rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Deep Learning for Genomics
    sub title: Data-Driven Approaches for Genomics Applications in Life Sciences and Biotechnology
  name:
    name part: Devisetty, Upendra Kumar.
    role:
      role term: creator
  type of resource: text
  origin info:
    place:
      place term: xx
    place:
      place term: S.l.]
    publisher: PACKT PUBLISHING LIMITED
    date issued: 2022
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource
  abstract: Learn concepts, methodologies, and applications of deep learning for building predictive models from complex genomics data sets to overcome challenges in the life sciences and biotechnology industries Key Features Apply deep learning algorithms to solve real-world problems in the field of genomics Extract biological insights from deep learning models built from genomic datasets Train, tune, evaluate, deploy, and monitor deep learning models for enabling predictions in genomics Book Description Deep learning has shown remarkable promise in the field of genomics; however, there is a lack of a skilled deep learning workforce in this discipline. This book will help researchers and data scientists to stand out from the rest of the crowd and solve real-world problems in genomics by developing the necessary skill set. Starting with an introduction to the essential concepts, this book highlights the power of deep learning in handling big data in genomics. First, you'll learn about conventional genomics analysis, then transition to state-of-the-art machine learning-based genomics applications, and finally dive into deep learning approaches for genomics. The book covers all of the important deep learning algorithms commonly used by the research community and goes into the details of what they are, how they work, and their practical applications in genomics. The book dedicates an entire section to operationalizing deep learning models, which will provide the necessary hands-on tutorials for researchers and any deep learning practitioners to build, tune, interpret, deploy, evaluate, and monitor deep learning models from genomics big data sets. By the end of this book, you'll have learned about the challenges, best practices, and pitfalls of deep learning for genomics. What you will learn Discover the machine learning applications for genomics Explore deep learning concepts and methodologies for genomics applications Understand supervised deep learning algorithms for genomics ap…
  subject:
    topic: Genomics
    topic: Statistical methods
  subject:
    topic: Genomics
    topic: Data processing
  subject:
    topic: Genomics
    topic: Data processing
  subject:
    topic: Genomics
    topic: Statistical methods
  classification: QH438.4.S73
  classification: 572.8/60727
  related item:
    identifier: (OCoLC)1348140226
  identifier: 9781804613016
  identifier: 1804613010
  identifier: 1804615447
  identifier: 9781804615447
  identifier: 9781804615447 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: YDX
    record creation date: 221112
    record change date: 20260130162102.7
    record identifier: in00004672949
    language of cataloging:
      language term: eng