Computational Intelligence for Genomics Data

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