{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T05:54:11Z","timestamp":1725861251637},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319422909"},{"type":"electronic","value":"9783319422916"}],"license":[{"start":{"date-parts":[[2016,1,1]],"date-time":"2016-01-01T00:00:00Z","timestamp":1451606400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2016]]},"DOI":"10.1007\/978-3-319-42291-6_20","type":"book-chapter","created":{"date-parts":[[2016,7,11]],"date-time":"2016-07-11T11:10:22Z","timestamp":1468235422000},"page":"205-213","source":"Crossref","is-referenced-by-count":0,"title":["Performance and Improvement of Tree-Based Methods for Gene Regulatory Network Reconstruction"],"prefix":"10.1007","author":[{"given":"Ming","family":"Shi","sequence":"first","affiliation":[]},{"given":"Yan-Wen","family":"Chong","sequence":"additional","affiliation":[]},{"given":"Shao-Ming","family":"Pan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,7,12]]},"reference":[{"issue":"16","key":"20_CR1","doi-asserted-by":"crossref","first-page":"2020","DOI":"10.1093\/bioinformatics\/btq325","volume":"26","author":"T Zhou","year":"2010","unstructured":"Zhou, T., Wang, Y.L.: Causal relationship inference for a large-scale cellular network. Bioinformatics 26(16), 2020\u20132028 (2010)","journal-title":"Bioinformatics"},{"issue":"1","key":"20_CR2","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1093\/bioinformatics\/bts619","volume":"29","author":"XJ Zhang","year":"2013","unstructured":"Zhang, X.J., Liu, K.Q., Liu, Z.P., Duval, B., Richer, J.M., Zhao, X.M., Hao, J.K., Chen, L.N.: NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference. Bioinformatics 29(1), 106\u2013113 (2013)","journal-title":"Bioinformatics"},{"issue":"4","key":"20_CR3","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1049\/iet-syb.2014.0057","volume":"9","author":"YY Wang","year":"2015","unstructured":"Wang, Y.Y., Li, Z.G., Chen, T., Zhao, X.M.: Understanding the aristolochic acid toxicities in rat kidneys with regulatory networks. IET Syst. Biol. 9(4), 141\u2013146 (2015)","journal-title":"IET Syst. Biol."},{"issue":"6448","key":"20_CR4","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1038\/365704b0","volume":"365","author":"GA Dover","year":"1993","unstructured":"Dover, G.A.: The origins of order-self-organization and selection in evolution- Kauffman, SA. Nature 365(6448), 704\u2013706 (1993)","journal-title":"Nature"},{"issue":"1","key":"20_CR5","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1089\/10665270252833208","volume":"9","author":"H Jong De","year":"2002","unstructured":"De Jong, H.: Modeling and simulation of genetic regulatory systems: a literature review. J. Comput. Biol. 9(1), 67\u2013103 (2002)","journal-title":"J. Comput. Biol."},{"issue":"3","key":"20_CR6","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1073\/pnas.94.3.814","volume":"94","author":"HH McAdams","year":"1997","unstructured":"McAdams, H.H., Arkin, A.: Stochastic mechanisms in gene expression. Proc. Natl. Acad. Sci. USA 94(3), 814\u2013819 (1997)","journal-title":"Proc. Natl. Acad. Sci. USA"},{"issue":"4","key":"20_CR7","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1038\/35066056","volume":"2","author":"J Hasty","year":"2001","unstructured":"Hasty, J., McMillen, D., Isaacs, F., Collins, J.J.: Computational studies of gene regulatory networks: in numero molecular biology. Nat. Rev. Genet. 2(4), 268\u2013279 (2001)","journal-title":"Nat. Rev. Genet."},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Wille, A., Buhlmann, P.: Low-order conditional independence graphs for inferring genetic networks. Stat. Appl. Genet. Mol. Biol. Mol. 5(1) (2006)","DOI":"10.2202\/1544-6115.1170"},{"issue":"18","key":"20_CR9","doi-asserted-by":"crossref","first-page":"3565","DOI":"10.1093\/bioinformatics\/bth445","volume":"20","author":"A Fuente De La","year":"2004","unstructured":"De La Fuente, A., Bing, N., Hoeschele, I., Mendes, P.: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18), 3565\u20133574 (2004)","journal-title":"Bioinformatics"},{"issue":"2","key":"20_CR10","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1109\/TCBB.2008.87","volume":"7","author":"A Tenenhaus","year":"2010","unstructured":"Tenenhaus, A., Guillemot, V., Gidrol, X., Frouin, V.: Gene association networks from microarray data using a regularized estimation of partial correlation based on PLS regression. IEEE\/ACM Trans. Comput. Biol. Bioinform. 7(2), 251\u2013262 (2010)","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"issue":"486","key":"20_CR11","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1198\/jasa.2009.0126","volume":"104","author":"J Peng","year":"2009","unstructured":"Peng, J., Wang, P., Zhou, N.F., Zhu, J.: Partial correlation estimation by joint sparse regression models. J. Am. Stat. Assoc. 104(486), 735\u2013746 (2009)","journal-title":"J. Am. Stat. Assoc."},{"issue":"3","key":"20_CR12","first-page":"18","volume":"2","author":"A Liaw","year":"2002","unstructured":"Liaw, A., Wiener, M.: Classification and regression by Random Forest. R news 2(3), 18\u201322 (2002)","journal-title":"R news"},{"issue":"1","key":"20_CR13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","volume":"63","author":"P Geurts","year":"2006","unstructured":"Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3\u201342 (2006)","journal-title":"Mach. Learn."},{"key":"20_CR14","first-page":"479","volume":"1059","author":"H Drucker","year":"1996","unstructured":"Drucker, H., Cortes, C.: Boosting decision trees. Adv. Neural Inf. Process. Syst. 1059, 479\u2013485 (1996)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"8","key":"20_CR15","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1038\/nmeth.2016","volume":"9","author":"D Marbach","year":"2012","unstructured":"Marbach, D., Costello, J.C., Kuffner, R., Vega, N.M., Prill, R.J., Camacho, D.M., Allison, K.R., Kellis, M., Collins, J.J., Stolovitzky, G., DREAM5 Consortium: Wisdom of crowds for robust gene network inference. Nat. Methods 9(8), 796\u2013804 (2012)","journal-title":"Nat. Methods"},{"key":"20_CR16","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4614-7138-7","volume-title":"An Introduction to Statistical Learning","author":"G James","year":"2013","unstructured":"James, G., Witten, D., Hastie, T., Tibshirani, R.: An Introduction to Statistical Learning. Springer, New York (2013)"}],"container-title":["Lecture Notes in Computer Science","Intelligent Computing Theories and Application"],"original-title":[],"link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-42291-6_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:31:49Z","timestamp":1568147509000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-319-42291-6_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016]]},"ISBN":["9783319422909","9783319422916"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-42291-6_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2016]]}}}