ArticleMolecular biology of the cell2014
Integration of biological data by kernels on graph nodes allows prediction of new genes involved in mitotic chromosome condensation.
Article in Molecular biology of the cell, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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21 citing papers in PubMed, 46 citations in OpenAlex.
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- LiveCellMiner: A new tool to analyze mitotic progression.PloS one · 2022Article
- Dual spindles assemble in bovine zygotes despite the presence of paternal centrosomes.The Journal of cell biology · 2021Article
- A Survey of Gene Prioritization Tools for Mendelian and Complex Human Diseases.Journal of integrative bioinformatics · 2019Review
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- Applications of molecular networks in biomedicine.Biology methods & protocols · 2019Review
- A quantitative map of human Condensins provides new insights into mitotic chromosome architecture.The Journal of cell biology · 2018Article
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- Structural and Functional View of Polypharmacology.Scientific reports · 2017Article
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- The Image Data Resource: A Bioimage Data Integration and Publication Platform.Nature methods · 2017Article
- How can functional annotations be derived from profiles of phenotypic annotations?BMC bioinformatics · 2017Article
- Defining functional interactions during biogenesis of epithelial junctions.Nature communications · 2016Article
- Exploring the interactions of the RAS family in the human protein network and their potential implications in RAS-directed therapies.Oncotarget · 2016Article
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- Sister chromatid resolution is an intrinsic part of chromosome organization in prophase.Nature cell biology · 2016Article
- Shaping mitotic chromosomes: From classical concepts to molecular mechanisms.BioEssays : news and reviews in molecular, cellular and developmental biology · 2015Review
Corrections and comments
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Authors and funding
16 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The advent of genome-wide RNA interference (RNAi)-based screens puts us in the position to identify genes for all functions human cells carry out. However, for many functions, assay complexity and cost make genome-scale knockdown experiments impossible. Methods to predict genes required for cell functions are therefore needed to focus RNAi screens from the whole genome on the most likely candidates. Although different bioinformatics tools for gene function prediction exist, they lack experimental validation and are therefore rarely used by experimentalists. To address this, we developed an effective computational gene selection strategy that represents public data about genes as graphs and then analyzes these graphs using kernels on graph nodes to predict functional relationships. To demonstrate its performance, we predicted human genes required for a poorly understood cellular function-mitotic chromosome condensation-and experimentally validated the top 100 candidates with a focused RNAi screen by automated microscopy. Quantitative analysis of the images demonstrated that the candidates were indeed strongly enriched in condensation genes, including the discovery of several new factors. By combining bioinformatics prediction with experimental validation, our study shows that kernels on graph nodes are powerful tools to integrate public biological data and predict genes involved in cellular functions of interest.
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