Predicting multicellular function through multi-layer tissue networks

By Marinka Zitnik, Jure Leskovec. 2017

593 citations recorded by the source corpus.

Motivation: Understanding 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. Results: Here, 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

Subfields: Biophysics, Molecular Biology, Computational Biology & Bioinformatics

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Citations in the corpus

Graph Attention Networks (2017, methodology): “…on 1000 test nodes, and we use 500 additional nodes for validation purposes (the same ones as used by Kipf & Welling (2017 Inductive learning We make use of a protein-protein interaction (PPI) dataset that consists of graphs corresponding to different human tissues (Zitnik & Leskovec, 2017).”
Inductive Representation Learning on Large Graphs (2017, neutral): “We classify protein roles—in terms of their cellular functions from gene ontology—in various protein-protein interaction (PPI) graphs, with each graph corresponding to a different human tissue [41].”
Auto-GNN: Neural architecture search of graph neural networks (2019, methodology): “Graph neural networks (GNN) [1, 2] has been demonstrated that it could achieve superior performance in modeling graph-structured data, within various domains such as social media [3–6] and bioinformatics [7, 8].”
Position-aware Graph Neural Networks (2019, background): “…et al., 2018b), with successful applications across domains such as social and information networks (Ying et al., 2018a), chemistry (You et al., 2018a), and biology (Zitnik & Leskovec, 2017). matrix-factorization approaches (Belkin & Niyogi, 2002), and random-walk approaches (Perozzi et al., 2014).”
Deep Graph Infomax (2018, methodology): “We make use of a protein-protein interaction (PPI) dataset that consists of graphs corresponding to different human tissues (Zitnik & Leskovec, 2017).”
Graph Normalizing Flows (2019, background): “Inductive Learning tasks consist of PPI (Protein-Protein Interaction Dataset) [31] and QM9 Molecule property prediction dataset [21].”
A Comprehensive Survey on Graph Neural Networks (2019, neutral): “…[43], [44], [45] [49], [51], [53], [55], [56], [61], [62] [70], [95] DBLP (v11) [118] 1 4107340 36624464 - - [64], [70], [99] Bio-chemicalGraphs PPI [119] 24 56944 818716 50 121 [18], [42], [43], [48], [45], [50], [55] [56], [58], [22], [41], [50] words (known as Abstract Meaning Representation).”
Distilling Knowledge From Graph Convolutional Networks (2020, methodology): “Speciﬁcally, for the PPI dataset, GAT [35] model is adopted that takes ﬁxed graph as input.”
How to Find Your Friendly Neighborhood: Graph Attention Design with Self-Supervision (2022, background): “The protein-protein interaction (PPI) dataset (Zitnik & Leskovec, 2017; Hamilton et al., 2017; Subramanian et al., 2005) is a well-known benchmark in the inductive setting.”
Overcoming Catastrophic Forgetting in Graph Neural Networks (2020, methodology): “For inductive learning, we use two datasets: a protein-protein interaction (PPI) dataset (Zitnik and Leskovec 2017) and a large graph dataset of Reddit posts (Hamilton, Ying, and Leskovec 2017).”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “For example, [23] use a neural network to predict tissue-specific protein functions, i.”
Everything is Connected: Graph Neural Networks (2023, methodology): “using gene ontology data (Zitnik and Leskovec, 2017)) in a given protein-protein interaction network, as first done by GraphSAGE (Hamilton et al.”
Graph embedding on biomedical networks: methods, applications and evaluations (2019, unknown): “(Cao et al., 2015) ✗✗ ✗✗ ✗ Random walk-based DeepWalk (Perozzi et al., 2014) ✗✗ ✗✗Cho et al. (2016) and Kulmanov et al. (2018) node2vec (Grover and Leskovec, 2016) ✗✗ ✗✗Grover and Leskovec (2016) and Zitnik and Leskovec (2017) struc2vec (Ribeiro et al., 2017) ✗✗ ✗✗ ✗ Neural network-based LINE (Tang ”
Bootstrapped Representation Learning on Graphs (2021, background): “PPI is a protein-protein interaction network (Zitnik & Leskovec, 2017; Hamilton et al., 2017), comprised of multiple ( 24 ) graphs each corresponding to different human tissues.”