ARCHITECTURES FOR TRAINING NEURAL NETWORKS USING BIOLOGICAL SEQUENCES, CONSERVATION, AND MOLECULAR PHENOTYPES

摘要

The present disclosure provides methods and systems that can ascertain how genetic variants impact molecular phenotypes. Such methods and systems may use additional conservation information. In an aspect, the present disclosure provides a method for training a molecular phenotype neural network (MPNN), comprising: (a) providing a molecular phenotype neural network (MPNN) comprising one or more parameters; (b) providing a training data set comprising (i) a set of one or more inputs comprising biological sequences and (ii) for each input in the set of one or more inputs, a set of one or more molecular phenotypes corresponding to the input; (c) configuring the one or more parameters of the MPNN based on the training data set to minimize a total loss of the training data set, thereby training the MPNN; and (d) outputting the one or more parameters of the MPNN.