{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T15:59:37Z","timestamp":1648915177427},"reference-count":0,"publisher":"World Scientific Pub Co Pte Lt","issue":"supp01","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[1992,1]]},"abstract":"<jats:p> Knowledge-based approaches are being increasingly used in predicting protein structure and motifs. Machine learning techniques such as neural networks and decision-trees have become invaluable tools for these approaches. This paper describes the use of machine learning in predicting sequence-based motifs in antibody fragments. Given the limited number of three dimensional structures and the plethora of sequences, this technique is useful for homology modeling of three dimensional structures of antibody fragments. <\/jats:p>","DOI":"10.1142\/s0129065792000516","type":"journal-article","created":{"date-parts":[[2004,11,23]],"date-time":"2004-11-23T22:29:42Z","timestamp":1101248982000},"page":"183-193","source":"Crossref","is-referenced-by-count":1,"title":["MACHINE APPROACHES TO PROTEIN FEATURE PREDICTION"],"prefix":"10.1142","volume":"03","author":[{"given":"David K.","family":"Tcheng","sequence":"first","affiliation":[{"name":"Department of Physiology &amp; Biophysics, Department of Computer Science, National Center for Supercomputing Applications, and Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, IL 61801, USA"}]},{"given":"Shankar","family":"Subramaniam","sequence":"additional","affiliation":[{"name":"Department of Physiology &amp; Biophysics, Department of Computer Science, National Center for Supercomputing Applications, and Beckman Institute for Advanced Science and Technology, University of Illinois, Urbana, IL 61801, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"container-title":["International Journal of Neural Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0129065792000516","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,8,7]],"date-time":"2019-08-07T12:22:22Z","timestamp":1565180542000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0129065792000516"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[1992,1]]},"references-count":0,"journal-issue":{"issue":"supp01","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[1992,1]]}},"alternative-id":["10.1142\/S0129065792000516"],"URL":"https:\/\/doi.org\/10.1142\/s0129065792000516","relation":{},"ISSN":["0129-0657","1793-6462"],"issn-type":[{"value":"0129-0657","type":"print"},{"value":"1793-6462","type":"electronic"}],"subject":[],"published":{"date-parts":[[1992,1]]}}}