" Sorlie et al, Proc Natl Acad Sci U S A. 2001; 98:10869-74
"Molecular Portraits of human breast tumors" Perou et al, Nature 2000, 406: 747-752
"Gene expression profiling in breast cancer: classification, prognostication, and prediction" Reis-Filho and Pusztai, Lancet 2011; 378:1812-23.
“Preparing for precision medicine”, Mirnezami, R, et al., N Engl J Med 2012, 366 (6), 489-91
“Personalized medicine: been there, done that, always needs work!”, Murray, Am J Respir Crit Care Med 2012, 185 (12), 1251-2
“Toward precision medicine and health: Opportunities and challenges in allergic diseases”, Galli, J Allergy Clin Immunol 2016, 137 (5), 1289-300
"Personalized Medicine at FDA: The Scope & Significance of Progress in 2021", Personalized Medicine Coalition
"From X-ray crystallography to AlphaFold to Generative AI: A ReAIssance of Empiricism and Connectionism in Biological Science"
Eric Xing,
President of the Mohamed bin Zayed University of Artificial Intelligence,
Professor of Computer Science at Carnegie Mellon University
U.S. Health Care from a Global Perspective, 2019: Higher Spending, Worse Outcomes?
“Comparing Health Outcomes of Privileged US Citizens With Those of Average Residents of Other Developed Countries”, Emanuel et al, JAMA Intern Med. 2021, 181 (3), 339–344
“Predicting the Future - Big Data, Machine Learning, and Clinical Medicine”, Obermeyer et al, N Engl J Med 2016, 375 (13), 1216-9
Slides:
Required Reading:
“Effect of Renin-Angiotensin-Aldosterone System Inhibitors in Patients with COVID-19: a Systematic Review and Meta-analysis of 28,872 Patients”, Baral et al, Curr. Atherosclerosis Reports (2020) 22:61
Evidence-based practice for individuals or groups: let’s make a difference, de Groot et al Perspect Med Educ (2013) 2:216–221
Osteoporosis therapy: an example of putting evidence-based medicine into clinical practice, Hosking, Geusens, Rizzoli; QJM 98(6), pp. 403–413,
"Why Most Published Research Findings Are False", Ioannidis, 2005, DOI: 10.1371/journal.pmed.0020124
“Healthcare outcomes assessed with observational study designs compared with those assessed in randomized trials”, Anglemyer et al, Cochrane Database Syst Rev 2014, (4), MR000034
"Clinical Versus Mechanical Prediction: A Meta Analysis" Grove et al Psychological Assessment, 2000 12(1) 19-30
A Randomized Trial of Hydroxychloroquine as Postexposure Prophylaxis for Covid-19, Boulware et al, N Engl J Med. 2020 doi: 10.1056/NEJMoa2016638
Slides:
An overview paper: “A guide to machine learning for biologists”, Greener et al, Nat Rev Mol Cell Biol 2022, 23 (1), 40-55
“Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.” Wu et al, J Allergy Clin Immunol 2014, 133(5):1280-8
"Asthma phenotypes: the evolution from clinical to molecular approaches." Wenzel, Nat Med. 2012;18(5):716-25.
Slides:
pdf (Lecture 6: Background - Multiple kernel clustering)
pdf (Lecture 7: Corticosteroid response phenotyping in asthma)
Required Reading:
“Multiview Cluster Analysis Identifies Variable Corticosteroid Response Phenotypes in Severe Asthma”, Wu et al, Am J Respir Crit Care Med 2019, 199 (11), 1358-1367
“Predicting Response to Triamcinolone in Severe Asthma by Machine Learning. Solving the Enigma”, Chung, Am J Respir Crit Care Med 2019, 199 (11), 1299-1300
“The prevention and treatment of missing data in clinical trials”, Little et al, N Engl J Med 2012, 367 (14), 1355-60
Slides:
Required Reading:
"Rubik: Knowledge guided tensor factorization and completion for health data analytics", Wang et al. KDD 2015, pp. 1265–1274
Slides:
Required Reading:
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles, Subramanian et al PNAS, 2005, 102 (43) 15545-15550
Slides:
Required Reading:
Xing, Jordan, Karp, Feature selection for high-dimensional genomic microarray data. In Proceedings of the Eighteenth International Conference on Machine Learning (ICML), 2001.
Optional Reading:
Xing et al, Distance Metric Learning, with application to Clustering with side-information. In Advances in Neural Information Processing Systems 16 (NeurIPS) MIT Press: 2002; pp 521-528.
