Graph representation learning in biomedicine and healthcare

MM Li, K Huang, M Zitnik - Nature Biomedical Engineering, 2022 - nature.com
Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Graph representation learning in biomedicine

MM Li, K Huang, M Zitnik - arxiv preprint arxiv:2104.04883, 2021 - arxiv.org
Biomedical networks (or graphs) are universal descriptors for systems of interacting
elements, from molecular interactions and disease co-morbidity to healthcare systems and …

Current and future directions in network biology

M Zitnik, MM Li, A Wells, K Glass, DM Gysi… - arxiv preprint arxiv …, 2023 - arxiv.org
Network biology, an interdisciplinary field at the intersection of computational and biological
sciences, is critical for deepening understanding of cellular functioning and disease. While …

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

A Venkat, SE Youlten, BPS Juan, C Purcell, M Amodio… - bioRxiv, 2024 - biorxiv.org
Identifying functionally important cell states and structure within a heterogeneous tumor
remains a significant biological and computational challenge. Moreover, current clustering …

[PDF][PDF] Machine learning and large scale cancer omic data: decoding the biological mechanisms underpinning cancer

V Fanfani - 2022 - core.ac.uk
Many of the mechanisms underpinning cancer risk and tumorigenesis are still not fully
understood. However, the next-generation sequencing revolution and the rapid advances in …