Computational Intelligence for Genomics Data

Erscheinungsjahr 2025
ISBN: 978-0-443-30080-6
Verlag: Elsevier Science & Technology

Buch, Englisch, Format (B × H): 216 mm x 276 mm, Gewicht: 450 g

Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.

Autoren/Hrsg.

Mahmud, Mufti

Fachgebiete

• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Vorklinische Medizin: Grundlagenfächer Humangenetik
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Naturwissenschaften Biowissenschaften Biowissenschaften Genetik und Genomik (nichtmedizinisch)
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

Section 1: Introduction to biological data and analysis
1.1 Genomic data
1.2 Microarray analysis
1.3 Hub gene selection
1.4 Pathogenesis
1.5 Expressive gene
1.6 Gene reduction
1.7 Biomarkers

Section 2: Traditional Machine learning models for gene selection and classification
2.1 Gene selection and liver disease classification using machine learning
2.2 Gene selection and Diabetic kidney disease classification using machine learning
2.3. Gene selection and neurodegenerative disease classification using machine learning
2.4. Gene selection and neuromuscular disorder classification using machine learning
2.5. Gene selection and cancer classification using machine learning
2.6. Gene selection and disease classification using machine learning

Section3: Deep learning models for gene selection and classification
3.1 Gene selection and liver disease classification using deep learning
3.2 Gene selection and Diabetic kidney disease classification using machine learning
3.3. Gene selection and neurodegenerative disease classification using deep learning
3.4. Gene selection and neuromuscular disorder classification using deep learning
3.5. Gene selection and cancer classification using deep learning
3.6. Gene selection and disease classification using deep learning

Section 4: Gene selection and classification using Artificial intelligence-based optimization methods
4.1 Gene selection and liver disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.2 Gene selection and Diabetic kidney disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.3. Gene selection and neurodegenerative disease classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.4. Gene selection and neuromuscular disorder classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.
4.5 Gene selection and cancer classification using Particle warm optimization, genetic algorithm, principal component analysis, wolf optimization, ant colony optimization etc.

Section 5: Explainable AI for computational biology
5.1. Use of LIME for diagnosis of disease
5.2. Use of Shape for diagnosis of disease
5.3. Quantitative graph theory for integrated omics data

Section 6: Applications of computational biology in healthcare
6.1 Diagnosis of liver disorder
6.2 Diagnosis of diabetic kidney disease
6.3 Diagnosis of cancer
6.4 Diagnosis of neurodegenerative disorder.
6.5 Diagnosis of neuromuscular disorder
6.6. Diagnosis of any other health disorder

Über Autor(innen)

Mahmud, Mufti
Mufti Mahmud is an Associate Professor of Cognitive Computing at the Department of Computer Science of Nottingham Trent University (NTU). Dr. Mahmud was appointed to the USET, University Shadow Executive Team, in 2022, providing specialist input to the University Executive Team and Vice-Chancellor on strategic policy and direction matters related to Equality, Diversity & Inclusion (EDI). He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group (ISRG) and the Cognitive Computing & Brain Informatics (CCBI) research group. He is also an active member of the Computing and Informatics Research Centre (CIRC) and the Medical Technologies Innovation Facility (MTIF). He is a member of the NTU Distance Learning Governance, Operation and Steering committee as well as the International Mobility Committee and serves as an independent end-point assessor for the Level 6 BSc (Hons) in Digital & Technology Solutions Professional Degree Apprenticeship, and an expert of the online master's degree in computer science. He led the teaching of the Big Data and its Infrastructures (Postgraduate - on-campus and online delivery) module. He is a Fellow of the Higher Education Academy, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association of Computing Machinery (ACM), and a professional member of the British Computer Society (BCS).