Deep learning and neural network for genetic analysis..

Deep learning architectures are artificial neural networks of multiple nonlinear layers 

they are divided into types based on input data characteristics and research objectives

Deep learning can learn complex features by combining simpler features learned from data

hierarchical representations of data can be discovered with increasing levels of abstraction

Deep learning architecture  can be of 4 types i.e.  deep neural networks,

convolutional neural networks, recurrent neural networks, emergent architectures

A deep convolutional neural network can call genetic variation in aligned read data by learning statistical relationships (likelihoods) between images of read pileups around putative variant sites and ground-truth genotype calls.