no code implementations • 20 Oct 2023 • Ruth Johnson, Michelle M. Li, Ayush Noori, Owen Queen, Marinka Zitnik
With diverse data -- from patient records to imaging -- GNNs process data holistically by viewing modalities as nodes interconnected by their relationships.
no code implementations • 15 Sep 2023 • Marinka Zitnik, Michelle M. Li, Aydin Wells, Kimberly Glass, Deisy Morselli Gysi, Arjun Krishnan, T. M. Murali, Predrag Radivojac, Sushmita Roy, Anaïs Baudot, Serdar Bozdag, Danny Z. Chen, Lenore Cowen, Kapil Devkota, Anthony Gitter, Sara Gosline, Pengfei Gu, Pietro H. Guzzi, Heng Huang, Meng Jiang, Ziynet Nesibe Kesimoglu, Mehmet Koyuturk, Jian Ma, Alexander R. Pico, Nataša Pržulj, Teresa M. Przytycka, Benjamin J. Raphael, Anna Ritz, Roded Sharan, Yang shen, Mona Singh, Donna K. Slonim, Hanghang Tong, Xinan Holly Yang, Byung-Jun Yoon, Haiyuan Yu, Tijana Milenković
As such, it is expected to help shape short- and long-term vision for future computational and algorithmic research in network biology.
1 code implementation • 20 Jun 2021 • Anna L. Trella, Peniel N. Argaw, Michelle M. Li, James A. Hay
We evaluate two data-generating models within this Bayesian inference framework: a simple exponential growth model and a highly flexible Gaussian process prior model.
no code implementations • 4 Jun 2021 • Michelle M. Li, Marinka Zitnik
We construct a multi-scale network of the Human Cell Atlas and apply AWARE to learn protein, cell type, and tissue embeddings that uphold cell type and tissue hierarchies.
no code implementations • 11 Apr 2021 • Michelle M. Li, Kexin Huang, Marinka Zitnik
Biomedical networks (or graphs) are universal descriptors for systems of interacting elements, from molecular interactions and disease co-morbidity to healthcare systems and scientific knowledge.
1 code implementation • NeurIPS 2020 • Emily Alsentzer, Samuel G. Finlayson, Michelle M. Li, Marinka Zitnik
Deep learning methods for graphs achieve remarkable performance on many node-level and graph-level prediction tasks.