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Machine learning and systems biology in genomics and health / Shailza Singh, editor.
Singh, Shailza, editor.
2022
QH447 .M33 2022
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Title
Machine learning and systems biology in genomics and health / Shailza Singh, editor.
ISBN
9789811659935 (electronic bk.)
9811659931 (electronic bk.)
9789811659928
9811659923
DOI
https://doi.org/10.1007/978-981-16-5993-5
Published
Singapore : Springer, [2022]
Copyright
©2022
Language
English
Description
1 online resource
Item Number
10.1007/978-981-16-5993-5 doi
Call Number
QH447 .M33 2022
Dewey Decimal Classification
572.8/6028
Summary
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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Access limited to authorized users.
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text file PDF
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed February 9, 2022).
Added Author
Singh, Shailza, editor.
Available in Other Form
Print version: 9789811659928
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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.

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