Table 3.

Deep Learning in SNV and Indel Detection

Model Type	Appliance	Study
Convolutional Neural Network (CNN)	For automated extraction of local features from genetic data, detection of SNVs and Indels	Improved accuracy and recall of variant detection109
Randomized neural networks (RNNs) and LSTMs	For capturing temporal dependence in gene expression, especially in low-frequency mutation detection110,111	Excellent performance in low-frequency mutation detection
Variable Auto-Encoder (VAE)	Detection of cancer-associated SNVs and Indels by generating patterns of potential variants	Successful identification of multiple mutations associated with cancer111