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000826446 020__ $$a9811074550$$q(electronic book)
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000826446 0247_ $$a10.1007/978-981-10-7455-4$$2doi
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000826446 049__ $$aISEA
000826446 050_4 $$aQH324.2
000826446 08204 $$a570.285$$223
000826446 24500 $$aSoft computing for biological systems /$$cHemant J. Purohit, Vipin Chandra Kalia, Ravi Prabhakar More, editors.
000826446 264_1 $$aSingapore :$$bSpringer,$$c2018.
000826446 300__ $$a1 online resource (xii, 300 pages) :$$billustrations
000826446 336__ $$atext$$btxt$$2rdacontent
000826446 337__ $$acomputer$$bc$$2rdamedia
000826446 338__ $$aonline resource$$bcr$$2rdacarrier
000826446 347__ $$atext file$$bPDF$$2rda
000826446 500__ $$aIncludes index.
000826446 5050_ $$aIntro; Preface; Contents; About the Editors; Chapter 1: Current Scenario on Application of Computational Tools in Biological Systems; 1.1 Introduction; 1.2 Protein Structure Prediction and Interaction; 1.3 Emerging Areas in Tool Development; 1.4 Gene Networks and Plasticity; 1.5 Epigenome: Emerging Area; 1.6 Expanding the Domain of Computational Statistical Analysis; 1.7 Pattern Recognition/Barcoding/Diagnostics; References; Chapter 2: Diagnostic Prediction Based on Gene Expression Profiles and Artificial Neural Networks; 2.1 Introduction; 2.2 Machine Learning and Artificial Neural Networks
000826446 5058_ $$a2.3 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
000826446 5058_ $$a3.4.1 Dataset3.4.2 Validation; 3.4.2.1 Functional Enrichment Analysis; 3.4.2.2 Topological Validation; 3.4.2.3 Experimental Results; 3.5 Conclusion and Future Scope; References; Chapter 4: A Hybridization of Artificial Bee Colony with Swarming Approach of Bacterial Foraging Optimization for Multiple Seq...; 4.1 Introduction; 4.2 Literature Review; 4.2.1 Genetic Algorithm (GA); 4.2.2 Particle Swarm Optimization (PSO); 4.2.3 Artificial Bee Colony (ABC); 4.2.4 Ant Colony Optimization (ACO); 4.2.5 Bacterial Foraging Optimization (BFO); 4.2.6 Bat and Firefly Optimization; 4.2.7 Cuckoo Search
000826446 5058_ $$a4.2.8 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
000826446 5058_ $$a5.3.2 Construction of Large-Scale Regulatory Networks5.4 Weighted Gene Co-expression Network Analysis (WGCNA); 5.5 Other Gene Co-expression Network Construction Applications; 5.6 Determining the Thresholds and Clusters for Co-expression Networks; 5.7 Network Concepts Useful in Co-expression Network Construction; 5.8 Conclusion; References; Chapter 6: Bioinformatics Tools for Shotgun Metagenomic Data Analysis; 6.1 Introduction; 6.2 Shotgun Metagenomics; 6.2.1 CAMERA; 6.2.2 MG-RAST; 6.2.3 IMG/M; 6.2.4 METAREP; 6.2.5 CoMet; 6.2.6 METAVIR; 6.2.7 MetaABC; 6.2.8 VIROME; 6.2.9 metaMicrobesOnline
000826446 506__ $$aAccess limited to authorized users.
000826446 520__ $$aThis book explains how the biological systems and their functions are driven by genetic information stored in the DNA, and their expression driven by different factors. The soft computing approach recognizes the different patterns in DNA sequence and try to assign the biological relevance with available information.The book also focuses on using the soft-computing approach to predict protein-protein interactions, gene expression and networks. The insights from these studies can be used in metagenomic data analysis and predicting artificial neural networks.
000826446 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 26, 2018).
000826446 650_0 $$aComputational biology.
000826446 650_0 $$aSystems biology.
000826446 650_0 $$aSoft computing.
000826446 7001_ $$aPurohit, H. J.$$q(Hemant J.),$$eeditor.
000826446 7001_ $$aKalia, Vipin Chandra,$$eeditor.
000826446 7001_ $$aMore, Ravi Prabhakar,$$eeditor.
000826446 77608 $$iPrint version: $$z9811074542$$z9789811074547$$w(OCoLC)1009052423
000826446 852__ $$bebk
000826446 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-981-10-7455-4$$zOnline Access$$91397441.1
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000826446 980__ $$aEBOOK
000826446 980__ $$aBIB
000826446 982__ $$aEbook
000826446 983__ $$aOnline
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