ArticlePLoS computational biology2010
An integrative multi-network and multi-classifier approach to predict genetic interactions.
Article in PLoS computational biology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it, 82 citations in OpenAlex.
- Synthetic lethal gene pairs: Experimental approaches and predictive models.Frontiers in genetics · 2022Pooled it
- MAGICAL: A multi-class classifier to predict synthetic lethal and viable interactions using protein-protein interaction network.PLoS computational biology · 2024Article
- Synthetic lethal connectivity and graph transformer improve synthetic lethality prediction.Briefings in bioinformatics · 2024Article
- MPASL: multi-perspective learning knowledge graph attention network for synthetic lethality prediction in human cancer.Frontiers in pharmacology · 2024Article
- Prioritizing candidate eQTL causal genes in Arabidopsis using RANDOM FORESTS.G3 (Bethesda, Md.) · 2022Article
- Computational methods, databases and tools for synthetic lethality prediction.Briefings in bioinformatics · 2022Review
- Multi-view graph convolutional network for cancer cell-specific synthetic lethality prediction.Frontiers in genetics · 2022Article
- Article
- Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.BMC genomics · 2020Article
- G2G: A web-server for the prediction of human synthetic lethal interactions.Computational and structural biotechnology journal · 2020Article
- EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction.Frontiers in pharmacology · 2020Article
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities.An international journal on information fusion · 2019Article
- HRGPred: Prediction of herbicide resistant genes with k-mer nucleotide compositional features and support vector machine.Scientific reports · 2019Article
- Dachshund Depletion Disrupts Mammary Gland Development and Diverts the Composition of the Mammary Gland Progenitor Pool.Stem cell reports · 2019Article
- Radiation Therapy Outcomes Models in the Era of Radiomics and Radiogenomics: Uncertainties and Validation.International journal of radiation oncology, biology, physics · 2018Article
- Using deep learning to model the hierarchical structure and function of a cell.Nature methods · 2018Article
- Predictability of Genetic Interactions from Functional Gene Modules.G3 (Bethesda, Md.) · 2017Article
- Human Pancreatic β Cell lncRNAs Control Cell-Specific Regulatory Networks.Cell metabolism · 2017Article
- LEARNING PARSIMONIOUS ENSEMBLES FOR UNBALANCED COMPUTATIONAL GENOMICS PROBLEMS.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2017Article
- Article
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Genetic interactions occur when a combination of mutations results in a surprising phenotype. These interactions capture functional redundancy, and thus are important for predicting function, dissecting protein complexes into functional pathways, and exploring the mechanistic underpinnings of common human diseases. Synthetic sickness and lethality are the most studied types of genetic interactions in yeast. However, even in yeast, only a small proportion of gene pairs have been tested for genetic interactions due to the large number of possible combinations of gene pairs. To expand the set of known synthetic lethal (SL) interactions, we have devised an integrative, multi-network approach for predicting these interactions that significantly improves upon the existing approaches. First, we defined a large number of features for characterizing the relationships between pairs of genes from various data sources. In particular, these features are independent of the known SL interactions, in contrast to some previous approaches. Using these features, we developed a non-parametric multi-classifier system for predicting SL interactions that enabled the simultaneous use of multiple classification procedures. Several comprehensive experiments demonstrated that the SL-independent features in conjunction with the advanced classification scheme led to an improved performance when compared to the current state of the art method. Using this approach, we derived the first yeast transcription factor genetic interaction network, part of which was well supported by literature. We also used this approach to predict SL interactions between all non-essential gene pairs in yeast (http://sage.fhcrc.org/downloads/downloads/predicted_yeast_genetic_interactions.zip). This integrative approach is expected to be more effective and robust in uncovering new genetic interactions from the tens of millions of unknown gene pairs in yeast and from the hundreds of millions of gene pairs in higher organisms like mouse and human, in which very few genetic interactions have been identified to date.
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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.