BookHemant J. Purohit, Vipin Chandra Kalia, Ravi Prabhakar More, editors.
Summary: This book explains how the biological systems and their functions are driven by genetic information stored in the DNA, and their expression driven by different factors. The soft computing approach recognizes the different patterns in DNA sequence and try to assign the biological relevance with available information. The book also focuses on using the soft-computing approach to predict protein-protein interactions, gene expression and networks. The insights from these studies can be used in metagenomic data analysis and predicting artificial neural networks.
Contents:
Intro; Preface; Contents; About the Editors;
Chapter 1: Current Scenario on Application of Computational Tools in Biological Systems; 1.1 Introduction; 1.2 Protein Structure Prediction and Interaction; 1.3 Emerging Areas in Tool Development; 1.4 Gene Networks and Plasticity; 1.5 Epigenome: Emerging Area; 1.6 Expanding the Domain of Computational Statistical Analysis; 1.7 Pattern Recognition/Barcoding/Diagnostics; References;
Chapter 2: Diagnostic Prediction Based on Gene Expression Profiles and Artificial Neural Networks; 2.1 Introduction; 2.2 Machine Learning and Artificial Neural Networks. 2.3 Gene Expression Profile2.4 Gene Expression Profile Studies with ANN; 2.4.1 Cancer; 2.4.2 Chemotherapy; 2.4.3 Schizophrenia; 2.5 Perspectives; References;
Chapter 3: Soft Computing Approaches to Extract Biologically Significant Gene Network Modules; 3.1 Introduction; 3.2 Computational Methods for Detecting Network Modules; 3.3 Soft Computing Methods for Network Module Extraction; 3.3.1 Weighted Gene Co-expression Network Analysis (WGCNA); 3.3.2 Fuzzy Network Module Extraction; 3.3.3 GA-RNN Hybrid Approach; 3.3.4 Multisource Integrative Framework; 3.3.5 AutoSOME; 3.4 Assessment. 3.4.1 Dataset3.4.2 Validation; 3.4.2.1 Functional Enrichment Analysis; 3.4.2.2 Topological Validation; 3.4.2.3 Experimental Results; 3.5 Conclusion and Future Scope; References;
Chapter 4: A Hybridization of Artificial Bee Colony with Swarming Approach of Bacterial Foraging Optimization for Multiple Seq ... ; 4.1 Introduction; 4.2 Literature Review; 4.2.1 Genetic Algorithm (GA); 4.2.2 Particle Swarm Optimization (PSO); 4.2.3 Artificial Bee Colony (ABC); 4.2.4 Ant Colony Optimization (ACO); 4.2.5 Bacterial Foraging Optimization (BFO); 4.2.6 Bat and Firefly Optimization; 4.2.7 Cuckoo Search. 4.2.8 Frog Leap Algorithm4.2.9 Multiple Sequence Alignment Using Fuzzy Logic; 4.3 Methodology; 4.3.1 Optimizing the Multi-objectives; 4.3.1.1 Sequence Similarity; 4.3.1.2 Penalty of a Gap; Affine Gap Penalty; Variable Gap Penalty; 4.3.2 Hybrid of ABC-BFO; 4.4 Results; 4.4.1 Applications of MSA; 4.4.2 Statistical Analysis; 4.5 Implementation and Discussion; 4.6 Conclusion; References;
Chapter 5: Construction of Gene Networks Using Expression Profiles; 5.1 Introduction; 5.2 Genetic Regulatory Networks; 5.3 Co-expression Networks; 5.3.1 Identifying Genes with Key Roles. 5.3.2 Construction of Large-Scale Regulatory Networks5.4 Weighted Gene Co-expression Network Analysis (WGCNA); 5.5 Other Gene Co-expression Network Construction Applications; 5.6 Determining the Thresholds and Clusters for Co-expression Networks; 5.7 Network Concepts Useful in Co-expression Network Construction; 5.8 Conclusion; References;
Chapter 6: Bioinformatics Tools for Shotgun Metagenomic Data Analysis; 6.1 Introduction; 6.2 Shotgun Metagenomics; 6.2.1 CAMERA; 6.2.2 MG-RAST; 6.2.3 IMG/M; 6.2.4 METAREP; 6.2.5 CoMet; 6.2.6 METAVIR; 6.2.7 MetaABC; 6.2.8 VIROME; 6.2.9 metaMicrobesOnline.