Optimized Feature Learning for Anti-Inflammatory Peptide Prediction Using Parallel Distributed Computing

Author:

Khan Salman1ORCID,Khan Muhammad Abbas1,Khan Mukhtaj2,Iqbal Nadeem3,AlQahtani Salman A.4ORCID,Al-Rakhami Mabrook S.5ORCID,Khan Dost Muhammad6ORCID

Affiliation:

1. Department of Computer Science, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan

2. Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan

3. Division of Computer Science, Mathematics and Science, Collins College of Professional Studies, St. John’s University, New York, NY 11439, USA

4. Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

5. Department of Information Systems, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia

6. Department of Statistics, Abdul Wali Khan University Mardan, Mardan 23200, Pakistan

Abstract

With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling in size every 18 months, leading to a big data issue in computational biology. Moreover, inflammatory illnesses and boosting immune function have recently attracted a lot of attention, yet accurate recognition of Anti-Inflammatory Peptides (AIPs) through a biological process is time-consuming as therapeutic agents for inflammatory-related diseases. Similarly, precise classification of these AIPs is challenging for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models and deep neural networks have become major computing platforms for big data analytics now required in computational biology. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model. The model performance is extensively evaluated regarding performance measurement parameters such as accuracy, efficiency, scalability, and speedup in sequential and distributed environments. The encoding sequence data were balanced using the SMOTETomek approach, resulting in a high-accuracy performance. The parallel deep neural network demonstrated high speed up and scalability compared to other traditional classification algorithms study’s outcome could promote a parallel-based model for predicting anti-Inflammatory Peptides.

Funder

King Saud University

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

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