Deep Learning in Proteomics Informatics: Applications, Challenges, and Future Directions

TL;DR

This paper reviews how deep learning advances proteomics by improving data analysis and prediction accuracy, discusses current applications, challenges like data scarcity and interpretability, and suggests future research directions.

Contribution

It provides a comprehensive review of deep learning applications in proteomics, highlighting challenges and proposing future research pathways.

Findings

Deep learning accelerates protein data processing.

Enhances accuracy of protein structure and function predictions.

Identifies key challenges like data scarcity and model interpretability.

Abstract

Deep learning is an advanced technology that relies on large-scale data and complex models for feature extraction and pattern recognition. It has been widely applied across various fields, including computer vision, natural language processing, and speech recognition. In recent years, deep learning has demonstrated significant potential in the realm of proteomics informatics, particularly in deciphering complex biological information. The introduction of this technology not only accelerates the processing speed of protein data but also enhances the accuracy of predictions regarding protein structure and function. This provides robust support for both fundamental biology research and applied biotechnological studies. Currently, deep learning is primarily focused on applications such as protein sequence analysis, three-dimensional structure prediction, functional annotation, and the construction of protein interaction networks. These applications offer numerous advantages to proteomic research. Despite its growing prevalence in this field, deep learning faces several challenges including data scarcity, insufficient model interpretability, and computational complexity; these factors hinder its further advancement within proteomics. This paper comprehensively reviews the applications of deep learning in proteomics along with the challenges it encounters. The aim is to provide a systematic theoretical discussion and practical basis for research in this domain to facilitate ongoing development and innovation of deep learning technologies within proteomics.