Gene Expression Data Analysis: A Statistical and Machine Learning Perspective
暫譯: 基因表達數據分析：統計與機器學習的視角
Barah, Pankaj, Bhattacharyya, Dhruba Kumar, Kalita, Jugal Kumar
- 出版商: CRC
- 出版日期: 2021-11-22
- 售價: $6,500
- 貴賓價: 9.5 折 $6,175
- 語言: 英文
- 頁數: 392
- 裝訂: Hardcover - also called cloth, retail trade, or trade
- ISBN: 0367338890
- ISBN-13: 9780367338893
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相關分類:
Data Science、Machine Learning
海外代購書籍(需單獨結帳)
商品描述
Development of high throughput technologies in molecular biology during the last two decades has contributed to the production of tremendous amounts of data. Microarray and RNA-sequencing are two such widely used high throughput technologies for monitoring the expression patterns of thousands of genes simultaneously. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data towards the identification of interesting patterns that are relevant for a given biological question requires high performance computational infrastructure as well as efficient machine learning algorithms. Cross-communication of ideas between biologists and computer scientists remains a big challenge.
Gene Expression Data Analysis: A Statistical and Machine Learning Perspective has been written keeping a multi-disciplinary audience in mind. The book discusses gene expression data analysis from molecular biology, machine learning and statistical perspectives. Readers will be able to acquire both theoretical as well as practical knowledge of methods for identification of novel patterns of high biological significance. To measure the effectiveness of such algorithms, we discuss statistical and biological performance metrics that can be used in real life or in a simulated environment. This book discusses a large number of benchmark algorithms, tools, systems and repositories that are commonly used in analyzing gene expression data and validating results.This book will benefit students, researchers and practitioners in biology, medicine, and computer science by enabling them to acquire in-depth knowledge in statistical and machine learning based methods for analyzing gene expression data.
Key features:
- An introduction to the Central Dogma of molecular biology and information flow in biological systems.
- A systematic overview of the methods for generating gene expression data.
- Background knowledge on statistical modeling and machine learning techniques.
- Detailed methodology of analyzing gene expression data with an example case study.
- Clustering methods for finding co-expression patterns from microarray, bulkRNA and scRNA data.
- A large number of practical tools, systems and repositories that are useful for computational biologists to create, analyze and validate biologically relevant gene expression patterns.
- Suitable for multi-disciplinary researchers and practitioners in computer science and biological sciences.
商品描述(中文翻譯)
在過去二十年中，分子生物學高通量技術的發展促進了大量數據的產生。微陣列（Microarray）和RNA測序（RNA-sequencing）是兩種廣泛使用的高通量技術，用於同時監測數千個基因的表達模式。這類實驗產生的數據量龐大（無論是在維度還是實例數量上）且具有演變性。分析大量數據以識別與特定生物學問題相關的有趣模式，需要高效能的計算基礎設施以及有效的機器學習算法。生物學家與計算機科學家之間的思想交流仍然是一個重大挑戰。
《基因表達數據分析：統計與機器學習的視角》這本書是針對多學科讀者而撰寫的。該書從分子生物學、機器學習和統計的角度討論基因表達數據分析。讀者將能夠獲得識別具有高度生物學意義的新模式的方法的理論和實踐知識。為了衡量這些算法的有效性，我們討論了可以在現實生活或模擬環境中使用的統計和生物學性能指標。本書討論了大量常用於分析基因表達數據和驗證結果的基準算法、工具、系統和資料庫。本書將使生物學、醫學和計算機科學的學生、研究人員和從業者獲得深入的統計和基於機器學習的方法知識，以分析基因表達數據。
主要特點：
- 介紹分子生物學的中心法則及生物系統中的信息流。
- 系統概述生成基因表達數據的方法。
- 統計建模和機器學習技術的背景知識。
- 詳細的基因表達數據分析方法學，並附有案例研究。
- 從微陣列、bulkRNA和scRNA數據中尋找共表達模式的聚類方法。
- 大量實用工具、系統和資料庫，對計算生物學家創建、分析和驗證生物相關的基因表達模式非常有用。
- 適合計算機科學和生物科學的多學科研究人員和從業者。
作者簡介
Pankaj Barah is an Assistant professor in Molecular Biology and Biotechnology at Tezpur University. He has received his M.Sc. degree in Bioinformatics (2006) from University of Madras in India and PhD in Computational Systems Biology (2013) from the Norwegian University of Science and Technology (NTNU), Trondheim, Norway. He has worked as Bioinformatics scientist in the division of Theoretical Bioinformatics at German Cancer Research Center (DKFZ) in Heidelberg, Germany during 2015-2017. His research areas include- computational systems biology, bioinformatics, evolutionary systems biology, Next Generation Sequencing (NGS), Big data analytics and biological networks. He has authored 20 research articles, edited two books and written 5 book chapters. He is recipient of Ramalingaswami Re-entry Fellowship from the Department of Biotechnology, Government of India. Dr. Barah is currently a member of the Indian National Young Academy of Sciences.
