Handbook of machine learning applications for genomics

Handbook of machine learning applications for genomics / Sanjiban Sekhar Roy, Y.-H. Taguchi, editors.

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...

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Bibliographic Details
Other Authors: Roy, Sanjiban Sekhar (Editor), Taguchi, Y.-H (Editor)
Format: eBook
Language:English
Published: Singapore : Springer, [2022]
Series:Studies in big data ; v. 103.
Subjects:
Genomics > Data processing.
Machine learning.
Genomics > Data processing
Machine learning
Online Access:Click for online access
Table of Contents:
  • 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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