Data Mining

New PDF release: Big Data Analytics in Genomics

By Ka-Chun Wong

ISBN-10: 3319412787

ISBN-13: 9783319412788

This contributed quantity explores the rising intersection among giant info analytics and genomics. fresh sequencing applied sciences have enabled high-throughput sequencing facts new release for genomics leading to a number of foreign initiatives that have resulted in immense genomic information accumulation at an unheard of speed.  To demonstrate novel genomic insights from this knowledge inside of a cheap time-frame, conventional facts research tools will not be enough or scalable, forcing the necessity for large information analytics to be built for genomics. The computational tools addressed within the booklet are meant to take on an important organic questions utilizing enormous information, and are applicable for both newbies or veterans within the field.
This quantity bargains 13 peer-reviewed contributions, written through foreign major specialists from various areas, representing Argentina, Brazil, China, France, Germany, Hong Kong, India, Japan, Spain, and the us.  In specific, the booklet surveys 3 major parts: statistical analytics, computational analytics, and melanoma genome analytics. pattern issues coated contain: statistical tools for integrative research of genomic facts, computation tools for protein functionality prediction, and views on computer studying thoughts in enormous info mining of melanoma. Self-contained and compatible for graduate scholars, this e-book is usually designed for bioinformaticians, computational biologists, and researchers in groups starting from genomics, significant facts, molecular genetics, facts mining, biostatistics, biomedical technology, melanoma examine, scientific study, and biology to laptop studying and desktop technological know-how.  Readers will locate this quantity to be an important learn for appreciating the position of huge info in genomics, making this a useful source for exciting additional examine at the topic.

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Big Data Analytics in Genomics by Ka-Chun Wong


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