Alan L. Chang

According to our database, Alan L. Chang authored at least 4 papers between 2020 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2023

Deep representation learning identifies associations between physical activity and sleep patterns during pregnancy and prematurity.

Neal G. Ravindra, Camilo Espinosa, Eloïse Berson, Thanaphong Phongpreecha, Peinan Zhao, Martin Becker, Alan L. Chang, Sayane Shome, Ivana Maric, Davide De Francesco, Samson Mataraso, Geetha Saarunya, Melan Thuraiappah, Lei Xue, Brice Gaudilliere, Martin S. Angst, Gary M. Shaw, Erik D. Herzog, David K. Stevenson, Sarah K. England, Nima Aghaeepour

npj Digit. Medicine, 2023

Large-scale correlation network construction for unraveling the coordination of complex biological systems.

Martin Becker, Huda Nassar, Camilo Espinosa, Ina A. Stelzer, Dorien Feyaerts, Eloïse Berson, Neda Hajiakhoond Bidoki, Alan L. Chang, Geetha Saarunya, Anthony Culos, Davide De Francesco, Ramin Fallahzadeh, Qun Liu, Yeasul Kim, Ivana Maric, Samson Mataraso, Seyedeh Neelufar Payrovnaziri, Thanaphong Phongpreecha, Neal G. Ravindra, Natalie Stanley, Sayane Shome, Yuqi Tan, Melan Thuraiappah, Maria Xenochristou, Lei Xue, Gary M. Shaw, David K. Stevenson, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour

Nat. Comput. Sci., 2023

2022

Early prediction and longitudinal modeling of preeclampsia from multiomics.

Ivana Maric, Kévin Contrepois, Mira N. Moufarrej, Ina A. Stelzer, Dorien Feyaerts, Xiaoyuan Han, Andy Tang, Natalie Stanley, Ronald J. Wong, Gavin M. Traber, Mathew Ellenberger, Alan L. Chang, Ramin Fallahzadeh, Huda Nassar, Martin Becker, Maria Xenochristou, Camilo Espinosa, Davide De Francesco, Mohammad S. Ghaemi, Elizabeth K. Costello, Anthony Culos, Xuefeng Bruce Ling, Karl G. Sylvester, Gary L. Darmstadt, Virginia D. Winn, Gary M. Shaw, David A. Relman, Stephen R. Quake, Martin S. Angst, Michael P. Snyder, David K. Stevenson, Brice Gaudilliere, Nima Aghaeepour

Patterns, 2022

2020

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

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

Nat. Mach. Intell., 2020