Gene Expression Data Analysis A Statistical and Machine Learning Perspective
Gene Expression Data Analysis A Statistical and Machine Learning Perspective by Pankaj Barah, published by Chapman & Hall/CRC Press in 2022, offers a comprehensive examination of the substantial growth in data resulting from genome sequencing projects and other experimental technologies. This 360-page book presents essential information regarding various sources of gene expression data, alongside methods for pre-processing, analysis, and validation.
Readers will find a detailed exploration of the statistical and machine learning techniques applicable to gene expression data analysis. The book addresses the challenges posed by the increasing volume of data and emphasizes the importance of effective data management and interpretation. With a focus on relevant subjects such as statistics and computer science, this edition serves as a valuable resource for those interested in the intersection of data analysis and biological research.
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The book introduces phenomenal growth of data generated by increasing numbers of genome sequencing projects and other throughput technology-led experimental efforts. It provides information about various sources of gene expression data, and pre-processing, analysis, and validation of such data.
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