ArticleBioinformatics (Oxford, England)2024
FAVA: high-quality functional association networks inferred from scRNA-seq and proteomics data.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Unraveling gene interaction networks in colorectal cancer and inflammatory bowel disease via a novel hybrid radial basis function network.Scientific reports · 2026Article
- Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics.Analytical chemistry · 2025Review
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Multi-layered genetic approaches to identify approved drug targets.Cell genomics · 2023Article
- Toward an Integrated Machine Learning Model of a Proteomics Experiment.Journal of proteome research · 2023Review
- The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest.Nucleic acids research · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
motivationProtein networks are commonly used for understanding how proteins interact. However, they are typically biased by data availability, favoring well-studied proteins with more interactions. To uncover functions of understudied proteins, we must use data that are not affected by this literature bias, such as single-cell RNA-seq and proteomics. Due to data sparseness and redundancy, functional association analysis becomes complex.
resultsTo address this, we have developed FAVA (Functional Associations using Variational Autoencoders), which compresses high-dimensional data into a low-dimensional space. FAVA infers networks from high-dimensional omics data with much higher accuracy than existing methods, across a diverse collection of real as well as simulated datasets. FAVA can process large datasets with over 0.5 million conditions and has predicted 4210 interactions between 1039 understudied proteins. Our findings showcase FAVA's capability to offer novel perspectives on protein interactions. FAVA functions within the scverse ecosystem, employing AnnData as its input source. AVAILABILITY AND IMPLEMENTATION: Source code, documentation, and tutorials for FAVA are accessible on GitHub at https://github.com/mikelkou/fava. FAVA can also be installed and used via pip/PyPI <pip install favapy> as well as via the scverse ecosystem https://github.com/scverse/ecosystem-packages/tree/main/packages/favapy.
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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.