Prediction of Alzheimer associated proteins (PAAP): a perspective to understand Alzheimer disease for therapeutic design. (22nd September 2021)

• Record Type: Journal Article
• Authors: Gupta, Gaurav; Gupta, Neha; Gupta, Ankit; Vaidya, Pankaj; Singh, Girish Kumar; Jaiswal, Varun
• Is Part Of: International journal of bioinformatics research and applications. Volume 17:Number 4(2021)
• Page Start: 363
• Page End: 374
• Publication Date: 2021-09-22
• Subjects: 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; Bioinformatics -- Periodicals; 570.285
• Languages: English
• ISSNs: 1744-5485

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.