Soft computing for biological systems /

Soft computing for biological systems /

Saved in:
Bibliographic Details
Imprint:Singapore : Springer, 2018.
Description:1 online resource (xii, 300 pages) : illustrations (some color)
Language:English
Subject:
Computational biology.
Systems biology.
Soft computing.
Computational biology.
Soft computing.
Systems biology.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11543594
Hidden Bibliographic Details
Other authors / contributors:Purohit, H. J. (Hemant J.), editor.
Kalia, Vipin Chandra, editor.
More, Ravi Prabhakar, editor.
ISBN:9789811074554
9811074550
9789811074561
9811074569
9789811339516
9811339511
9789811074547
9811074542
Digital file characteristics:text file PDF
Notes:Includes index.
Online resource; title from PDF title page (SpringerLink, viewed February 26, 2018).
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.
Other form:Print version: Soft computing for biological systems. Singapore : Springer, 2018 9811074542 9789811074547
Standard no.:10.1007/978-981-10-7455-4

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245 0 0 |a Soft computing for biological systems /  |c Hemant J. Purohit, Vipin Chandra Kalia, Ravi Prabhakar More, editors. 
264 1 |a Singapore :  |b Springer,  |c 2018. 
300 |a 1 online resource (xii, 300 pages) :  |b illustrations (some color) 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
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500 |a Includes index. 
588 0 |a Online resource; title from PDF title page (SpringerLink, viewed February 26, 2018). 
505 0 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
505 8 |a 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. 
520 |a 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. 
650 0 |a Computational biology.  |0 http://id.loc.gov/authorities/subjects/sh2003008355 
650 0 |a Systems biology.  |0 http://id.loc.gov/authorities/subjects/sh2008002926 
650 0 |a Soft computing.  |0 http://id.loc.gov/authorities/subjects/sh96004789 
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655 4 |a Electronic books. 
700 1 |a Purohit, H. J.  |q (Hemant J.),  |e editor.  |0 http://id.loc.gov/authorities/names/no2001053554 
700 1 |a Kalia, Vipin Chandra,  |e editor.  |0 http://id.loc.gov/authorities/names/n2015181242 
700 1 |a More, Ravi Prabhakar,  |e editor. 
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