Protein secondary structure prediction using a small training set (compact model) combined with a Complex-valued neural network approach.

ABSTRACT:

Background

Protein secondary structure prediction (SSP) has been an area of intense research interest. Despite advances in recent methods conducted on large datasets, the estimated upper limit accuracy is yet to be reached. Since the predictions of SSP methods are applied as input to higher-level structure prediction pipelines, even small errors may have large perturbations in final models. Previous works relied on cross validation as an estimate of classifier accuracy. However, training on large numbers of protein chains compromises the classifier ability to generalize to new sequences. This prompts a novel approach to training and an investigation into the possible structural factors that lead to poor predictions. Here, a small group of 55 proteins termed the compact model is sel

SUBMITTER: Rashid S 

PROVIDER: S-EPMC5020447 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature