ArticleBioinformatics (Oxford, England)2017
Incorporating interaction networks into the determination of functionally related hit genes in genomic experiments with Markov random fields.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- A new Similarity Based Adapted Louvain Algorithm (SIMBA) for active module identification in p-value attributed biological networks.Scientific reports · 2025Article
- A network embedding approach to identify active modules in biological interaction networks.Life science alliance · 2023Article
- NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- Temporal and sequential order of nonoverlapping gene networks unraveled in mated female Drosophila.Life science alliance · 2022Article
- Network analysis reveals rare disease signatures across multiple levels of biological organization.Nature communications · 2021Article
- The VRNetzer platform enables interactive network analysis in Virtual Reality.Nature communications · 2021Article
- Chromatin recruitment of OGG1 requires cohesin and mediator and is essential for efficient 8-oxoG removal.Nucleic acids research · 2020Article
- MasterPATH: network analysis of functional genomics screening data.BMC genomics · 2020Article
- Bayesian integrative analysis of epigenomic and transcriptomic data identifies Alzheimer's disease candidate genes and networks.PLoS computational biology · 2020Article
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Authors and funding
6 authors.
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Abstract
motivationIncorporating gene interaction data into the identification of 'hit' genes in genomic experiments is a well-established approach leveraging the 'guilt by association' assumption to obtain a network based hit list of functionally related genes. We aim to develop a method to allow for multivariate gene scores and multiple hit labels in order to extend the analysis of genomic screening data within such an approach.
resultsWe propose a Markov random field-based method to achieve our aim and show that the particular advantages of our method compared with those currently used lead to new insights in previously analysed data as well as for our own motivating data. Our method additionally achieves the best performance in an independent simulation experiment. The real data applications we consider comprise of a survival analysis and differential expression experiment and a cell-based RNA interference functional screen. AVAILABILITY AND IMPLEMENTATION: We provide all of the data and code related to the results in the paper. CONTACT: sean.j.robinson@utu.fi or laurent.guyon@cea.fr. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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