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
Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications

article Full Text OpenAlex

Autonomous AI Agent for QSAR Modeling with Dataset Curation, Descriptor Selection, and Domain Assessment

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Accurate quantification in proteomics with QuantUMS

article Full Text OpenAlex

Predicting and Decoding Allosteric Binding Sites Using Protein Language Models and Structure-Based Machine Learning: An Energy Landscape-Guided Explainable AI Framework

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Expanding the human proteome with microproteins and peptideins

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Integrated MINFLUX tracking reveals two distinct chromatin dynamics classes across cell types

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How artificial intelligence is reengineering protein engineering

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Artificial intelligence-driven discovery of coumarin-based therapeutics: Revolutionizing target identification and validation

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Molecular characterization and immunoinformatics-based design of a multi-epitope vaccine against Staphylococcus nepalensis

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

article Full Text OpenAlex 9929 FWCI695.1921

UniProt: the Universal Protein Knowledgebase in 2023

article Full Text OpenAlex 7276 FWCI491.4424

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

article Full Text OpenAlex 5409 FWCI265.3678

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

article Full Text OpenAlex 5280 FWCI484.3431

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

article Full Text OpenAlex 2888 FWCI191.3775

InterPro in 2022

article Full Text OpenAlex 2630 FWCI196.6239

Fast and accurate protein structure search with Foldseek

article Full Text OpenAlex 2502 FWCI226.9671

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

article Full Text OpenAlex 2095 FWCI163.7358

Robust deep learning–based protein sequence design using ProteinMPNN

article Full Text OpenAlex 1990 FWCI111.3498

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

preprint Full Text OpenAlex 1563 FWCI0