Handbook of Machine Learning Applications for Genomics (2022)Hardcover - 2022, 24 June 2022

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.