| dc.description.abstract | The rapid emergence of drug-resistant bacteria due to the abuse of antibiotics is a serious threat to global public health, which makes finding new therapeutics to assist or improve this problem more urgent. Owing to the specificity of the mechanism of action of antimicrobial peptides (AMPs) against microbial pathogens, AMPs are not readily leading to the occurrence of resistance, and AMPs can be produced by the innate immune system of almost all life forms. Many AMPs are broad-spectrum with effective activities against various infectious microorganisms, such as bacteria, viruses, fungi and parasites. But the development of AMPs is greatly limited by high development and manufacturing costs. Therefore, if the functional classes of AMPs can be more accurately predicted, it can not only effectively reduce the development and manufacturing costs, but also provide more information for the development of new drugs. In this study, we constructed multi-label classifiers by means of binary relevance and algorithm adaptation methods to predict whether AMPs have effective activities against bacteria, mammalian cells, fungi, virus and cancer cells. In addition, we also adopted forward feature selection to find informative features, explore these features from different aspects and use these features to retrain the classifiers, the retrained classifiers had the performance that under the curves (AUCs) of antibacterial, mammalian cells, antifungal, antiviral and anticancer are 0.9066, 0.8568, 0.8492, 0.9126 and 0.8639 on the independent testing data respectively and subset accuracy is 0.4978. The results show that our model can achieve good performance for distinguishing functional classes. | en_US |