ArticlePLoS computational biology2007
Predicting gene expression from sequence: a reexamination.
Article in PLoS computational biology, 2007. 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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- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions.Scientific reports · 2023Article
- Enhanced performance of gene expression predictive models with protein-mediated spatial chromatin interactions.bioRxiv : the preprint server for biology · 2023Article
- Navigating the pitfalls of applying machine learning in genomics.Nature reviews. Genetics · 2022Review
- Using Machine Learning Approaches to Predict Target Gene Expression in Rice T-DNA Insertional Mutants.Frontiers in genetics · 2021Article
- Improved recovery of cell-cycle gene expression in Saccharomyces cerevisiae from regulatory interactions in multiple omics data.BMC genomics · 2020Article
- A comprehensive review of computational prediction of genome-wide features.Briefings in bioinformatics · 2020Article
- Deciphering eukaryotic gene-regulatory logic with 100 million random promoters.Nature biotechnology · 2020Article
- EAT-Rice: A predictive model for flanking gene expression of T-DNA insertion activation-tagged rice mutants by machine learning approaches.PLoS computational biology · 2019Article
- Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk.Nature genetics · 2018Article
- DNA methylation and gene expression in Mimulus guttatus.BMC genomics · 2015Article
- Predicting the human epigenome from DNA motifs.Nature methods · 2015Article
- Dissection of thousands of cell type-specific enhancers identifies dinucleotide repeat motifs as general enhancer features.Genome research · 2014Article
- Mapping yeast transcriptional networks.Genetics · 2013Review
- Highly parallel assays of tissue-specific enhancers in whole Drosophila embryos.Nature methods · 2013Article
- Genome-wide prediction and functional validation of promoter motifs regulating gene expression in spore and infection stages of Phytophthora infestans.PLoS pathogens · 2013Article
- Identifying and characterizing regulatory sequences in the human genome with chromatin accessibility assays.Genes · 2012Article
- Mapping of H3N2 influenza antigenic evolution in China reveals a strategy for vaccine strain recommendation.Nature communications · 2012Article
- Modeling the relative relationship of transcription factor binding and histone modifications to gene expression levels in mouse embryonic stem cells.Nucleic acids research · 2012Article
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Authors and funding
4 authors.
Funding
Abstract
Although much of the information regarding genes' expressions is encoded in the genome, deciphering such information has been very challenging. We reexamined Beer and Tavazoie's (BT) approach to predict mRNA expression patterns of 2,587 genes in Saccharomyces cerevisiae from the information in their respective promoter sequences. Instead of fitting complex Bayesian network models, we trained naïve Bayes classifiers using only the sequence-motif matching scores provided by BT. Our simple models correctly predict expression patterns for 79% of the genes, based on the same criterion and the same cross-validation (CV) procedure as BT, which compares favorably to the 73% accuracy of BT. The fact that our approach did not use position and orientation information of the predicted binding sites but achieved a higher prediction accuracy, motivated us to investigate a few biological predictions made by BT. We found that some of their predictions, especially those related to motif orientations and positions, are at best circumstantial. For example, the combinatorial rules suggested by BT for the PAC and RRPE motifs are not unique to the cluster of genes from which the predictive model was inferred, and there are simpler rules that are statistically more significant than BT's ones. We also show that CV procedure used by BT to estimate their method's prediction accuracy is inappropriate and may have overestimated the prediction accuracy by about 10%.
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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.