Litcius/Paper detail

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

Anthony Culos, Amy S. Tsai, Natalie Stanley, Martin Becker, Mohammad Sajjad Ghaemi, David R. McIlwain, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Camilo Espinosa, Maria Xenochristou, Edward A. Ganio, Laura S. Peterson, Xiaoyuan Han, Ina A. Stelzer, Kazuo Ando, Dyani Gaudillière, Thanaphong Phongpreecha, Ivana Marić, Alan L. Chang, Gary M. Shaw, David K. Stevenson, Sean C. Bendall, Kara L. Davis, Wendy J. Fantl, Garry P. Nolan, Trevor Hastie, Robert Tibshirani, Martin S. Angst, Brice Gaudillière, Nima Aghaeepour

2020Nature Machine Intelligence84 citationsDOIOpen Access PDF

Topics & Concepts

Mass cytometryComputer scienceOverfittingMachine learningArtificial intelligenceProfiling (computer programming)Immune systemImmunologyMedicineArtificial neural networkBiologyBiochemistryPhenotypeGeneOperating systemSingle-cell and spatial transcriptomicsCell Image Analysis TechniquesImmune cells in cancer
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions | Litcius