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

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 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 models
• 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 genomics. 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.

學習概念、方法論和應用深度學習於從複雜基因組數據集中建立預測模型，以克服生命科學和生物技術行業中的挑戰。

主要特點：
- 應用深度學習算法解決基因組學領域的實際問題
- 從基因組數據集中建立的深度學習模型中提取生物學洞察
- 訓練、調整、評估、部署和監控深度學習模型，以實現基因組學的預測

書籍描述：
深度學習在基因組學領域展示了顯著的潛力，然而在這個領域缺乏熟練的深度學習人才。本書將幫助研究人員和數據科學家脫穎而出，通過開發必要的技能集來解決基因組學中的實際問題。從基本概念入手，本書突出了深度學習在處理基因組學中的大數據方面的優勢。首先，您將了解傳統的基因組學分析，然後過渡到最先進的基於機器學習的基因組學應用，最後深入探討基因組學的深度學習方法。本書涵蓋了研究界常用的所有重要深度學習算法，並詳細介紹了它們是什麼、如何工作以及在基因組學中的實際應用。本書專門介紹了操作化深度學習模型的一整個部分，為研究人員和任何深度學習從業人員提供了必要的實踐教程，以從基因組學的大數據集中構建、調整、解釋、部署、評估和監控深度學習模型。

通過閱讀本書，您將了解深度學習在基因組學中的挑戰、最佳實踐和陷阱。

您將學到什麼：
- 發現基因組學的機器學習應用
- 探索基因組學應用的深度學習概念和方法論
- 理解基因組學應用的監督式深度學習算法
- 通過自編碼器掌握非監督式深度學習
- 使用生成模型改進深度學習模型
- 從基因組數據集中操作化深度學習模型
- 可視化和解釋深度學習模型
- 理解深度學習在基因組學中的挑戰、陷阱和最佳實踐

本書適合機器學習工程師、數據科學家和在基因組學領域從事學術研究的人士。假設讀者具有中級的Python編程知識，基本的Python庫知識，如NumPy和Pandas用於操作和解析數據，Matplotlib和Seaborn用於可視化數據，以及基因組學和基因組分析概念的基礎知識。