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Titlebook: Handbook of Machine Learning Applications for Genomics; Sanjiban Sekhar Roy,Y.-H. Taguchi Book 2022 The Editor(s) (if applicable) and The

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發(fā)表于 2025-3-21 19:35:08 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Handbook of Machine Learning Applications for Genomics
編輯Sanjiban Sekhar Roy,Y.-H. Taguchi
視頻videohttp://file.papertrans.cn/422/421577/421577.mp4
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
叢書名稱Studies in Big Data
圖書封面Titlebook: Handbook of Machine Learning Applications for Genomics;  Sanjiban Sekhar Roy,Y.-H. Taguchi Book 2022 The Editor(s) (if applicable) and The
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
關(guān)鍵詞Deep Learning; Genomics; Medical Diagnosis; Machine Learning; Convolutional Neural Network; Gene predicti
版次1
doihttps://doi.org/10.1007/978-981-16-9158-4
isbn_softcover978-981-16-9160-7
isbn_ebook978-981-16-9158-4Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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Marenglen Biba,Narasimha Rao Vajjhalare not specifically designed for optimal gas treatment. Slight variations where the reactor is specifically designed for gas treatment and is not co-degrading waste water contaminants have been termed . (Bielefeldt and Stensel, 1998), . (Neal and Loehr, 2000), and . (Andrews and Noah, 1995). More sp
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Vinamra Khoria,Amit Kumar,Sanjiban Shekhar Royethylene) or are only aerobically degraded by co-metabolism (e.g., trichloroethylene). Some compounds are in principle biodegradable, but their elimination in biofilters needs long start up periods. It has been reported that elimination of certain organo-sulphur compounds only started 5 months after
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Manojit Bhattacharya,Ashish Ranjan Sharma,Chiranjib Chakrabortyase of up to 4.2 over a period of 5 days compared to a maximum 1.5–2-fold increase in the adherent static culture over a 1 week period. In the bioreactor culture, these cells maintained self-renewal, karyotype stability, and cloning efficiency capabilities. This approach can be also used for human n
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