Abstract

AIDS, which is caused by the most widespread HIV-1 virus, attacks the immune system of the human body, and despite the incredible endeavors for finding proficient medication strategies, the continuing spread of AIDS and claiming subsequent infections has not yet been decreased. Consequently, the discovery of innovative medicinal methodologies is highly in demand. Some available therapies, based on peptides, proclaim the treatment for several deadly diseases such as AIDS and cancer. Since many experimental types of research are restricted by the analysis period and expenses, computational methods overcome the issues effectually. In computational technique, the peptide residues with anti-HIV-1 activity are predicted by classification method, and the learning process of the classification is improved with significant features. Rough set-based algorithms are capable of dealing with the gaps and imperfections present in real-time data. In this work, feature selection using Rough Set Improved Harmony Search Quick Reduct and Rough Set Improved Harmony Search Relative Reduct with Rough Set Classification framework is implemented to classify Anti-HIV-1 peptides. The primary objective of the proposed methodology is to predict the peptides with an anti-HIV-1 activity using effective feature selection and classification algorithms incorporated in the proposed framework. The results of the proposed algorithms are comparatively studied with existing rough set feature selection algorithms and benchmark classifiers, and the reliability of the algorithms implemented in the proposed framework is measured by validity measures, such as Precision, Recall, F-measure, Kulczynski Index, and Fowlkes–Mallows Index. The final results show that the proposed framework analyzed and classified the peptides with a high predictive accuracy of 96%. In this study, we have investigated the ability of a rough set-based framework with sequence-based numeric features to classify anti-HIV-1 peptides, and the experimentation results show that the proposed framework discloses the most satisfactory solutions, where it rapidly congregates in the problem space and finds the best reduct, which improves the prediction accuracy of the given dataset.

Highlights

  • AIDS (Acquired Immunodeficiency Syndrome) is a deadly and overwhelming disease caused by the HIV virus [1]

  • This study proposes using the rough set rapid reduct and relative reduct with better Harmony Search (HS) Feature Selection (FS) to reduce the dimensions of a given dataset

  • The proposed Rough Set Improved Harmony Search Quick Reduct (RSIHSQR) and Rough Set Improved Harmony Search Relative Reduct (RSIHSRR) algorithms are evaluated with Rough Set Particle Swarm Optimization Quick Reduct (RSPSOQR) and Rough Set Particle Swarm Optimization Relative Reduct (RSPSORR) algorithms

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Summary

Introduction

AIDS (Acquired Immunodeficiency Syndrome) is a deadly and overwhelming disease caused by the HIV virus [1]. The HIV-1 type virus is the most widespread type and major stimulator of AIDS [2]. Many therapeutics have been discovered to save the infected person, but recovery is a challenging process; innovative and effective treatment for AIDS is needed. Peptide molecules are created by the dehydration condensation reaction of amino acids joined by peptide bonds. They are used to treat a variety of diseases [4]. Peptides that have an anti-HIV-1 activity are being focused on and reveal promising results, which gradually reduce the effects of AIDS [5]. Investigating the enormous anti-HIV-1 peptides demands much time and effort, where simplified methodologies, such as machine learning, can be developed and implemented to predict the anti-HIV-1 peptides [6]

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