This book provides an overview of basic and advanced computational techniques for analyzing and understanding protein, RNA, and DNA sequences. It covers effective computing techniques for DNA and protein classifications, evolutionary and sequence information analysis, evolutionary algorithms, and ensemble algorithms. Furthermore, the book reviews the role of machine learning techniques, artificial intelligence, ensemble learning, and sequence-based features in predicting post-translational modifications in proteins, DNA methylation, and mRNA methylation, along with their functional implications. The book also discusses the prediction of protein–protein and protein–DNA interactions, protein structure, and function using computational methods. It also presents techniques for quantitative analysis of protein–DNA interactions and protein methylation and their involvement in gene regulation. Additionally, the use of nature-inspired algorithms to gain insights into gene regulatory mechanisms and metabolic pathways in human diseases is explored. This book acts as a useful reference for bioinformaticians and computational biologists working in the fields of molecular biology, genomics, and bioinformatics.
Key Features:
Reviews machine learning techniques for DNA sequence classification and protein structure predictionDiscusses genetic algorithms for analyzing multiple sequence alignments and predicting protein–protein interaction sitesExplores computational methods for quantitative analysis of protein–DNA interactionsExamine the role of nature-inspired algorithms in understanding the gene regulation and metabolic pathwaysCovers evolutionary algorithms and sequence-based features in predicting post-translational modifications
This book provides an overview of basic and advanced computational techniques for analysing and understanding protein, RNA, and DNA sequences. This book acts as useful reference for bioinformaticians and computational biologists working in the field of molecular biology, genomics, and bioinformatics.
- Machine Learning and Computational Models for the Prediction of Post-translational Modification Sites.
2. Application of Artificial Intelligence in Recognition of Gene Regulation and Metabolic Pathways.
3. Assessment of Machine Learning Algorithms in DNA Sequence Data Mining.
4. Efficient Detection and Recuperation of Mental Health Using Twitter and Fitbit Data-Based Recommendation System.
5. Role of Artificial Intelligence in Detection of Congenital Diseases.
6. A Hybrid Multi-Level Segmentation-Based Ensemble Classification Model for Multi-Class Diabetic Retinopathy Detection.
7. Innovative Approaches to Bilirubin Detection: Utilizing Smart Sensor Technologies for Enhanced Diagnostic Capabilities.
8. Targeted Immunization: Application of Machine Learning in Prediction of IL-4 Inducing Peptides.
9. Healthcare Portal - Django Framework for Healthcare Management System.
10. Unveiling Genetic Codes: Harnessing Machine Learning and Deep Learning for Deoxyribonucleic Acid Sequence Analysis.
Saiyed Umer is currently serving as an Assistant Professor in the Department of Computer Science and Engineering Aliah University, Kolkata, India. He was the Research Personnel at Indian Statistical Institute (ISI), Kolkata, India, from November 2012 to April 2017. He received a PhD Degree (Engineering executed in ISI Kolkata) from the Department of Information Technology at Jadavpur University, Kolkata, India, in March 2017. He earned B.Sc. (Hons) degree in Mathematics from Vidyasagar University, India, in 2005 and a Master of Computer Applications from the West Bengal University of Technology, India, in 2008 respectively. Dr Umer received an M.Tech degree from the University of Kalyani, India, in 2012. He has published several papers in peer-reviewed international and scientific journals in the field of Biometric, Affective Computing, Big-data research, Business Human Resource Management, and Computational Biology. His research interests include Computer Vision, Machine Learning, Deep Learning, and Business data analytics techniques.
Ranjeet Kumar Rout is currently serving as Assistant Professor in the Department of Information Technology, Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, India. Formally, he was the Assistant Professor in the Department of Computer Science and Engineering, National Institute of Technology Srinagar, Hazratbal, India. He received his Ph.D. from the department of Information Technology of Indian Institute of Engineering Science and Technology Shibpur, West Bengal, India. Previously, he earned Post Graduate and bachelors degree in computer science and Engineering from Biju Patnaik University of Technology, Odisha, India, in 2010 and 2005, respectively. Prior to working at NIT Srinagar, Dr. Ranjeet had research and teaching experience from Amity University Noida, National Institute Technology Jalandhar, and Indian Statistical Institute (ISI) Kolkata, India. His research interests include machine learning, deep learning, visual cryptography, and computational biology. He has published several papers in peer reviewed international and scientific journals in the field of non-linear Boolean functions and computational biology.
Monika Khandelwal is currently an Assistant Professor in the Department of Computer Science and Engineering, Jaypee University, Solan, Himachal Pradesh, India. She earned her Ph.D. from the Department of Computer Science and Engineering at National Institute of Technology Srinagar, Hazratbal, India. She received her M.Tech. Degree in Computer Science and Engineering from Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, India in 2016 and B.Tech. Degree in Computer Science and Engineering from Guru Jambheshwar University of Science & Technology, Hisar, Haryana, India. Prior to joining Ph.D. at NIT Srinagar, she had teaching experience from National Institute Technology Hamirpur and Malaviya National Institute of Technology Jaipur, India. She has published several papers in conferences, journals, and book chapters. Her research interests include machine learning, deep learning, bioinformatics, and computational biology.
Smitarani Pati is working in the Instrumentation and Control Engineering on modeling, control, and optimization of industrial processes such as energy optimization using soft computing techniques. She earned her Ph.D. Degree at Instrumentation and Control Engineering from Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, India. She received the B. Tech degree in electrical engineering from the Biju Patnaik University of Technology, Odisha, India, in 2011, the M.Tech degree in control and Instrumentation Engineering from Dr. B.R. Ambedkar National Institute of Technology Jalandhar, Punjab, India 2018. She has published several articles in international conferences and book chapters. Her current research interests include Energy modeling and optimization, design of distributed systems, and fault-tolerant controls.
