mACPpred: A Support Vector Machine-Based Meta-Predictor for Identification of Anticancer Peptides

Abstract

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field of immunoinformatics. Recently, machine learning algorithms have emerged as a promising tool for helping experimental scientists predict ACPs. However, the performance of existing methods still needs to be improved. In this study, we present a novel approach for the accurate prediction of ACPs, which involves the following two steps: (i) We applied a two-step feature selection protocol on seven feature encodings that cover various aspects of sequence information (composition-based, physicochemical properties and profiles) and obtained their corresponding optimal feature-based models. The resultant predicted probabilities of ACPs were further utilized as feature vectors. (ii) The predicted probability feature vectors were in turn used as an input to support vector machine to develop the final prediction model called mACPpred. Cross-validation analysis showed that the proposed predictor performs significantly better than individual feature encodings. Furthermore, mACPpred significantly outperformed the existing methods compared in this study when objectively evaluated on an independent dataset.

iQSP: A Sequence-Based Tool for the Prediction and Analysis of Quorum Sensing Peptides Using Informative Physicochemical Properties

“…In order to establish a robust sequence-based tool for modeling the investigated QSPs, we followed Chou's five-step guidelines as mentioned in a series of recent publications [67][68][69][70][71][72] and summarized in two comprehensive review papers [34,35]: (i) Compilation of a reliable dataset that contains experimentally validated sequences for training and validating the model; (ii) quantifying peptides sequences to describe their physicochemical properties; (iii) developing the prediction model using robust algorithm; (iv) assess the prediction model using standard cross-validation tests; and (v) constructing a user-friendly web-server for obtaining the prediction without the need to understand complex mathematical and statistical details. Furthermore, Figure 2 shows the workflow of iQSP which works in discriminating peptides as QSPs or Non-QSPs.…”

“…SVM method is an effective ML algorithm for supervised pattern and has been widely used in various biological problems [36][37][38][39]41,43,45,52,[71][72][73][74][75][76][77][78]. This method is based on the Vapnik-Chervonenkis theory of statistical learning [79][80][81].…”

Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

“…Many if not most machine learning algorithms have actually been developed for a fairly low number of variables, and using a very large number of features will likely result in overfitting [62]. It is, therefore, a requirement to remove noisy and redundant features with the help of one or more feature selection algorithms [63]. A variety of such algorithms have been published, but few comparative performance data with respect to these algorithms are available.…”

Design of a Chamfering Tool Diagnosis System Using Autoencoder Learning Method

“…To develop and evaluate the prediction model, three CV techniques such as K-fold CV, leave-one-out CV, and independent dataset are frequently used [28][29][30][31]. To reduce the noise of dataset influence, we attempted the K-fold CV, where K = 4.…”