ArticleBioinformatics (Oxford, England)2017
Predicting multicellular function through multi-layer tissue networks.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 79 papers, 1 of them a synthesis that pooled it.
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Who cites it
79 citing papers in PubMed, 1 synthesis or guideline pooled it, 529 citations in OpenAlex.
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- Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.PloS one · 2025Article
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- CrossIsoFun: predicting isoform functions using the integration of multi-omics data.Bioinformatics (Oxford, England) · 2024Article
- MARML: Motif-Aware Deep Representation Learning in Multilayer Networks.IEEE transactions on neural networks and learning systems · 2024Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- Contextual AI models for single-cell protein biology.Nature methods · 2024Article
- Contextual AI models for single-cell protein biology.bioRxiv : the preprint server for biology · 2024Article
19 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationUnderstanding functions of proteins in specific human tissues is essential for insights into disease diagnostics and therapeutics, yet prediction of tissue-specific cellular function remains a critical challenge for biomedicine.
resultsHere, we present OhmNet , a hierarchy-aware unsupervised node feature learning approach for multi-layer networks. We build a multi-layer network, where each layer represents molecular interactions in a different human tissue. OhmNet then automatically learns a mapping of proteins, represented as nodes, to a neural embedding-based low-dimensional space of features. OhmNet encourages sharing of similar features among proteins with similar network neighborhoods and among proteins activated in similar tissues. The algorithm generalizes prior work, which generally ignores relationships between tissues, by modeling tissue organization with a rich multiscale tissue hierarchy. We use OhmNet to study multicellular function in a multi-layer protein interaction network of 107 human tissues. In 48 tissues with known tissue-specific cellular functions, OhmNet provides more accurate predictions of cellular function than alternative approaches, and also generates more accurate hypotheses about tissue-specific protein actions. We show that taking into account the tissue hierarchy leads to improved predictive power. Remarkably, we also demonstrate that it is possible to leverage the tissue hierarchy in order to effectively transfer cellular functions to a functionally uncharacterized tissue. Overall, OhmNet moves from flat networks to multiscale models able to predict a range of phenotypes spanning cellular subsystems. AVAILABILITY AND IMPLEMENTATION: Source code and datasets are available at http://snap.stanford.edu/ohmnet . CONTACT: jure@cs.stanford.edu.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.