Machine Learning Based Methods For Rna Data Analysis Volume Ii
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Machine Learning Based Methods For Rna Data Analysis Volume Ii
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Author : Lihong Peng
language : en
Publisher: Frontiers Media SA
Release Date : 2023-01-02
Machine Learning Based Methods For Rna Data Analysis Volume Ii written by Lihong Peng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-02 with Science categories.
Machine Learning Based Methods For Rna Data Analysis Volume Iii
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Author : Lihong Peng
language : en
Publisher: Frontiers Media SA
Release Date : 2023-02-17
Machine Learning Based Methods For Rna Data Analysis Volume Iii written by Lihong Peng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-02-17 with Science categories.
Machine Learning Based Methods For Rna Data Analysis
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Author : Lihong Peng
language : en
Publisher: Frontiers Media SA
Release Date : 2022-06-16
Machine Learning Based Methods For Rna Data Analysis written by Lihong Peng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-16 with Science categories.
Gene Expression Data Analysis
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Author : Pankaj Barah
language : en
Publisher: CRC Press
Release Date : 2021-11-08
Gene Expression Data Analysis written by Pankaj Barah and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-08 with Computers categories.
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 simultaneously monitoring the expression patterns of thousands of genes. Data produced from such experiments are voluminous (both in dimensionality and numbers of instances) and evolving in nature. Analysis of huge amounts of data toward 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 with a multidisciplinary 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 and practical knowledge of methods for identifying 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 multidisciplinary researchers and practitioners in computer science and the biological sciences
Computational Methods For Microbiome Analysis Volume 2
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Author : Setubal
language : en
Publisher: Frontiers Media SA
Release Date : 2023-01-04
Computational Methods For Microbiome Analysis Volume 2 written by Setubal and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-04 with Science categories.
Machine Learning Techniques On Gene Function Prediction Volume Ii
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Author : Quan Zou
language : en
Publisher: Frontiers Media SA
Release Date : 2023-04-11
Machine Learning Techniques On Gene Function Prediction Volume Ii written by Quan Zou and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-04-11 with Science categories.
System Biology Methods And Tools For Integrating Omics Data Volume Ii
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Author : Liang Cheng
language : en
Publisher: Frontiers Media SA
Release Date : 2022-09-07
System Biology Methods And Tools For Integrating Omics Data Volume Ii written by Liang Cheng and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-07 with Science categories.
Microbiome And Machine Learning Volume Ii
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Author : Erik Bongcam-Rudloff
language : en
Publisher: Frontiers Media SA
Release Date : 2024-10-24
Microbiome And Machine Learning Volume Ii written by Erik Bongcam-Rudloff and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-24 with Science categories.
Due to the success of Microbiome and Machine Learning, which collected research results and perspectives of researchers working in the field of machine learning (ML) applied to the analysis of microbiome data, we are launching the second volume to collate any new findings in the field to further our understanding and encourage the participation of experts worldwide in the discussion. The success of ML algorithms in the field is substantially due to their capacity to process high-dimensional data and deal with uncertainty and noise. However, to maximize the combinatory potential of these emerging fields (microbiome and ML), researchers have to deal with some aspects that are complex and inherently related to microbiome data. Microbiome data are convoluted, noisy and highly variable, and non-standard analytical methodologies are required to unlock their clinical and scientific potential. Therefore, although a wide range of statistical modelling and ML methods are available, their application is only sometimes optimal when dealing with microbiome data.
Computational Epigenetics In Human Diseases Cell Differentiation And Cell Reprogramming Volume Ii
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Author : Yongchun Zuo
language : en
Publisher: Frontiers Media SA
Release Date : 2022-03-31
Computational Epigenetics In Human Diseases Cell Differentiation And Cell Reprogramming Volume Ii written by Yongchun Zuo and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-31 with Science categories.
Deep Learning In Genetics And Genomics
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Author : Khalid Raza
language : en
Publisher: Elsevier
Release Date : 2024-11-28
Deep Learning In Genetics And Genomics written by Khalid Raza and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-28 with Science categories.
Deep Learning in Genetics and Genomics: Vol. 2 (Advanced Applications) delves into the Deep Learning methods and their applications in various fields of studies, including genetics and genomics, bioinformatics, health informatics and medical informatics generating the momentum of today's developments in the field. In 25 chapters this title covers advanced applications in the field which includes deep learning in predictive medicines), analysis of genetic and clinical features, transcriptomics and gene expression patterns analysis, clinical decision support in genetic diagnostics, deep learning in personalised genomics and gene editing, and understanding genetic discoveries through Explainable AI. Further, it also covers various deep learning-based case studies, making this book a unique resource for wider, deeper, and in-depth coverage of recent advancement in deep learning based approaches. This volume is not only a valuable resource for health educators, clinicians, and healthcare professionals but also to graduate students of genetics, genomics, biology, biostatistics, biomedical sciences, bioinformatics, and interdisciplinary sciences. - Embraces the potential that deep learning holds for understanding genome biology - Encourages further advances in this area, extending to all aspects of genomics research - Provides Deep Learning algorithms in genetic and genomic research