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This contributed volume explores the intersection of big data analytics and genomics, driven by advancements in high-throughput sequencing technologies that have led to unprecedented genomic data accumulation. Traditional data analysis methods are often insufficient for extracting novel insights from this vast data, necessitating the development of big data analytics tailored for genomics. The computational methods discussed aim to address key biological questions and are suitable for both newcomers and experienced professionals in the field. Featuring thirteen peer-reviewed contributions from leading international experts across countries such as Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the USA, the volume focuses on three main areas: statistical analytics, computational analytics, and cancer genome analytics. Topics include statistical methods for integrative genomic analysis, computational techniques for protein function prediction, and machine learning perspectives in cancer big data mining. This self-contained resource is designed for graduate students, bioinformaticians, computational biologists, and researchers from diverse fields, including genomics, molecular genetics, data mining, biostatistics, biomedical science, and machine learning. It serves as an essential read for understanding the role of big data in genomics and encourages further research in this vital area.
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Big Data Analytics in Genomics, Ka-Chun Wong
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- 2018
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