Machine Learning and Systems Biology in Genomics and Health

Buch | Softcover

236 Seiten

2023 | 1st ed. 2022
Springer Verlag, Singapore
978-981-16-5995-9 (ISBN)

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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.

Dr. Shailza Singh is Scientist-E and Incharge of Bioinformatics and High Performance Computing Facility, National Centre for Cell Science, Pune, India Her research chiefly focuses on systems and synthetic biology. She also specializes in infectious diseases such as leishmaniasis. Her research group is working to integrate the action of regulatory circuits, cross-talk between pathways, and non-linear kinetics of biochemical processes through mathematical modeling. Dr. Singh has been honored with the DBT-RGYI, DST Young Scientist and INSA Bilateral Exchange Programme awards, and was selected by the DBT for a SAKURA EXCHANGE Programme in Science in the field of Artificial Intelligence and Machine learning to Tokyo in 2018. She serves as a reviewer for prestigious international grants such as the Research Councils UK; for national grants from the DBT, DST and CSIR; and for several prominent international journals, e.g. Parasite and Vectors, PLOS One, BMC Infectious Disease, BMC Research Notes, Oncotarget, and the International Journal of Cancer.

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.

Erscheinungsdatum	03.04.2023
Zusatzinfo	1 Illustrations, black and white; VII, 236 p. 1 illus.
Verlagsort	Singapore
Sprache	englisch
Maße	155 x 235 mm
Themenwelt	Medizin / Pharmazie ► Medizinische Fachgebiete ► Onkologie
Themenwelt	Naturwissenschaften ► Biologie ► Genetik / Molekularbiologie
Schlagworte	big data genomics • Computational Biology • Deep learning • genomic analysis • random forest • systems biology
ISBN-10	981-16-5995-8 / 9811659958
ISBN-13	978-981-16-5995-9 / 9789811659959
Zustand	Neuware