Biological sequence classification utilizing positive and unlabeled data.

@article{citeulike:2562445, abstract = {MOTIVATION: In the genomics setting, an increasingly common data configuration consists of a small set of sequences possessing a targeted property (positive instances) amongst a large set of sequences for which class membership is unknown (unlabeled instances). Traditional two-class classification methods do not effectively handle such data. RESULTS: Here, we develop a novel method, Likely Positive-Iterative Classification (LP-IC), for this problem and contrast its performance with the few existing methods, most of which were devised and utilized in the text classification context. LPIC employs an iterative classification scheme and introduces a class dispersion measure, adopted from unsupervised clustering approaches, to monitor the model selection process. Using two case studies - prediction of HLA binding, and alternative splicing conservation between human and mouse - we show that LP-IC provides superior performance to existing methodologies in terms of: (i) combined accuracy and precision in positive identification from the unlabeled set; and (ii) predictive performance of the resultant classifiers on independent test data. CONTACT: mark@biostat.ucsf.edu.}, address = {Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA.}, author = {Xiao, Yuanyuan and Segal, Mark R R. }, citeulike-article-id = {2562445}, doi = {10.1093/bioinformatics/btn089}, issn = {1460-2059}, journal = {Bioinformatics}, keywords = {classification, machine\_learning, sequence}, month = {March}, posted-at = {2008-03-19 15:44:36}, priority = {3}, title = {Biological sequence classification utilizing positive and unlabeled data.}, url = {http://dx.doi.org/10.1093/bioinformatics/btn089}, year = {2008} }

TY - JOUR ID - citeulike:2562445 L3 - citeulike-article-id:2562445 TI - Biological sequence classification utilizing positive and unlabeled data. JF - Bioinformatics AD - Department of Epidemiology and Biostatistics, Center for Bioinformatics and Molecular Biostatistics, University of California, 185 Berry Street, Lobby 4, Suite 5700, San Francisco, CA 94107, USA. SN - 1460-2059 N2 - MOTIVATION: In the genomics setting, an increasingly common data configuration consists of a small set of sequences possessing a targeted property (positive instances) amongst a large set of sequences for which class membership is unknown (unlabeled instances). Traditional two-class classification methods do not effectively handle such data. RESULTS: Here, we develop a novel method, Likely Positive-Iterative Classification (LP-IC), for this problem and contrast its performance with the few existing methods, most of which were devised and utilized in the text classification context. LPIC employs an iterative classification scheme and introduces a class dispersion measure, adopted from unsupervised clustering approaches, to monitor the model selection process. Using two case studies - prediction of HLA binding, and alternative splicing conservation between human and mouse - we show that LP-IC provides superior performance to existing methodologies in terms of: (i) combined accuracy and precision in positive identification from the unlabeled set; and (ii) predictive performance of the resultant classifiers on independent test data. CONTACT: mark@biostat.ucsf.edu. KW - classification KW - machine_learning KW - sequence AU - Xiao, Yuanyuan AU - Segal, Mark R PY - 2008/03/14/ UR - http://dx.doi.org/10.1093/bioinformatics/btn089 ER -