ChromDL: a next-generation regulatory DNA classifier.

ABSTRACT:

Motivation

Predicting the regulatory function of non-coding DNA using only the DNA sequence continues to be a major challenge in genomics. With the advent of improved optimization algorithms, faster GPU speeds, and more intricate machine-learning libraries, hybrid convolutional and recurrent neural network architectures can be constructed and applied to extract crucial information from non-coding DNA.

Results

Using a comparative analysis of the performance of thousands of Deep Learning architectures, we developed ChromDL, a neural network architecture combining bidirectional gated recurrent units, convolutional neural networks, and bidirectional long short-term memory units, which significantly improves upon a range of prediction metrics compared to its predecessors in trans

SUBMITTER: Hill C 

PROVIDER: S-EPMC10311331 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature