| 书目名称 | Handbook of Machine Learning Applications for Genomics | | 编辑 | Sanjiban Sekhar Roy,Y.-H. Taguchi | | 视频video | | | 概述 | Presents comprehensive coverage of machine learning methods in the classification of genomics data.Addresses the effect of imbalance in the machine learning classifications of genomics data.Provides a | | 丛书名称 | Studies in Big Data | | 图书封面 |  | | 描述 | Currently, machine learning is playing a pivotal role in the progress of genomics. The applications of machine learning are helping all to understand the emerging trends and the future scope of genomics. This book provides comprehensive coverage of machine learning applications such as DNN, CNN, and RNN, for predicting the sequence of DNA and RNA binding proteins, expression of the gene, and splicing control. In addition, the book addresses the effect of multiomics data analysis of cancers using tensor decomposition, machine learning techniques for protein engineering, CNN applications on genomics, challenges of long noncoding RNAs in human disease diagnosis, and how machine learning can be used as a tool to shape the future of medicine. More importantly, it gives a comparative analysis and validates the outcomes of machine learning methods on genomic data to the functional laboratory tests or by formal clinical assessment. The topics of this book will cater interest to academicians, practitioners working in the field of functional genomics, and machine learning. Also, this book shall guide comprehensively the graduate, postgraduates, and Ph.D. scholars working in these fields.. | | 出版日期 | Book 2022 | | 关键词 | Deep Learning; Genomics; Medical Diagnosis; Machine Learning; Convolutional Neural Network; Gene predicti | | 版次 | 1 | | doi | https://doi.org/10.1007/978-981-16-9158-4 | | isbn_softcover | 978-981-16-9160-7 | | isbn_ebook | 978-981-16-9158-4Series ISSN 2197-6503 Series E-ISSN 2197-6511 | | issn_series | 2197-6503 | | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor |
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发表于 2025-3-21 19:35:08
