Deep Learning for Genomics: Data-driven approaches for genomics applications in life sciences and biotechnology

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Deep Learning for Genomics: Data-driven approaches for genomics applications in life sciences and biotechnology

  • Author:Upendra Kumar Devisetty
  • Length: 270 pages
  • Edition: 1
  • Publisher: Packt Publishing
  • Publication Date: 2022-11-11
  • ISBN-10: 1804615447
  • ISBN-13: 9781804615447
  • Sales Rank: #121274 (See Top 100 Books)
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    Book Description

    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 mod未分类 *els 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 res欧米伽earchers 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 methodologie宝珀s for genomics applica杰克宝tions
    • Understand supervised deep learning algorithms for genomics applications
    • Get to grips with unsupervised deep learning with autoencoders
    • Improve deep learning models using generative models
    • Operationalize deep learning models from genomics datasets
    • Visualize and interpret deep learning m赛格威电动车odels
    • Understand deep learning challenges, pitfalls, and best practices

    Who this book is for

    This deep learning book is for machine learning engineers, data scientists, and academicians practicing in the field of genomic尊皇s. It assumes that readers have intermediate Python programming knowledge, basic knowledge of Python libraries such as NumPy and Pandas to manipulate and parse data, Matplotlib, and Seaborn for visualizing data, along with a base in genomics and genomic analysis concepts.

    中文:

    书名:基因组学的深度学习: 生命科学和生物技术中基因组学应用的数据驱动方法

    学习深度学习的概念,方法和应用,以从复杂的基因组学数据集建立预测模型,以克服生命科学和生物技术行业的挑战

    主要功能

    • 应用深度学习算法解决基因组学领域的现实问题
    • 从基因组数据集构建的深度学习模型中提取生物见解
    • 训练、调整、评估、部署和监控深度学习模型,实现基因组预测

    图书描述

    深度学习在基因组学领域显示出非凡的前景; 但是,该学科缺乏熟练的深度学习队伍。这本书将帮助研究人员和数据科学家从其他人群中脱颖而出,并通过开发必要的技能来解决基因组学中的现实宇路表问题。本书首先介绍了基本概念,重点介绍了深度学习在处理基因组学中的大数据方面的力量。首先,您将了解传统的基因组学分析,然后过渡到最先进的基于机器学习的基因组学应用程序,最后深入到基因组学的深度学习方法。本书涵盖了研究界常用的所有重要深度学习算法,并详细介绍了它们是什么,它们如何工作以及它们在基因组学中的实际应用。本书将整个部分专门用于操作深度学习模型,这将为研究人员和任何深度学习从业者提供必要的动手教程,以构建,调整,解释,部署,评估和监视来自基因组学大数据集的深度学习模型。

    到本书结束时,您将了解到基因组学深度学习的挑战,最佳实践和陷阱。

    什么你会学到

    • 探索机器学习基因组学应用
    • 探索深度学习概念和基因组学应用方法
    • 理解基因组学应用的监督深度学习算法
    • 掌握无监督深度学习与自动编码器
    • 使用生成模型改进深度学习模型
    • 从基因组数据集中实现深度学习模型
    • 深度学习模型可视化解读
    • 理解深度学习的挑战、陷阱和最佳实践

    这本书是为谁准备的

    这本深范思哲 (2)度学习书是为机器学习工程师,数据科学家和在基因组学领域实践的院士准备的。它假设读者具有中间的Python编程知识,Python库的基本知识,例如NumPy和Pandas来操纵和解析数据,Matplotlib和用于可视化数据的Seaborn,以及基因组学和基因组分析概念的基础。

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