04172nam 22006135 450 991029842530332120200706222812.0981-10-7455-010.1007/978-981-10-7455-4(CKB)4100000002485538(MiAaPQ)EBC5307254(DE-He213)978-981-10-7455-4(PPN)22463674X(EXLCZ)99410000000248553820180219d2018 u| 0engurcnu||||||||rdacontentrdamediardacarrierSoft Computing for Biological Systems /edited by Hemant J. Purohit, Vipin Chandra Kalia, Ravi Prabhakar More1st ed. 2018.Singapore :Springer Singapore :Imprint: Springer,2018.1 online resource (301 pages) illustrations981-10-7454-2 Includes bibliographical references at the end of each chapters and index.1. Diagnostic prediction based on gene expression profiles and artificial neural networks -- 2. Soft-Computing Approaches to Extract Biologically Significant Gene Network Modules -- 3. A Hybridization of Artificial Bee Colony with Swarming Approach of Bacterial Foraging Optimization for Multiple Sequence Alignment -- 4. Construction Gene Networks Using Gene Expression Profiles -- 5. Bioinformatics tools for shotgun metagenomic data analysis -- 6. Prediction of protein-protein interactions using machine learning techniques -- 7. Protein structure prediction using machine learning approaches -- 8. Drug-transporters as Therapeutic targets: Computational Models, Challenge and Opportunity -- 9. Module-Based Knowledge Discovery for Multiple-Cytosine-Variant Methylation Profile -- 10. Outlook of various soft computing data pre-processing techniques to study the pest population dynamics in Integrated Pest Management -- 11. Genomics for Oral Cancer Biomarker research -- 12. Soft-computing methods and tools for Bacteria DNA Barcoding data analysis -- 13. Fish DNA Barcoding: A comprehensive survey of the Bioinformatics tools and databases.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.BioinformaticsGene expressionBiomedical engineeringMedical geneticsBioinformaticshttps://scigraph.springernature.com/ontologies/product-market-codes/L15001Gene Expressionhttps://scigraph.springernature.com/ontologies/product-market-codes/B12010Biomedical Engineering/Biotechnologyhttps://scigraph.springernature.com/ontologies/product-market-codes/B24000Computational Biology/Bioinformaticshttps://scigraph.springernature.com/ontologies/product-market-codes/I23050Gene Functionhttps://scigraph.springernature.com/ontologies/product-market-codes/B12030Bioinformatics.Gene expression.Biomedical engineering.Medical genetics.Bioinformatics.Gene Expression.Biomedical Engineering/Biotechnology.Computational Biology/Bioinformatics.Gene Function.570.285Purohit Hemant Jedthttp://id.loc.gov/vocabulary/relators/edtKalia Vipin Chandraedthttp://id.loc.gov/vocabulary/relators/edtMore Ravi Prabhakaredthttp://id.loc.gov/vocabulary/relators/edtBOOK9910298425303321Soft Computing for Biological Systems2503041UNINA