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Handbook of Machine Learning Applications for Genomics Studies in Big Data 103
by Handbook of Machine Learning Applications for Genomics (2022)
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Description
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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Product Details
- Springer Brand
- Jun 24, 2022 Pub Date:
- 9789811691577 ISBN-13:
- 9811691576 ISBN-10:
- 228.0 pages Hardcover
- English Language
- 9.21 in * 0.56 in * 6.14 in Dimensions:
- 1 lb Weight:
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