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Handbook of Machine Learning Applications for Genomics
Sanjiban Sekhar Roy
(Illustrated by)
·
Y. -H Taguchi
(Illustrated by)
·
Springer
· Hardcover
Handbook of Machine Learning Applications for Genomics - Roy, Sanjiban Sekhar ; Taguchi, Y. -H
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Synopsis "Handbook of Machine Learning Applications for Genomics"
Local 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.
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All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.
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