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
Expanding the human proteome with microproteins and peptideins

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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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DefensePredictor: A machine learning model to discover prokaryotic immune systems

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AlphaFold Database expands to proteome-scale quaternary structures

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Has AI Reshaped Drug Discovery, or Is There Still a Long Way to Go?

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From single-sequences to evolutionary trajectories: protein language models capture the evolutionary potential of SARS-CoV-2

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Artificial intelligence and microscopy: A systematic review of deep learning in microalgae and cyanobacteria species identification

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Accurate predictions of disordered protein ensembles with STARLING

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ChatDIA: A zero-shot large language model workflow for targeted analysis of data-independent acquisition mass spectrometry data

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The e-Dimensionality Information Principle

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

article Full Text OpenAlex 9452 FWCI772.8696

UniProt: the Universal Protein Knowledgebase in 2023

article Full Text OpenAlex 6772 FWCI545.2288

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

preprint Full Text OpenAlex 5372 FWCI358.3027

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

article Full Text OpenAlex 4616 FWCI682.1736

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

article Full Text OpenAlex 2656 FWCI212.0431

InterPro in 2022

article Full Text OpenAlex 2539 FWCI218.0567

Fast and accurate protein structure search with Foldseek

article Full Text OpenAlex 2242 FWCI327.4315

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 1714 FWCI124.0023

DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks

preprint Full Text OpenAlex 1440 FWCI0