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008			220730s2022 si | s |||| 0|eng d
020			_a9789811691584
024	7		_a10.1007/978-981-16-9158-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQH441.2 _b2022 EB
245	0	0	_aHandbook of Machine Learning Applications for Genomics _cedited by Sanjiban Sekhar Roy, Y.-H. Taguchi
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (X, 218 páginas) _b75 ilustraciones, 60 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aStudies in Big Data _x2197-6511 _v103
505	0		_aLocal and global characterization of genomic data -- DNA sequencing using RNN -- Deep learning to study functional activities of DNA sequence -- Autoencoders for gene clastering -- Dimension reduction in gene expression using deep learning -- To predict DNA methylation states using deep learning -- Transfer learning in genomics -- CNN model to analyze gene expression images -- Gene expression Prediction using advanced machine learning -- Predicting splicing regulation using deep learning -- Transcription factor binding site prediction using deep learning -- Deep learning for prediction of structural classification of proteins -- Prediction of secondary strucure of RNA using advanced machine learning and deep learning -- Deep learning for pepositioning of drug and pharmacogenomics.
520			_aCurrently, 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.
988			_aSpringer_Robotics_2022
650		7	_2embne _9164326 _aGenómica
650		7	_2embne _9166090 _aAprendizaje automático
700	1		_aRoy, Sanjiban Sekhar _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt
700			_aTaguchi, Y-h _eeditor literario _4edt _4http://id.loc.gov/vocabulary/relators/edt _9671606
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9789811691577
776	0	8	_iPrinted edition: _z9789811691591
776	0	8	_iPrinted edition: _z9789811691607
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-16-9158-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b07/2022 _dz _eIG _zSI