Autoencoders for genomics
Several applications of autoencoders for genomics exist. The most common type of application, however, is for predicting gene expression from microarray and RNA-Seq datasets. Let’s understand how autoencoders work for gene expression analysis.
Gene expression
The main application of autoencoders, as you learned in the previous section, is for gene expression analysis, which includes
- Time-series gene expression where they are mainly used at the preprocessing step for clustering, cDNA microarrays
- RNA-Seq, where they are used to predict the organization of transcriptomics machinery
- Gene expression, where they are mainly used for identification of biological signals and patterns respectively
In a typical gene expression experiment, the inputs are typically numerical values estimating how much RNA is produced from a DNA template through transcription across various cells, tissues, or conditions. Let’s look at some popular...
