{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T20:55:07Z","timestamp":1783198507293,"version":"3.54.6"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2005,3,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Protein dispensability is fundamental to the understanding of gene function and evolution. Recent advances in generating high-throughput data such as genomic sequence data, protein\u2013protein interaction data, gene-expression data and growth-rate data of mutants allow us to investigate protein dispensability systematically at the genome scale.<\/jats:p><jats:p>Results: In our studies, protein dispensability is represented as a fitness score that is measured by the growth rate of gene-deletion mutants. By the analyses of high-throughput data in yeast Saccharomyces cerevisiae, we found that a protein's dispensability had significant correlations with its evolutionary rate and duplication rate, as well as its connectivity in protein\u2013protein interaction network and gene-expression correlation network. Neural network and support vector machine were applied to predict protein dispensability through high-throughput data. Our studies shed some lights on global characteristics of protein dispensability and evolution.<\/jats:p><jats:p>Availability: The original datasets for protein dispensability analysis and prediction, together with related scripts, are available at http:\/\/digbio.missouri.edu\/~ychen\/ProDispen\/<\/jats:p><jats:p>Contact: \u00a0xudong@missouri.edu<\/jats:p>","DOI":"10.1093\/bioinformatics\/bti058","type":"journal-article","created":{"date-parts":[[2004,10,13]],"date-time":"2004-10-13T01:32:31Z","timestamp":1097631151000},"page":"575-581","source":"Crossref","is-referenced-by-count":86,"title":["Understanding protein dispensability through machine-learning analysis of high-throughput data"],"prefix":"10.1093","volume":"21","author":[{"given":"Yu","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2004,10,12]]},"reference":[{"key":"2023013107212090200_B1","unstructured":"Albert, R., Jeong, H., Barabasi, A. 2000Error and attack tolerance of complex networks. 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