Computational Intelligence for Genomics Data

Section 1: Introduction to biological data and analysis1.1 Genomic data1.2 Microarray analysis1.3 Hub gene selection1.4 Pathogenesis1.5 Expressive gene1.6 Gene reduction1.7 BiomarkersSection 2: Traditional Machine learning models for gene selection and classification2.1 Gene selection and liver disease classification using machine learning2.2 Gene selection and Diabetic kidney disease classification using machine learning2.3. Gene selection and neurodegenerative disease classification using machine learning2.4. Gene selection and neuromuscular disorder classification using machine learning2.5. Gene selection and cancer classification using machine learning2.6. Gene selection and disease classification using machine learningSection3: Deep learning models for gene selection and classification3.1 Gene selection and liver disease classification using deep learning3.2 Gene selection and Diabetic kidney disease classification using machine learning3.3. Gene selection and neurodegenerative disease classification using deep learning3.4. Gene selection and neuromuscular disorder classification using deep learning3.5. Gene selection and cancer classification using deep learning3.6. Gene selection and disease classification using deep learningSection 4: Gene selection and classification using Artificial intelligence-based optimization methods4.1 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 biology5.1. Use of LIME for diagnosis of disease5.2. Use of Shape for diagnosis of disease5.3. Quantitative graph theory for integrated omics dataSection 6: Applications of computational biology in healthcare6.1 Diagnosis of liver disorder6.2 Diagnosis of diabetic kidney disease6.3 Diagnosis of cancer6.4 Diagnosis of neurodegenerative disorder.6.5 Diagnosis of neuromuscular disorder6.6. Diagnosis of any other health disorder