PMC:4331676 / 4561-5760 JSONTXT

{"project":"2_test","denotations":[{"id":"25708928-18515839-14842622","span":{"begin":526,"end":528},"obj":"18515839"},{"id":"25708928-15356290-14842623","span":{"begin":529,"end":531},"obj":"15356290"},{"id":"25708928-10427000-14842624","span":{"begin":761,"end":763},"obj":"10427000"},{"id":"25708928-16284202-14842625","span":{"begin":1055,"end":1057},"obj":"16284202"},{"id":"25708928-21069866-14842625","span":{"begin":1055,"end":1057},"obj":"21069866"},{"id":"25708928-19439068-14842626","span":{"begin":1079,"end":1081},"obj":"19439068"},{"id":"25708928-12589754-14842626","span":{"begin":1079,"end":1081},"obj":"12589754"},{"id":"25708928-15312763-14842626","span":{"begin":1079,"end":1081},"obj":"15312763"},{"id":"25708928-20089514-14842627","span":{"begin":1102,"end":1104},"obj":"20089514"},{"id":"25708928-16712732-14842628","span":{"begin":1131,"end":1133},"obj":"16712732"},{"id":"25708928-16899225-14842629","span":{"begin":1157,"end":1159},"obj":"16899225"},{"id":"25708928-18175049-14842630","span":{"begin":1187,"end":1189},"obj":"18175049"},{"id":"25708928-20386937-14842631","span":{"begin":1190,"end":1192},"obj":"20386937"}],"text":"In terms of classifiers, the computational methods can be divided into template-based and machine-learning-based methods, depending on how they use the information from the putative DNA-binding proteins. Template-based methods can be further classified into two classes, one of which utilize a structural comparison protocol to detect significant structural similarity between the query and a template known to bind DNA at either the domain or the structural motif to assess the DNA-binding preference of the target sequence [11,12] and the other employ a sequence comparison protocol (such as PSI-BLAST) to detect significant sequence similarity between the query and a template known to bind DNA to evaluate the DNA-binding preference of the target sequence [13]. Machine-learning-based methods do not perform direct structural comparison, but typically follow a machine-learning framework. To obtain good predictive model, various machine-learning algorithms have been employed to construct classification models, such as support vector machine (SVM) [14-17], neural network [18-22], random forest [23], naïve Bayes classifier [24,25], nearest neighbor [26] and ensemble classifiers [27,28], [29]"}