Abstract
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.
Highlights
Cancer has become a common disease in humans, and it often leads to a higher mortality rate, especially in developing and developed countries (Ortega-Garcia et al, 2020)
In order to find the effective feature coding representation of the peptide sequence, four kinds of feature representation methods including 19 feature encodings were extracted in terms of amino acid composition, autocorrelation, pseudo amino acid composition and profile-based features
In terms of the various feature codes, pseudo amino acid composition worked best according to the value of the performance indexes Acc, AUC, Sp, Sn, and Matthews correlation coefficient (MCC)
Summary
Cancer has become a common disease in humans, and it often leads to a higher mortality rate, especially in developing and developed countries (Ortega-Garcia et al, 2020). The complexity and heterogeneity of cancer are major obstacles for anticancer therapy development (Kasak and Laan, 2020; Umbreit et al, 2020). Traditional cancer treatments, such as radiation therapy, targeted therapy and chemotherapy, often fail to distinguish cancer cells from normal cells. Traditional treatment methods have obvious side effects and poor results. In view of these problems, there is an urgent to discover and design novel cancer treatments and anticancer agents to fight against this deadly disease (Esfandiari Mazandaran et al, 2019; Sima et al, 2019; Bahuguna et al, 2020)
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