Disease state prediction from single-cell data using graph attention networks. - Neal G. Ravindra; Arijit Sehanobish; Jenna L. Pappalardo; David A. Hafler; David van Dijk (CHIL, 2020)

Self-supervised edge features for improved Graph Neural Network training. - Arijit Sehanobish; Neal G. Ravindra; David van Dijk (CoRR, 2020)

Permutation invariant networks to learn Wasserstein metrics. - Arijit Sehanobish; Neal G. Ravindra; David van Dijk (CoRR, 2020)

Gaining Insight into SARS-CoV-2 Infection and COVID-19 Severity Using Self-supervised Edge Features and Graph Neural Networks. - Arijit Sehanobish; Neal G. Ravindra; David van Dijk (AAAI, 2021)

Large-scale correlation network construction for unraveling the coordination of complex biological systems. - Martin Becker 003; 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; et al. (Nat. Comput. Sci., 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 003; Alan L. Chang; Sayane Shome; Ivana Maric; Davide De Francesco; Samson Mataraso; Geetha Saarunya; et al. (npj Digit. Medicine, 2023)