ArticleMolecular systems biology2007
Prediction of phenotype and gene expression for combinations of mutations.
Article in Molecular systems biology, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
32 citing papers in PubMed, 48 citations in OpenAlex.
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- A parallelized strategy for epistasis analysis based on Empirical Bayesian Elastic Net models.Bioinformatics (Oxford, England) · 2020Article
- Past Roadblocks and New Opportunities in Transcription Factor Network Mapping.Trends in genetics : TIG · 2016Review
- Weak Epistasis Generally Stabilizes Phenotypes in a Mouse Intercross.PLoS genetics · 2016Article
- Automated Identification of Core Regulatory Genes in Human Gene Regulatory Networks.PLoS computational biology · 2015Article
- Epistasis and quantitative traits: using model organisms to study gene-gene interactions.Nature reviews. Genetics · 2014Review
- Article
- Dissection of complex gene expression using the combined analysis of pleiotropy and epistasis.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2014Article
- Dominant PRPF31 mutations are hypostatic to a recessive CNOT3 polymorphism in retinitis pigmentosa: a novel phenomenon of "linked trans-acting epistasis".Annals of human genetics · 2014Article
- Inferring gene function and network organization in Drosophila signaling by combined analysis of pleiotropy and epistasis.G3 (Bethesda, Md.) · 2013Article
- Article
- Systematic measurement of transcription factor-DNA interactions by targeted mass spectrometry identifies candidate gene regulatory proteins.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Prediction of gene phenotypes based on GO and KEGG pathway enrichment scores.BioMed research international · 2013Article
- Chapter 5: Network biology approach to complex diseases.PLoS computational biology · 2012Article
- Feature Identification of Compensatory Gene Pairs without Sequence Homology in Yeast.Comparative and functional genomics · 2012Article
- Predicting the effects of copy-number variation in double and triple mutant combinations.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2012Article
- Multiple genetic interaction experiments provide complementary information useful for gene function prediction.PLoS computational biology · 2012Article
- Use of pleiotropy to model genetic interactions in a population.PLoS genetics · 2012Article
- Quantitative epistasis analysis and pathway inference from genetic interaction data.PLoS computational biology · 2011Article
- Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Molecular interactions provide paths for information flows. Genetic interactions reveal active information flows and reflect their functional consequences. We integrated these complementary data types to model the transcription network controlling cell differentiation in yeast. Genetic interactions were inferred from linear decomposition of gene expression data and were used to direct the construction of a molecular interaction network mediating these genetic effects. This network included both known and novel regulatory influences, and predicted genetic interactions. For corresponding combinations of mutations, the network model predicted quantitative gene expression profiles and precise phenotypic effects. Multiple predictions were tested and verified.
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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.