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Feature weighting for antimicrobial peptides classification: A multi-objective evolutionary approach

机译：抗菌肽分类的特征权重：多目标进化方法

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Antimicrobial peptides might become crucial in fighting antibiotic resistant bacteria and other infections. Next Generation Sequencing technologies are generating a large amount of data where peptides with antimicrobial activity could be found. Therefore, algorithms that can efficiently determine whether or not a short sequence of amino acids is antimicrobial are needed. In this context, Quantitative Structure-Activity Relationship modeling has paved the way toward the association of the physicochemical properties of peptides to their biological activity. Nowadays, there are algorithms that can compute thousands of physicochemical properties known as molecular descriptors. However, some of these descriptors are irrelevant and some might even mislead the correct classification of the peptide activity. To mitigate this problem, a descriptor selection process must be performed, this will help to improve the classification accuracy and to decrease the computational time required for classification. In a recent work, a general method to weight and select features has been proposed. The method models the descriptor selection problem as a multi-objective optimization problem (MOOP). The main idea is to optimize simultaneously the intra- and inter-class distances. We follow this approach and apply it to the feature selection problem for the classification of antimicrobial peptides. To this aim we modify the original MOOP formulation to avoid bringing together non-antimicrobial peptides. Preliminary results indicate that our approach can substantially reduce the number of required molecular descriptors and improve the performance of classification with respect to the original formulation.
机译：抗菌肽可能在抵抗抗生素抗性细菌和其他感染方面变得至关重要。下一代测序技术正在产生大量数据，在这些数据中可以发现具有抗菌活性的肽。因此，需要能够有效地确定氨基酸的短序列是否具有抗菌性的算法。在这种情况下，定量结构-活性关系模型为肽的物理化学性质与其生物学活性之间的联系铺平了道路。如今，有一些算法可以计算成千上万的物理化学性质，称为分子描述符。然而，这些描述符中的一些无关紧要，甚至可能误导肽活性的正确分类。为了缓解此问题，必须执行描述符选择过程，这将有助于提高分类准确性并减少分类所需的计算时间。在最近的工作中，已经提出了一种加权和选择特征的通用方法。该方法将描述符选择问题建模为多目标优化问题（MOOP）。主要思想是同时优化类内和类间距离。我们遵循这种方法，并将其应用于特征选择问题以进行抗菌肽的分类。为此，我们修改了原始的MOOP配方，以避免将非抗菌肽结合在一起。初步结果表明，相对于原始配方，我们的方法可以大大减少所需的分子描述符的数量并提高分类性能。

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