Umer, saiyed (29 Ergebnisse)

paperback. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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hardcover. Zustand: Fine.

Paperback. Zustand: Brand New. 344 pages. 9.18x6.12x9.21 inches. In Stock.

Taschenbuch. Zustand: Neu. Artificial Intelligence Technologies for Computational Biology | Ranjeet Kumar Rout (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2024 | CRC Press | EAN 9781032160023 | Verantwortliche Person für die EU: Taylor & Francis Verlag GmbH, Kaufingerstr. 24, 80331 München, gpsr[at]taylorandfrancis[dot]com | Anbieter: preigu. …

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hardcover. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

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Hardcover. Zustand: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Zustand: As New. Unread book in perfect condition.

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Hardcover. Zustand: Brand New. 200 pages. 9.19x6.13x0.79 inches. In Stock.

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Hardback. Zustand: New. This book provides an overview of basic and advanced computational techniques for analyzing and understanding protein, RNA, and DNA sequences. It covers effective computing techniques for DNA and protein classifications, evolutionary and sequence information analysis, evolutionary algorithms, and ensemble algorithms. Furthermore, the book reviews the role of machine learning techniques, artificial intelligence, ensemble learning, and sequence-based features in predicting post-translational modifications in proteins, DNA methylation, and mRNA methylation, along with their functional implications. The book also discusses the prediction of protein-protein and protein-DNA interactions, protein structure, and function using computational methods. It also presents techniques for quantitative analysis of protein-DNA interactions and protein methylation and their involvement in gene regulation. Additionally, the use of nature-inspired algorithms to gain insights into gene regulatory mechanisms and metabolic pathways in human diseases is explored. This book acts as a useful reference for bioinformaticians and computational biologists working in the fields of molecular biology, genomics, and bioinformatics.Key Features:Reviews machine learning techniques for DNA sequence classification and protein structure predictionDiscusses genetic algorithms for analyzing multiple sequence alignments and predicting protein-protein interaction sitesExplores computational methods for quantitative analysis of protein-DNA interactionsExamine the role of nature-inspired algorithms in understanding the gene regulation and metabolic pathwaysCovers evolutionary algorithms and sequence-based features in predicting post-translational modifications. …

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Hardcover. Zustand: Brand New. 176 pages. 9.18x6.12x9.21 inches. In Stock.

Hardcover. Zustand: Brand New. 1st edition. 324 pages. 9.75x6.50x1.00 inches. In Stock.

Hardback. Zustand: New. This book provides an overview of basic and advanced computational techniques for analyzing and understanding protein, RNA, and DNA sequences. It covers effective computing techniques for DNA and protein classifications, evolutionary and sequence information analysis, evolutionary algorithms, and ensemble algorithms. Furthermore, the book reviews the role of machine learning techniques, artificial intelligence, ensemble learning, and sequence-based features in predicting post-translational modifications in proteins, DNA methylation, and mRNA methylation, along with their functional implications. The book also discusses the prediction of protein-protein and protein-DNA interactions, protein structure, and function using computational methods. It also presents techniques for quantitative analysis of protein-DNA interactions and protein methylation and their involvement in gene regulation. Additionally, the use of nature-inspired algorithms to gain insights into gene regulatory mechanisms and metabolic pathways in human diseases is explored. This book acts as a useful reference for bioinformaticians and computational biologists working in the fields of molecular biology, genomics, and bioinformatics.Key Features:Reviews machine learning techniques for DNA sequence classification and protein structure predictionDiscusses genetic algorithms for analyzing multiple sequence alignments and predicting protein-protein interaction sitesExplores computational methods for quantitative analysis of protein-DNA interactionsExamine the role of nature-inspired algorithms in understanding the gene regulation and metabolic pathwaysCovers evolutionary algorithms and sequence-based features in predicting post-translational modifications. …