ILMCNet: A Deep Neural Network Model That Uses PLM to Process Features and Employs CRF to Predict Protein Secondary Structure.

ABSTRACT:

Background

Protein secondary structure prediction (PSSP) is a critical task in computational biology, pivotal for understanding protein function and advancing medical diagnostics. Recently, approaches that integrate multiple amino acid sequence features have gained significant attention in PSSP research.

Objectives

We aim to automatically extract additional features represented by evolutionary information from a large number of sequences while simultaneously incorporating positional information for more comprehensive sequence features. Additionally, we consider the interdependence between secondary structures during the prediction stage.

Methods

To this end, we propose a deep neural network model, ILMCNet, which utilizes a language model and Conditional Random Field (

SUBMITTER: Dong B 

PROVIDER: S-EPMC11507629 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature