PMC:4331678 / 4795-5641 JSONTXT

{"project":"2_test","denotations":[{"id":"25707434-20507895-14839061","span":{"begin":279,"end":281},"obj":"20507895"},{"id":"25707434-15130933-14839062","span":{"begin":389,"end":390},"obj":"15130933"},{"id":"25707434-19435516-14839063","span":{"begin":645,"end":647},"obj":"19435516"}],"text":"Current techniques on integrating multiple networks can be mainly divided into two categories: (i) several approaches model the composite kernel optimization and the final predictor training as separate problems. As such they may not necessarily result in optimal predictors [15,16]. (ii) Some methods optimize the composite network and the predictor for each functional label separately [5,14]. Since protein functions are inter-correlated and most functional labels often have a relatively small number of member proteins, these algorithms ignore the interrelationship among labels, which can often be used to boost the prediction accuracy [3,19]. Furthermore, they have to resort to time consuming special techniques (i.e., parameter tuning, regularization) to avoid the over-fitting problem and to optimize a composite network for each label."}