ArticleNucleic acids research2019
Functional protein representations from biological networks enable diverse cross-species inference.
Article in Nucleic acids research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- SPACE: STRING proteins as complementary embeddings.Bioinformatics (Oxford, England) · 2025Article
- Deciphering and steering population-level response under spatial drug heterogeneity on microhabitat structures.bioRxiv : the preprint server for biology · 2025Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- Exact p-values for global network alignments via combinatorial analysis of shared GO terms : REFANGO: Rigorous Evaluation of Functional Alignments of Networks using Gene Ontology.Journal of mathematical biology · 2024Article
- Phenotypic plasticity for improved light harvesting, in tandem with methylome repatterning in reef-building corals.Molecular ecology · 2024Article
- Joint representation of molecular networks from multiple species improves gene classification.PLoS computational biology · 2024Article
- Joint embedding of biological networks for cross-species functional alignment.Bioinformatics (Oxford, England) · 2023Article
- Article
- Supervised biological network alignment with graph neural networks.Bioinformatics (Oxford, England) · 2023Article
- Use ofHeliyon · 2023Article
- An Extensive Assessment of Network Embedding in PPI Network Alignment.Entropy (Basel, Switzerland) · 2022Article
- MUNDO: protein function prediction embedded in a multispecies world.Bioinformatics advances · 2022Article
- NetQuilt: deep multispecies network-based protein function prediction using homology-informed network similarity.Bioinformatics (Oxford, England) · 2021Article
- Juxtapose: a gene-embedding approach for comparing co-expression networks.BMC bioinformatics · 2021Article
- Data-driven biological network alignment that uses topological, sequence, and functional information.BMC bioinformatics · 2021Article
- Advances in synthetic lethality for cancer therapy: cellular mechanism and clinical translation.Journal of hematology & oncology · 2020Review
- Data-driven network alignment.PloS one · 2020Article
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Authors and funding
8 authors.
Funding
Abstract
Transferring knowledge between species is key for many biological applications, but is complicated by divergent and convergent evolution. Many current approaches for this problem leverage sequence and interaction network data to transfer knowledge across species, exemplified by network alignment methods. While these techniques do well, they are limited in scope, creating metrics to address one specific problem or task. We take a different approach by creating an environment where multiple knowledge transfer tasks can be performed using the same protein representations. Specifically, our kernel-based method, MUNK, integrates sequence and network structure to create functional protein representations, embedding proteins from different species in the same vector space. First we show proteins in different species that are close in MUNK-space are functionally similar. Next, we use these representations to share knowledge of synthetic lethal interactions between species. Importantly, we find that the results using MUNK-representations are at least as accurate as existing algorithms for these tasks. Finally, we generalize the notion of a phenolog ('orthologous phenotype') to use functionally similar proteins (i.e. those with similar representations). We demonstrate the utility of this broadened notion by using it to identify known phenologs and novel non-obvious ones supported by current research.
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