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211026s2021 enka ob 001 0 eng d |
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|a YDX
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|a 9780323900058
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|z 9780323897754
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|z 0323897754
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|a QH324.2
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|a 570.285
|2 23
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|a Bioinformatics :
|b methods and applications /
|c edited by Dev Bukhsh Singh and Rajesh Kumar Pathak.
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|a London :
|b Academic Press,
|c 2021.
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|a 1 online resource :
|b illustrations (some color)
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|a text
|b txt
|2 rdacontent
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|a computer
|b c
|2 rdamedia
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|a online resource
|b cr
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|a Includes bibliographical references and index.
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0 |
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|a Introduction to basics of bioinformatics -- Biological databases and their application -- Biological sequence analysis -- Genome assembly and annotation -- Computational molecular phylogeny: concepts and applications -- Applications and challenges of microarray and RNA-sequencing -- RNA-seq for revealing the function of the transcriptome -- Analysis of SSR and SNP markers -- Gene ontology: application and importance in functional annotation of the genomic data -- Metagenomics: the boon for microbial world knowledge and current challenges -- Protein structure prediction -- Structural and functional analysis of protein -- Computational approaches in drug designing -- Structure-based drug designing -- Ligand-based drug designing -- Discovery and optimization of lead molecules in drug designing -- Pharmacophore modeling and its applications -- Molecular docking and molecular dynamics simulation -- Pharmacokinetics and pharmacodynamics analysis of drug candidates -- Computational approaches for vaccine designing -- Metabolomics and flux balance analysis -- Topological parameters, patterns, and motifs in biological networks -- Network biology and applications -- Pathway modeling and simulation analysis -- Systems biology and big data analytics -- Machine learning in bioinformatics -- Bioinformatics and biological data mining.
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520 |
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|a Systems biology is an emerging field to study the complex interactions in biological systems. The enormous amount of data generated through high-throughput technologies allows the reconstruction of the biological pathways in a structured and dynamic way at the systems level. Hence, for understating the dynamics of these biological pathways, it is necessary to convert the molecular data in the form of biological pathways. Graph theory is utilized for the modeling of complex biological data. Graphs are used to analyze, simulate, and visualize the biological pathways, such as protein-protein interaction and protein metabolite interaction. Motifs are used for the analysis of complex biological networks. The network motifs describe the local properties of a network and are represented as a small connected subgraph, which is frequently appearing in a network. In this chapter, we have described the structure of biological networks, graph theory including its statistical parameters, network motifs, different types of algorithms, and its role in the biological application.
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650 |
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|a Bioinformatics.
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650 |
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2 |
|a Computational Biology
|0 (DNLM)D019295
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650 |
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6 |
|a Bio-informatique.
|0 (CaQQLa)201-0313075
|
650 |
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7 |
|a Bioinformatics
|2 fast
|0 (OCoLC)fst00832181
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700 |
1 |
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|a Singh, Dev Bukhsh.
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700 |
1 |
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|a Pathak, Rajesh Kumar.
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776 |
0 |
8 |
|i Print version:
|t Bioinformatics.
|d London : Academic Press, 2021
|z 0323897754
|z 9780323897754
|w (OCoLC)1237630627
|
856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/book/9780323897754
|z Texto completo
|