Slides:
Required Reading:
"A Molecular Signature Predictive of Indolent Prostate Cancer", Irshad et al Sci Transl Med. 2013; 5(202): 202ra122
Slides:
Required Reading:
“Gene expression profiling predicts clinical outcome of breast cancer”, van 't Veer et al, Nature 2002, 415 (6871), 530-6
“A gene-expression signature as a predictor of survival in breast cancer”, van de Vijver et al, N Engl J Med 2002, 347 (25), 1999-2009
“70-Gene Signature as an Aid to Treatment Decisions in Early-Stage Breast Cancer”, Cardoso et al, N Engl J Med 2016, 375 (8), 717-29
Slides:
Required Reading:
"Identification of novel mutations by exome sequencing in African American colorectal cancer patients", Ashktorab et al Cancer 2014, 121(1):34-42
“Hallmarks of cancer: the next generation”, Hanahan et al, Cell 2011, 144 (5), 646-74
“Why don't we get more cancer? A proposed role of the microenvironment in restraining cancer progression”, Bissell et al, Nature Medicine 2011, 17 (3), 320-9
Slides:
Required Reading:
“Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study”, Corey et al, PLoS Med 2018, 15 (11), e1002701
"The elements of statistical learning: data mining, inference, and prediction." 2nd ed. Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). New York, Springer. -- Chapters 4.4 (LR), 10 (boosting), 15 (RF).
Slides:
Required Reading:
“Mobile detection of autism through machine learning on home video: A development and prospective validation study”, Tariq et al, PLoS Med 2018, 15 (11), e1002705
"The elements of statistical learning: data mining, inference, and prediction." 2nd ed. Hastie, T., Tibshirani, R., & Friedman, J. H. (2009). New York, Springer. -- Chapter 12 (SVM).
Slides:
Required Reading:
"Using recurrent neural network models for early detection of heart failure onset", Choi et al J Am Med Inform Assoc. 2017; 24(2): 361–370
"Deep Learning", Goodfellow, Bengio & Courville (2016), MIT Press. -- Chapter 10.2 (RNN), 10.7, 10.10 (LSTM, GRU)
“Video-based AI for beat-to-beat assessment of cardiac function”, Ouyang et al, Nature 2020, 580 (7802), 252-256
“Blinded, randomized trial of sonographer versus AI cardiac function assessment”, He et al, Nature 2023, 616 (7957), 520-524
Module 5: Medical imaging analysis
Slides:
Required Reading:
"Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study", Kathner, et al PLOS Medicine, 2019
“Deep learning”, LeCun, Bengio & Hinton, Nature 2015, 521 (7553), 436-44
"Deep Learning", Goodfellow, Bengio & Courville (2016), MIT Press. -- Chapter 9 (CNN)
Slides:
Required Reading:
“Dermatologist-level classification of skin cancer with deep neural networks”, Esteva et al, Nature 2017, 542 (7639), 115-118
“Adversarial attacks on medical machine learning”, Finlayson et al, Science 2019, 363 (6433), 1287-1289
“Why do deep convolutional networks generalize so poorly to small image transformations?”, Azulay and Weiss, Journal of Machine Learning Research 2019, 20, 1-25
"Dissecting racial bias in an algorithm used to manage the health of populations", Obermeyer et al, Science 2019, 366
“Validation of artificial intelligence prediction models for skin cancer diagnosis using dermoscopy images: the 2019 International Skin Imaging Collaboration Grand Challenge”, Combalia et al, Lancet Digit Health 2022, 4 (5), e330-e339
Slides:
Required Reading:
"Optimization algorithms for functional deimmunization of therapeutic proteins", Parker et al, BMC Bioinformatics 2010, 11, 180
"Mapping the Pareto Optimal Design Space for a Functionally Deimmunized Biotherapeutic Candidate", Salvat et al, PLoS Comput Biol 2015, 11 (1), e1003988
Slides:
Required Reading:
"Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells", Kuenzi et al, Cancer Cell 2020, 38 (5), 672-684.e6
Slides:
pdf (Lecture 22: Background - Attention and transformer-based models; Protein structure prediction)
pdf (Lecture 23: Predicting protein structures using AlphaFold; course summary)
Required Reading:
“Highly accurate protein structure prediction with AlphaFold”, Jumper et al, Nature 2021, 596 (7873), 583-589
"Attention is all you need", Vaswani et al, In Advances in Neural Information Processing Systems, 2017; pp 5998–6008.
“A Holy Grail - The Prediction of Protein Structure”, Altman, N Engl J Med 2023, 389 (15), 1431-1434
“'The entire protein universe': AI predicts shape of nearly every known protein”, Callaway, Nature 2022, 608 (7921), 15-16
“Accurate proteome-wide missense variant effect prediction with AlphaMissense”, Cheng et al, Science 2023, 381 (6664), eadg7492