Objectives
Identify genes: Deep learning models can be trained to identify genes in DNA sequences. This information can be used to develop new diagnostic tests and treatments.
Predict gene function: Deep learning models can be used to predict the function of genes by comparing their sequences to the sequences of genes with known functions. This information can be used to understand how genes work and to develop new drugs and therapies.
Detect genetic variants: Deep learning models can be used to detect genetic variants in DNA sequences. Genetic variants can be associated with diseases, traits, and responses to drugs. This information can be used to diagnose diseases, predict disease risk, and personalize treatments.
Analyze DNA methylation data: DNA methylation is a chemical modification that can affect gene expression. Deep learning models can be used to analyze DNA methylation data to identify patterns that are associated with diseases and other conditions.
Interpret single-cell sequencing data: Single-cell sequencing is a technique that allows researchers to sequence the DNA of individual cells. Deep learning models can be used to interpret single-cell sequencing data to identify different cell types and to understand how gene expression varies between cells.