{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,19]],"date-time":"2025-03-19T15:07:33Z","timestamp":1742396853323},"reference-count":26,"publisher":"Oxford University Press (OUP)","issue":"23","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2007,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Motivation: Inferring genetic networks from time-series expression data has been a great deal of interest. In most cases, however, the number of genes exceeds that of data points which, in principle, makes it impossible to recover the underlying networks. To address the dimensionality problem, we apply the subset selection method to a linear system of difference equations. Previous approaches assign the single most likely combination of regulators to each target gene, which often causes over-fitting of the small number of data.<\/jats:p><jats:p>Results: Here, we propose a new algorithm, named LEARNe, which merges the predictions from all the combinations of regulators that have a certain level of likelihood. LEARNe provides more accurate and robust predictions than previous methods for the structure of genetic networks under the linear system model. We tested LEARNe for reconstructing the SOS regulatory network of Escherichia coli and the cell cycle regulatory network of yeast from real experimental data, where LEARNe also exhibited better performances than previous methods.<\/jats:p><jats:p>Availability: The MATLAB codes are available upon request from the authors.<\/jats:p><jats:p>Contact: \u00a0dunam@nims.re.kr or jfk@kribb.re.kr<\/jats:p>","DOI":"10.1093\/bioinformatics\/btm514","type":"journal-article","created":{"date-parts":[[2007,11,1]],"date-time":"2007-11-01T00:33:58Z","timestamp":1193877238000},"page":"3225-3231","source":"Crossref","is-referenced-by-count":14,"title":["Ensemble learning of genetic networks from time-series expression data"],"prefix":"10.1093","volume":"23","author":[{"given":"Dougu","family":"Nam","sequence":"first","affiliation":[{"name":"1 Korea Research Institute of Bioscience and Biotechnology (KRIBB), PO Box 115, Yuseong, Daejeon 305-600 and 2National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea"},{"name":"1 Korea Research Institute of Bioscience and Biotechnology (KRIBB), PO Box 115, Yuseong, Daejeon 305-600 and 2National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Ho","family":"Yoon","sequence":"additional","affiliation":[{"name":"1 Korea Research Institute of Bioscience and Biotechnology (KRIBB), PO Box 115, Yuseong, Daejeon 305-600 and 2National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jihyun F.","family":"Kim","sequence":"additional","affiliation":[{"name":"1 Korea Research Institute of Bioscience and Biotechnology (KRIBB), PO Box 115, Yuseong, Daejeon 305-600 and 2National Institute for Mathematical Sciences (NIMS), Yuseong, Daejeon 305-340, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2007,10,31]]},"reference":[{"key":"2023041107514303600_","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1126\/science.1072152","article-title":"Gene expression during the life cycle of Drosophila melanogaster","volume":"297","author":"Arbeitman","year":"2002","journal-title":"Science"},{"key":"2023041107514303600_","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1093\/bioinformatics\/btl003","article-title":"Inference of gene regulatory networks and compound mode of action from time course gene expression profiles","volume":"22","author":"Bansal","year":"2006","journal-title":"Bioinformatics"},{"key":"2023041107514303600_","doi-asserted-by":"crossref","first-page":"2883","DOI":"10.1093\/bioinformatics\/bti415","article-title":"A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae","volume":"21","author":"Chen","year":"2005","journal-title":"Bioinformatics"},{"key":"2023041107514303600_","first-page":"41","article-title":"Linear modeling of mRNA expression levels during CNS development and injury","volume":"4","author":"D\u2019Haeseleer","year":"1999","journal-title":"Pac. 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