ArticleBioinformatics (Oxford, England)2015
Tissue-aware data integration approach for the inference of pathway interactions in metazoan organisms.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- FNDC1 is a myokine that promotes myogenesis and muscle regeneration.The EMBO journal · 2025Article
- High-throughput identification of repurposable neuroactive drugs with potent anti-glioblastoma activity.Nature medicine · 2024Article
- Differential Host Gene Expression in Response to Infection by DifferentMicroorganisms · 2024Article
- Multimodal single-cell analyses reveal mechanisms of perianal fistula in diverse patients with Crohn's disease.Med (New York, N.Y.) · 2024Article
- O-GlcNAcylation of MITF regulates its activity and CDK4/6 inhibitor resistance in breast cancer.Nature communications · 2024Article
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- MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.Nature communications · 2023Article
- MEMMAL: A tool for expanding large-scale mechanistic models with machine learned associations and big datasets.Frontiers in systems biology · 2023Article
- And-1 Coordinates with the FANCM Complex to Regulate Fanconi Anemia Signaling and Cisplatin Resistance.Cancer research · 2022Article
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- MYH10 Governs Adipocyte Function and Adipogenesis through Its Interaction with GLUT4.International journal of molecular sciences · 2022Article
- Oncogenic signaling inhibits c-FLIPScientific reports · 2021Article
- Machine learning identifies molecular regulators and therapeutics for targeting SARS-CoV2-induced cytokine release.Molecular systems biology · 2021Article
- The Capacity of the Ovarian Cancer Tumor Microenvironment to Integrate Inflammation Signaling Conveys a Shorter Disease-free Interval.Clinical cancer research : an official journal of the American Association for Cancer Research · 2020Article
- L-HetNetAligner: A novel algorithm for Local Alignment of Heterogeneous Biological Networks.Scientific reports · 2020Article
- Article
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Authors and funding
6 authors.
Funding
Abstract
motivationLeveraging the large compendium of genomic data to predict biomedical pathways and specific mechanisms of protein interactions genome-wide in metazoan organisms has been challenging. In contrast to unicellular organisms, biological and technical variation originating from diverse tissues and cell-lineages is often the largest source of variation in metazoan data compendia. Therefore, a new computational strategy accounting for the tissue heterogeneity in the functional genomic data is needed to accurately translate the vast amount of human genomic data into specific interaction-level hypotheses.
resultsWe developed an integrated, scalable strategy for inferring multiple human gene interaction types that takes advantage of data from diverse tissue and cell-lineage origins. Our approach specifically predicts both the presence of a functional association and also the most likely interaction type among human genes or its protein products on a whole-genome scale. We demonstrate that directly incorporating tissue contextual information improves the accuracy of our predictions, and further, that such genome-wide results can be used to significantly refine regulatory interactions from primary experimental datasets (e.g. ChIP-Seq, mass spectrometry). AVAILABILITY AND IMPLEMENTATION: An interactive website hosting all of our interaction predictions is publically available at http://pathwaynet.princeton.edu. Software was implemented using the open-source Sleipnir library, which is available for download at https://bitbucket.org/libsleipnir/libsleipnir.bitbucket.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.