Title: Prediction of Alzheimer associated proteins (PAAP): a perspective to understand Alzheimer disease for therapeutic design

Authors: Gaurav Gupta; Neha Gupta; Ankit Gupta; Pankaj Vaidya; Girish Kumar Singh; Varun Jaiswal

Addresses: Department of Computer Science Engineering, School of Electrical and Computer Science Engineering, Shoolini University, Solan, Himachal Pradesh, 173229, India ' Department of Biotechnology, School of Biotechnology, Guru Gobind Singh Indraprastha University, New Delhi, 110075, India ' Department of Computer Science Engineering, School of Electrical and Computer Science Engineering, Shoolini University, Solan, Himachal Pradesh, 173229, India ' Department of Computer Science Engineering, School of Electrical and Computer Science Engineering, Shoolini University, Solan, Himachal Pradesh, 173229, India ' Department of Computer Science, Dr. Harisingh Gour University, Sagar, Madhya Pradesh – 470003, India ' Department of Computer Science Engineering, School of Electrical and Computer Science Engineering, Shoolini University, Solan, Himachal Pradesh, 173229, India

Abstract: Alzheimer disease (AD) is a complex progressive neurodegenerative disease with no cure and its occurrence rate increased worldwide with an increase of human life span. It became the sixth killer in the USA and no vaccines are available for the disease. Its unclear aetiology is the major hurdle in therapeutics discovery against it. Discovery of proteins/genes associated with AD can decipher the disease aetiology and further discovery of vaccine and drug targets. Computational methods can be used to predict the association of all possible genes/proteins with AD. In the current research data of all known proteins/genes associated with AD was used to develop a machine learning-based method. High accuracy of the developed model warrants the reliability of the method. The developed method is expected to help in the understanding of AD and the discovery of new vaccine and drug target candidates for AD and it is available at http://117.242.138.233/cgienabled/index.html.

Keywords: AD; Alzheimer disease; machine learning; Protr; PAAP; prediction of Alzheimer associated protein; SVM; support vector machine; CD-HIT; UniPort; 10-fold-cross-validation; LibSVM; ROC - receiver operating characteristics Plot.

DOI: 10.1504/IJBRA.2021.117929

International Journal of Bioinformatics Research and Applications, 2021 Vol.17 No.4, pp.363 - 374

Received: 18 Jun 2018
Accepted: 09 Aug 2019
Published online: 05 Oct 2021 *

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