Anticancer Peptides Classification Using Kernel Sparse Representation Classifier

Fazal E. Ibrahim M.S. Park S. Naseem I. Wahab A.
2023 Institute of Electrical and Electronics Engineers Inc.

IEEE Access
2023 #11 17626 - 17637 pp.

Cancer is one of the most challenging diseases because of its complexity, variability, and diversity of causes. It has been one of the major research topics over the past decades, yet it is still poorly understood. To this end, multifaceted therapeutic frameworks are indispensable. Anticancer peptides (ACPs) are the most promising treatment option, but their large-scale identification and synthesis require reliable prediction methods, which is still a problem. In this paper, we present an intuitive classification strategy that differs from the traditional black-box method and is based on the well-known statistical theory of sparse-representation classification (SRC). Specifically, we create over-complete dictionary matrices by embedding the composition of the K-spaced amino acid pairs (CKSAAP). Unlike the traditional SRC frameworks, we use an efficient matching pursuit solver instead of the computationally expensive basis pursuit solver in this strategy. Furthermore, the kernel principal component analysis (KPCA) is employed to cope with non-linearity and dimension reduction of the feature space whereas the synthetic minority oversampling technique (SMOTE) is used to balance the dictionary. The proposed method is evaluated on two benchmark datasets for well-known statistical parameters and is found to outperform the existing methods. The results show the highest sensitivity with the most balanced accuracy, which might be beneficial in understanding structural and chemical aspects and developing new ACPs. The Google-Colab implementation of the proposed method is available on the GitHub page (https://github.com/ehtisham-Fazal/ACP-Kernel-SRC).

Amino acid composition (AAC) , anticancer peptide (ACP) , composition of the K-spaced amino acid pairs (CKSAAP) , kernel sparse reconstruction classification (KSRC) matching pursuit (MP) , over-complete dictionary (OCD) , sample-specific classification

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Lambda Theta, Karachi, 74270, Pakistan
Zhejiang University, College of Electrical Engineering, Hangzhou, 310027, China
National Cancer Institute, National Institutes of Health (NIH), Cancer Data Science Laboratory, Center for Cancer Research, Bethesda, 20894, MD, United States
University of Ulsan, Asan Medical Center, College of Medicine, Department of Anesthesiology and Pain Medicine, Seoul, Songpa-gu, 05505, South Korea
School of Electrical, Electronic and Computer Engineering, The University of Western Australia, Crawley, 6009, WA, Australia
Korangi Creek, College of Engineering, Karachi Institute of Economics and Technology, Karachi, 75190, Pakistan
Love for Data, Research and Development, Karachi, 75600, Pakistan
Nazarbayev University, Department of Mathematics, Astana, 010000, Kazakhstan

Lambda Theta
Zhejiang University
National Cancer Institute
University of Ulsan
School of Electrical
Korangi Creek
Love for Data
Nazarbayev University

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