Agrawal S, Klarqvist MDR, Emdin C, et al. Selection of 51 predictors from 13,782 candidate multimodal features using machine learning improves coronary artery disease prediction. Patterns (N Y). 2021;2(12):100364. doi:10.1016/j.patter.2021.100364
Camacho DM, Collins KM, Powers RK, Costello JC, Collins JJ. Next-Generation Machine Learning for Biological Networks. Cell. 2018;173(7):1581-1592. doi:10.1016/j.cell.2018.05.015
Ott PA, Hu Z, Keskin DB, et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature. 2017;547(7662):217-221. doi:10.1038/nature22991
Abadi S, Yan WX, Amar D, Mayrose I. A machine learning approach for predicting CRISPR-Cas9 cleavage efficiencies and patterns underlying its mechanism of action. PLoS Comput Biol. 2017;13(10):e1005807. doi:10.1371/journal.pcbi.1005807
Li T, Kim A, Rosenbluh J, et al. GeNets: a unified web platform for network-based genomic analyses. Nat Methods. 2018;15(7):543-546. doi:10.1038/s41592-018-0039-6
Steinhoff G, Nesteruk J, Wolfien M, et al. Cardiac Function Improvement and Bone Marrow Response -: Outcome Analysis of the Randomized PERFECT Phase III Clinical Trial of Intramyocardial CD133 Application After Myocardial Infarction. EBioMedicine. 2017;22:208-224. doi:10.1016/j.ebiom.2017.07.022
Bohnenberger H, Kaderali L, Ströbel P, et al. Comparative proteomics reveals a diagnostic signature for pulmonary head-and-neck cancer metastasis. EMBO Mol Med. 2018;10(9). doi:10.15252/emmm.201708428
Simm J, Klambauer G, Arany A, et al. Repurposing High-Throughput Image Assays Enables Biological Activity Prediction for Drug Discovery. Cell Chem Biol. 2018;25(5):611-618.e3. doi:10.1016/j.chembiol.2018.01.015
Bray MA, Carpenter AE. Quality Control for High-Throughput Imaging Experiments Using Machine Learning in Cellprofiler. Methods Mol Biol. 2018;1683:89-112. doi:10.1007/978-1-4939-7357-6_7
Eulenberg P, Köhler N, Blasi T, et al. Reconstructing cell cycle and disease progression using deep learning. Nat Commun. 2017;8(1):463. doi:10.1038/s41467-017-00623-3