Pysster: classification of biological sequences by learning sequence and structure motifs with convolutional neural networks.

ABSTRACT:

Summary

Convolutional neural networks (CNNs) have been shown to perform exceptionally well in a variety of tasks, including biological sequence classification. Available implementations, however, are usually optimized for a particular task and difficult to reuse. To enable researchers to utilize these networks more easily, we implemented pysster, a Python package for training CNNs on biological sequence data. Sequences are classified by learning sequence and structure motifs and the package offers an automated hyper-parameter optimization procedure and options to visualize learned motifs along with information about their positional and class enrichment. The package runs seamlessly on CPU and GPU and provides a simple interface to train and evaluate a network with a handful lines o

SUBMITTER: Budach S 

PROVIDER: S-EPMC6129303 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature