FastISM: performant in silico saturation mutagenesis for convolutional neural networks.

ABSTRACT:

Motivation

Deep-learning models, such as convolutional neural networks, are able to accurately map biological sequences to associated functional readouts and properties by learning predictive de novo representations. In silico saturation mutagenesis (ISM) is a popular feature attribution technique for inferring contributions of all characters in an input sequence to the model's predicted output. The main drawback of ISM is its runtime, as it involves multiple forward propagations of all possible mutations of each character in the input sequence through the trained model to predict the effects on the output.

Results

We present fastISM, an algorithm that speeds up ISM by a factor of over 10× for commonly used convolutional neural network architectures. fastISM is based on the

SUBMITTER: Nair S 

PROVIDER: S-EPMC9048647 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature