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
Accurate quantification in proteomics with QuantUMS

article Full Text OpenAlex

Towards the explainability of protein language models

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Expanding the human proteome with microproteins and peptideins

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GROQ-seq Datasets Across Transcription Factors (LacI, RamR, VanR), T7 RNA Polymerase and TEV Protease

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How artificial intelligence is reengineering protein engineering

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Learning heterogeneous biological interactions via meta-relation-guided dual-channel graph transformer for circular ribonucleic acid function prediction

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DefensePredictor: A machine learning model to discover prokaryotic immune systems

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Computational identification of marine microalgal metabolites as human voltage-dependent anion channel 1 modulators: Virtual screening, DFT analysis, molecular dynamics simulation, and machine learning-supported cheminformatic validation

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Protein and genomic language models uncover the unexplored diversity of bacterial immunity

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AI-based methods for simulating, sampling, and predicting protein ensembles

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

article Full Text OpenAlex 9659 FWCI773.1752

UniProt: the Universal Protein Knowledgebase in 2023

article Full Text OpenAlex 7003 FWCI545.1388

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

preprint Full Text OpenAlex 5398 FWCI332.5614

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

article Full Text OpenAlex 4892 FWCI690.5483

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

article Full Text OpenAlex 2759 FWCI212.008

InterPro in 2022

article Full Text OpenAlex 2581 FWCI218.0728

Fast and accurate protein structure search with Foldseek

article Full Text OpenAlex 2368 FWCI330.3358

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

article Full Text OpenAlex 2093 FWCI181.2046

Robust deep learning–based protein sequence design using ProteinMPNN

article Full Text OpenAlex 1842 FWCI124.0685

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

preprint Full Text OpenAlex 1495 FWCI0