{"product_id":"gene-expression-data-analysis-a-statistical-and-machine-learning-perspective-9780367338893","title":"Gene Expression Data Analysis: A Statistical and Machine Learning Perspective","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThe development of high-throughput technologies in molecular biology has led to the production of vast amounts of data. This book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives, providing theoretical and practical knowledge of methods for identifying novel patterns of high biological significance. It also discusses benchmark algorithms, tools, systems, and repositories commonly used in analyzing gene expression data and validating results. Suitable for multidisciplinary researchers and practitioners in computer science and biological sciences. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 360 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 22 November 2021\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003eThe development of high-throughput technologies in molecular biology has led to the generation of vast amounts of data, particularly through microarray and RNA sequencing. These technologies enable simultaneous monitoring of the expression patterns of thousands of genes, generating data that is both voluminous and evolving in nature.\u003cbr\u003e\u003cbr\u003eAnalyzing such large datasets requires high-performance computational infrastructure and efficient machine learning algorithms to identify interesting patterns that are relevant to a given biological question. However, cross-communication between biologists and computer scientists remains a significant challenge.\u003cbr\u003e\u003cbr\u003eGene Expression Data Analysis: A Statistical and Machine Learning Perspective aims to address this gap by providing a comprehensive and interdisciplinary approach to gene expression data analysis. The book discusses gene expression data analysis from molecular biology, machine learning, and statistical perspectives, providing readers with both theoretical and practical knowledge of methods for identifying novel patterns of high biological significance.\u003cbr\u003e\u003cbr\u003eTo measure the effectiveness of these algorithms, the book discusses statistical and biological performance metrics that can be used in real-life or simulated environments. It also provides a comprehensive list of benchmark algorithms, tools, systems, and repositories commonly used in analyzing gene expression data and validating results.\u003cbr\u003e\u003cbr\u003eThe book is designed for a multidisciplinary audience, including students, researchers, and practitioners in biology, medicine, and computer science. It will enable them to acquire in-depth knowledge in statistical and machine-learning-based methods for analyzing gene expression data, which is crucial in understanding biological processes and developing new treatments.\u003cbr\u003e\u003cbr\u003eSome key features of the book include:\u003cbr\u003e\u003cbr\u003eAn interdisciplinary approach: The book brings together experts from different fields, including molecular biology, statistics, and machine learning, to provide a comprehensive perspective on gene expression data analysis.\u003cbr\u003e\u003cbr\u003eComprehensive coverage: The book covers a wide range of topics, including data preprocessing, feature selection, classification, and regression, using both statistical and machine learning methods.\u003cbr\u003e\u003cbr\u003eReal-world examples: The book includes case studies and real-world datasets to demonstrate the practical application of the discussed methods and algorithms.\u003cbr\u003e\u003cbr\u003eBenchmark algorithms: The book provides a comprehensive list of benchmark algorithms, tools, systems, and repositories that are commonly used in analyzing gene expression data.\u003cbr\u003e\u003cbr\u003eAccessible language: The book is written in an accessible and engaging style, making it suitable for students and practitioners with a limited background in statistics or machine learning.\u003cbr\u003e\u003cbr\u003eOverall, Gene Expression Data Analysis: A Statistical and Machine Learning Perspective is a valuable resource for anyone interested in analyzing gene expression data and gaining insights into biological processes. It will serve as a valuable reference for students, researchers, and practitioners in biology, medicine, and computer science.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 857g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 234 x 156 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9780367338893\u003c\/p\u003e","brand":"PankajBarah,Dhruba KumarBhattacharyya,Jugal KumarKalita","offers":[{"title":"Hardback","offer_id":44104489074938,"sku":"9780367338893","price":142.8,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1646170547208_book.jpg?v=1646919428","url":"https:\/\/shulphink.com\/products\/gene-expression-data-analysis-a-statistical-and-machine-learning-perspective-9780367338893","provider":"Shulph Ink","version":"1.0","type":"link"}