Multiple protein feature prediction with statistical relational learning

TL;DR

This paper introduces a novel method for protein feature prediction using semantic-based regularization, improving prediction quality by incorporating prior knowledge, demonstrated on yeast genome data.

Contribution

It presents a new approach leveraging semantic-based regularization to enhance protein feature prediction beyond traditional homology-based methods.

Findings

Improved prediction accuracy on yeast genome data

Semantic regularization positively impacts prediction quality

Outperforms traditional annotation transfer methods

Abstract

High throughput sequencing techniques have highly impactedon modern biology, widening the gap between sequenced andannotated data. Automatic annotation tools are thereforeof the foremost importance to guide biologists' experiments. However, most of the state-of-the-art methods rely on annotation transfer, offering reliable predictions only in homology settings. In this work we present a novel appraoch to protein feature prediction, which exploits the Semanti Based Regularization to inject prior knowledge in the learning process. The experimental results conducted on the yeast genome show that the introduction of the constraints positively impacts on the overall prediction quality.