Partial AUC maximization for essential gene prediction using genetic algorithms
2
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
Identifying genes indispensable for an organism‘s life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, proteinprotein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature’s relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.
Abstract
Identifying genes indispensable for an organism‘s life and their characteristics is one of the central questions in current biological research, and hence it would be helpful to develop computational approaches towards the prediction of essential genes. The performance of a predictor is usually measured by the area under the receiver operating characteristic curve (AUC). We propose a novel method by implementing genetic algorithms to maximize the partial AUC that is restricted to a specific interval of lower false positive rate (FPR), the region relevant to follow-up experimental validation. Our predictor uses various features based on sequence information, proteinprotein interaction network topology, and gene expression profiles. A feature selection wrapper was developed to alleviate the over-fitting problem and to weigh each feature’s relevance to prediction. We evaluated our method using the proteome of budding yeast. Our implementation of genetic algorithms maximizing the partial AUC below 0.05 or 0.10 of FPR outperformed other popular classification methods.
참고문헌 (22)
- Zhang, R., "DEG 5.0, a database of essential genes in both prokaryotes and eukaryotes", Nucleic Acids Res, 37, pp.D455~D458 (2009)
- Plaimas, K., "Identifying essential genes in bacterial metabolic networks with machine learning methods", BMC Syst. Biol, 4, pp.56~ (2010)
- Jordan, K., "Essential genes are more evolutionarily conserved than are nonessential genes in bacteria", Genome Res, 12, pp.962~968 (2002)
- Seringhaus, M., "Predicting essential genes in fungal genomes", Genome Res, 16, pp.1126~1135 (2006)
- Gustafson, A. M., "Towards the identification of essential genes using targeted genome sequencing and comparative analysis", BMC Genomics, 7, pp.265~ (2006)
- Dezso, Z., "Bioinformatics analysis of experimentally determined protein complexes in the yeast Saccharomyces cerevisiae", Genome Res, 13, pp.2450~2454 (2003)
- Jeong, H., "Prediction of protein essentiality based on genomic data", ComPlexUs, 1, pp.19~28 (2003)
- Yu, H., "Genomic analysis of essentiality within protein networks", Trends Genet, 20, pp.227~231 (2004)
- Hwang, Y. C., "Predicting essential genes based on network and sequence analysis", Mol. Biosyst, 5, pp.1672~1678 (2009)
- Choi, J. K., "Impact of transcriptional properties on essentiality and evolutionary rate", Genetics, 175, pp.199~206 (2007)
- Zhou, L., "The effects of protein interactions, gene essentiality and regulatory regions on expression variation", BMC Syst. Biol, 2, pp.54~ (2008)
- Deng, J., "Investigating the predictability of essential genes across distantly related organisms using an integrative approach", Nucleic Acids Res, 39, pp.795~807 (2011)
- Takenouchi, T., "An extension of the receiver operating characteristic curve and auc-optimal classification", Neural Comput, 24, pp.2789~2824 (2012)
- Salwinski, L., "The database of interacting proteins: 2004 update", Nucleic Acids Res, 32, pp.D449~D451 (2004)
- Barrett, T., "NCBI GEO: archive for functional genomics data sets-10 years on", Nucleic Acids Res, pp.D1005~D1010 (2011)
- Zhang, C. T., "Gene essentiality analysis based on DEG, a database of essential genes", Methods Mol. Biol, 416, pp.391~400 (2008)
- Krylov, D. M., "Gene loss, protein sequence divergence, gene dispensability, expression level, and interactivity are correlated in eukaryotic evolution", Genome Res, 13, pp.2229~2235 (2003)
- Jeong, H., "Lethality and centrality in protein networks", Nature, 411, pp.41~42 (2001)
- Sharp, P. M., "Codon usage in yeast: cluster analysis clearly differentiates highly and lowly expressed genes", Nucleic Acids Res, 14, pp.5125~5143 (1986)
- Jansen, R., "Relating whole-genome expression data with protein-protein interactions", Genome Res, 12, pp.37~46 (2002)
- Irizarry, R. A., "Exploration, normalization, and summarization of high density oligonucleotide array probe level data", Biostatistics, 4, pp.249~264 (2003)
- Kohavi, R., "Wrappers for feature selection", Artif. Intell, 97, pp.249~256 (1997)