As cancer remains one of the main threats of human life, developing efficient cancer treatments is urgent. Anticancer peptides, which could overcome the significant side effects and poor results of traditional cancer treatments, have become a new potential alternative these years. However, identifying anticancer peptides by experimental methods is time consuming and resource consuming, it is of great significance to develop effective computational tools to quickly and accurately identify potential anticancer peptides from amino acid sequences. For most current computational methods, feature representation plays a key role in their final successes. This study proposes a novel fast and accurate approach to identify anticancer peptides using diversified feature representations and ensemble learning method. For the feature representations, the information is encoded from multidimensional feature spaces, including sequence composition, sequence-order, physicochemical properties, etc. In order to better model the potential relationships of peptides, multiple ensemble classifiers, LightGBMs, are applied to detect the different feature sets at first. Then the obtained multiple outputs are used as inputs of the support vector machine classifier, which effectively identifies anticancer peptides. Experimental results on cross validation and independent test sets demonstrate that our method can achieve better or comparable performances compared with other state-of-the-art methods.

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Cancer remains one of the most threatening diseases, which kills millions of lives every year. As a promising perspective for cancer treatments, anticancer peptides (ACPs) overcome a lot of disadvantages of traditional treatments. However, it is time-consuming and expensive to identify ACPs through conventional experiments. Hence, it is urgent and necessary to develop highly effective approaches to accurately identify ACPs in large amounts of protein sequences. In this work, we proposed a novel and effective method named ME-ACP which employed multi-view neural networks with ensemble model to identify ACPs. Firstly, we employed residue level and peptide level features preliminarily with ensemble models based on lightGBMs. Then, the outputs of lightGBM classifiers were fed into a hybrid deep neural network (HDNN) to identify ACPs. The experiments on independent test datasets demonstrated that ME-ACP achieved competitive performance on common evaluation metrics.