Dhruba Kumar Bhattacharyya is a professor in Computer Science and Engineering at Tezpur University. He teaches machine learning, network security, cryptography and computational biology in UG, PG and PhD classes at Tezpur University. Professor Bhattacharyya's research areas include machine learning, network security, and bioinformatics. He has published more than 280 research articles in leading international journals and peer-reviewed conference proceedings. Dr. Bhattacharyya has authored 5 technical reference books and edited 9 technical volumes. Under his guidance, twenty students have successfully completed Ph.D. in the areas of machine learning, bioinformatics and network security. He is PI of several major research grants, including the Centre of Excellence of Ministry of HRD of Government of India under FAST instituted at Tezpur University. Professor Bhattacharyya is a Fellow of IETE and IE, India. He is also a Senior Member of IEEE. More details about Dr Bhattacharyya can be found at http: //agnigarh.tezu.ernet.in/_dkb/index.html.
Jugal Kumar Kalita teaches computer science at the University of Colorado, Colorado Springs. He received M.S. and Ph.D. degrees in computer and information science from the University of Pennsylvania in Philadelphia in 1988 and 1990, respectively. Prior to that he had received an M.Sc. from the University of Saskatchewan in Saskatoon, Canada in 1984 and a B.Tech. from the Indian Institute of Technology, Kharagpur in 1982. His expertise is in the areas of artificial intelligence and machine learning, and the application of techniques in machine learning to network security, natural language processing and bioinformatics. He has published 130 papers in journals and refereed conferences. He is the author of a book on Perl titled "On Perl: Perl for Students and Professionals". He is also a coauthor of a book titled "Network Anomaly Detection: A Machine Learning Perspective" with Dr Dhruba K Bhattacharyya. He received the Chancellor's Award at the University of Colorado, Colorado Springs, in 2011, in recognition of lifelong excellence in teaching, research and service. More details about Dr. Kalita can be found at http: //www.cs.uccs.edu/_kalita.
作者簡介(中文翻譯)
Pankaj Barah 是泰茲普爾大學分子生物學與生物技術的助理教授。他於2006年在印度馬德拉斯大學獲得生物資訊學碩士學位，並於2013年在挪威科技大學（NTNU）獲得計算系統生物學博士學位。他曾在2015年至2017年間於德國海德堡的德國癌症研究中心（DKFZ）理論生物資訊學部門擔任生物資訊學科學家。他的研究領域包括計算系統生物學、生物資訊學、演化系統生物學、下一代測序（NGS）、大數據分析和生物網絡。他已發表20篇研究文章，編輯兩本書籍並撰寫5章書籍。他是印度政府生物技術部Ramalingaswami重返獎學金的獲得者。Barah博士目前是印度國家青年科學院的成員。
Dhruba Kumar Bhattacharyya 是泰茲普爾大學計算機科學與工程的教授。他在泰茲普爾大學的本科、研究生和博士班教授機器學習、網絡安全、密碼學和計算生物學。Bhattacharyya教授的研究領域包括機器學習、網絡安全和生物資訊學。他在國際知名期刊和同行評審的會議論文集中發表了超過280篇研究文章。Bhattacharyya博士已撰寫5本技術參考書籍並編輯9本技術卷。在他的指導下，二十名學生成功完成了機器學習、生物資訊學和網絡安全領域的博士學位。他是多個主要研究計畫的首席研究員，包括印度政府人力資源發展部在泰茲普爾大學設立的卓越中心。Bhattacharyya教授是印度電子與電氣工程師學會（IETE）和印度工程師學會（IE）的會士，也是IEEE的高級會員。關於Bhattacharyya博士的更多詳細資訊可以在 http://agnigarh.tezu.ernet.in/_dkb/index.html 找到。
Jugal Kumar Kalita 在科羅拉多大學科羅拉多斯普林斯分校教授計算機科學。他於1988年和1990年分別在賓夕法尼亞大學獲得計算機與資訊科學的碩士和博士學位。在此之前，他於1984年在加拿大薩斯喀徹溫省的薩斯喀徹溫大學獲得碩士學位，並於1982年在印度理工學院卡哈爾古爾獲得工程學士學位。他的專長領域包括人工智慧和機器學習，以及將機器學習技術應用於網絡安全、自然語言處理和生物資訊學。他在期刊和經過審核的會議上發表了130篇論文。他是一本名為《On Perl: Perl for Students and Professionals》的Perl書籍的作者，並與Dhruba K Bhattacharyya博士共同撰寫了名為《Network Anomaly Detection: A Machine Learning Perspective》的書籍。他於2011年獲得科羅拉多大學科羅拉多斯普林斯分校的校長獎，以表彰其在教學、研究和服務方面的終身卓越表現。關於Kalita博士的更多詳細資訊可以在 http://www.cs.uccs.edu/_kalita 找到。
