Soft computing for biological systems /
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| Imprint: | Singapore : Springer, 2018. |
|---|---|
| Description: | 1 online resource (xii, 300 pages) : illustrations (some color) |
| Language: | English |
| Subject: | |
| Format: | E-Resource Book |
| URL for this record: | http://pi.lib.uchicago.edu/1001/cat/bib/11543594 |
Table of 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.