专题:Machine Learning in Bioinformatics

This cluster of papers focuses on the prediction of protein subcellular localization using various computational methods such as amino acid composition, machine learning algorithms like support vector machines, and the analysis of signal peptides and transmembrane topology. The research aims to improve the accuracy and reliability of predicting the subcellular location of proteins, which has significant implications for understanding protein function and cellular processes.
最新文献
ChatDIA: A zero-shot large language model workflow for targeted analysis of data-independent acquisition mass spectrometry data

article Full Text OpenAlex

Reviewing the role of artificial intelligence in accelerating the development of novel antimicrobial peptides

article Full Text OpenAlex

Fundamental limitations of genomic language models for realistic sequence generation

article Full Text OpenAlex

Language model-guided anticipation and discovery of mammalian metabolites

article Full Text OpenAlex

Immunoinformatics-based design and evaluation of a multi-epitope vaccine against Vibrio fluvialis

article Full Text OpenAlex

RLBindDeep: A ResNet-LSTM based novel framework for protein–ligand binding affinity prediction

article Full Text OpenAlex

A systematic review of molecular representation learning foundation models

article Full Text OpenAlex

Deep contrastive learning enables genome-wide virtual screening

article Full Text OpenAlex

Approaches for accelerating microbial gene function discovery using artificial intelligence

article Full Text OpenAlex

Root-associated protein prediction using a protein large language model and hypergraph convolutional networks

article Full Text OpenAlex

近5年高被引文献
ColabFold: making protein folding accessible to all

article Full Text OpenAlex 9228 FWCI773.1492

UniProt: the Universal Protein Knowledgebase in 2023

article Full Text OpenAlex 6542 FWCI545.3594

Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

preprint Full Text OpenAlex 5347 FWCI395.5915

Evolutionary-scale prediction of atomic-level protein structure with a language model

article Full Text OpenAlex 4285 FWCI670.2699

SignalP 6.0 predicts all five types of signal peptides using protein language models

article Full Text OpenAlex 2527 FWCI212.089

InterPro in 2022

article Full Text OpenAlex 2475 FWCI218.2479

Fast and accurate protein structure search with Foldseek

article Full Text OpenAlex 2111 FWCI326.073

SignalP 6.0 predicts all five types of signal peptides using protein language models

article Full Text OpenAlex 2093 FWCI179.4489

Robust deep learning–based protein sequence design using ProteinMPNN

article Full Text OpenAlex 1602 FWCI123.8704

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

preprint Full Text OpenAlex 1393 FWCI0