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020 _a9789811074554
_9978-981-10-7455-4
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_2bicssc
072 7 _aSCI056000
_2bisacsh
072 7 _aPSD
_2thema
072 7 _aUB
_2thema
082 0 4 _a570.285
_223
245 1 0 _aSoft Computing for Biological Systems
_h[electronic resource] /
_cedited by Hemant J. Purohit, Vipin Chandra Kalia, Ravi Prabhakar More.
250 _a1st ed. 2018.
264 1 _aSingapore :
_bSpringer Singapore :
_bImprint: Springer,
_c2018.
300 _aXII, 300 p. 43 illus., 31 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
500 _aAcceso multiusuario
505 0 _a1. 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.
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.
541 _fUABC ;
_cTemporal ;
_d01/01/2021-12/31/2023.
650 0 _aBioinformatics.
650 0 _aGene expression.
650 0 _aBiomedical engineering.
650 0 _aMedical genetics.
650 1 4 _aBioinformatics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/L15001
650 2 4 _aGene Expression.
_0https://scigraph.springernature.com/ontologies/product-market-codes/B12010
650 2 4 _aBiomedical Engineering/Biotechnology.
_0https://scigraph.springernature.com/ontologies/product-market-codes/B24000
650 2 4 _aComputational Biology/Bioinformatics.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I23050
650 2 4 _aGene Function.
_0https://scigraph.springernature.com/ontologies/product-market-codes/B12030
700 1 _aPurohit, Hemant J.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aKalia, Vipin Chandra.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aMore, Ravi Prabhakar.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811074547
776 0 8 _iPrinted edition:
_z9789811074561
776 0 8 _iPrinted edition:
_z9789811339516
856 4 0 _zLibro electrónico
_uhttp://148.231.10.114:2048/login?url=https://doi.org/10.1007/978-981-10-7455-4
912 _aZDB-2-SBL
912 _aZDB-2-SXB
942 _cLIBRO_ELEC
999 _c244166
_d244165