Supervised promoter recognition: a benchmark framework.

ABSTRACT:

Motivation

Deep learning has become a prevalent method in identifying genomic regulatory sequences such as promoters. In a number of recent papers, the performance of deep learning models has continually been reported as an improvement over alternatives for sequence-based promoter recognition. However, the performance improvements in these models do not account for the different datasets that models are evaluated on. The lack of a consensus dataset and procedure for benchmarking purposes has made the comparison of each model's true performance difficult to assess.

Results

We present a framework called Supervised Promoter Recognition Framework ('SUPR REF') capable of streamlining the complete process of training, validating, testing, and comparing promoter recognition models

SUBMITTER: Perez Martell RI 

PROVIDER: S-EPMC8976979 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature