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Machine learning and systems biology in genomics and health

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning and systems biology in genomics and healthedited by Shailza Singh.
其他作者:	Singh, Shailza.
出版者:	Singapore :Springer Singapore :2022.
面頁冊數:	vii, 236 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	GenomicsData processing.
電子資源:	https://doi.org/10.1007/978-981-16-5993-5
ISBN:	9789811659935$q(electronic bk.)

Machine learning and systems biology in genomics and health
Machine learning and systems biology in genomics and health[electronic resource] /edited by Shailza Singh. - Singapore :Springer Singapore :2022. - vii, 236 p. :ill., digital ;24 cm.

Chapter 1: Construction of feedforward multilayer perceptron model for diagnosing leishmaniasis using transcriptome datasets and cognitive computing -- Chapter2- Big data in drug discovery -- Chapter3 - An overview of databases and tools for lncRNA genomics advancing precision medicine -- Chapter 4-Machine Learning in Genomics -- Chapter 5-How Machine Learning has revolutionized the field of Cancer Informatics? -- Chapter 6- Connecting the dots: Using machine learning to Forge Gene Regulatory Networks from large biological datasets -- Chapter 7-Identification of novel Non-coding RNAs in Plants by Big data analysis -- Chapter 8-Artificial Intelligence in Biomedical Image Processing -- Chapter 9- Artificial Intelligence and its Application in Cardiovascular Disease Management.

This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.

ISBN: 9789811659935$q(electronic bk.)

Standard No.: 10.1007/978-981-16-5993-5doiSubjects--Topical Terms:

196486
Genomics
--Data processing.

LC Class. No.: QH447 / .M33 2022

Dewey Class. No.: 572.86028

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505 0 $a Chapter 1: Construction of feedforward multilayer perceptron model for diagnosing leishmaniasis using transcriptome datasets and cognitive computing -- Chapter2- Big data in drug discovery -- Chapter3 - An overview of databases and tools for lncRNA genomics advancing precision medicine -- Chapter 4-Machine Learning in Genomics -- Chapter 5-How Machine Learning has revolutionized the field of Cancer Informatics? -- Chapter 6- Connecting the dots: Using machine learning to Forge Gene Regulatory Networks from large biological datasets -- Chapter 7-Identification of novel Non-coding RNAs in Plants by Big data analysis -- Chapter 8-Artificial Intelligence in Biomedical Image Processing -- Chapter 9- Artificial Intelligence and its Application in Cardiovascular Disease Management.
520 $a This book discusses the application of machine learning in genomics. Machine Learning offers ample opportunities for Big Data to be assimilated and comprehended effectively using different frameworks. Stratification, diagnosis, classification and survival predictions encompass the different health care regimes representing unique challenges for data pre-processing, model training, refinement of the systems with clinical implications. The book discusses different models for in-depth analysis of different conditions. Machine Learning techniques have revolutionized genomic analysis. Different chapters of the book describe the role of Artificial Intelligence in clinical and genomic diagnostics. It discusses how systems biology is exploited in identifying the genetic markers for drug discovery and disease identification. Myriad number of diseases whether be infectious, metabolic, cancer can be dealt in effectively which combines the different omics data for precision medicine. Major breakthroughs in the field would help reflect more new innovations which are at their pinnacle stage. This book is useful for researchers in the fields of genomics, genetics, computational biology and bioinformatics.
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