cACP-2LFS: Classification of Anticancer Peptides Using Sequential Discriminative Model of KSAAP and Two-Level Feature Selection Approach

Abstract

Cancer is a leading killer disease globally, it occurs when the cellular changes cause the abnormal growth and division of the cells. Conventional treatment such as therapies and wet experimental methods are deemed unsatisfactory and worthless because of its huge cost and laborious nature. However, the recent innovation of anticancer peptides (ACPs) offers an effective way to treat cancer affected cells. Due to the rapid growth of biological sequences, truly identification of ACPs has become a difficult task for scientists. Therefore, measuring the importance of ACPs, an efficient and reliable intelligent model is highly essential to accurately identify its pattern. In this study, three distinct nature encoding schemes are employed to obtain features from peptide sequences. However, K-space amino acid pair (KSAAP) is used to extract highly correlated and effective descriptors. Apart from the sequential features, composite physiochemical properties are applied to gather local structure descriptors. Furthermore, to represent the intrinsic residue information of amino acids, autocovariance is also used. Additionally, a novel two-level feature selection (2LFS) method is utilized to select high discriminative features and to minimize the dimensionality of the proposed descriptors. At last, to examine the performance of the proposed model, several learning hypotheses are investigated to select a superior operational engine. To measure the generalization capability, two diverse benchmark datasets are used. After evaluating the empirical outcomes, KSAAP using 2LFS reported high classification results on both datasets. Whereas, the classification outcomes reveal that our proposed cACP-2LFS achieved ~11% improved performance accuracy than present models in the literature so far. It is expected that our proposed model might be useful in the area of medicine, proteomics, and research academia. The source code and all datasets are publicly available at https://github.com/shahidawkum/cACP-2LFS.

Recent Advances in Machine Learning-Based Models for Prediction of Antiviral Peptides

“…GAN has many real applications in different fields of life. For instance, improving cybersecurity [ 69 ], predictors in healthcare [ 70 ], stock market prediction [ 71 ], producing animation models[ 72 ], editing photographs, and image translation are popular applications [ 73 , 74 ].…”

A software tool for data mining of physicochemical properties of peptides

“…Research has shown that the natural biological properties of peptides are a complex combination of hydrophobicity, charge, molecular mass, reduction of the hydrophobic moment [1], and other physicochemical characteristics which can provide useful information in classifying, predicting, and synthesizing new peptides [2,3].…”

Prediction of Amyloid Proteins Using Embedded Evolutionary & Ensemble Feature Selection Based Descriptors With eXtreme Gradient Boosting Model

“…Additionally, to propose a computationally effective model, we used the eXtreme Gradient Boosting-Recursive Feature Elimination (XGB-RFE) to choose the optimal features from the heterogeneous feature vector. Moreover, the predictive rates of the extracted spaces are investigated via several learning models namely, Extra trees classifier (ETC) [34], XGBoost (XGB) [35], Support vector machine (SVM) [36,37], AdaBoost (Ada) [38], Fuzzy KNN (FKNN) [39], and Light Gradient Boosted Machine (LGBM) [40]. The flow diagram of our proposed model is shown in Figure 1.…”