ArticleProtein science : a publication of the Protein Society2024
A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning.
Article in Protein science : a publication of the Protein Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 10 citations in OpenAlex.
- Taxonomy-aware, disorder-matched benchmarking of phase-separating protein predictors.Genome biology · 2026Article
- MuFGPS: enhancing liquid-liquid phase separation protein prediction through multi-level features and ensemble learning.Briefings in bioinformatics · 2026Article
- Rethinking bioinformatics in liquid-liquid phase separation: data resources, predictive models, and an event-centric perspective.Briefings in bioinformatics · 2026Review
- Empirical Assessment of Sequence-Based Predictions of Intrinsically Disordered Regions Involved in Phase Separation.Biomolecules · 2025Article
- Comprehensive protein datasets and benchmarking for liquid-liquid phase separation studies.Genome biology · 2025Article
- Predicting heteropolymer phase separation using two-chain contact maps.The Journal of chemical physics · 2025Article
- MambaPhase: deep learning for liquid-liquid phase separation protein classification.Briefings in bioinformatics · 2025Article
- PSTP: accurate residue-level phase separation prediction using protein conformational and language model embeddings.Briefings in bioinformatics · 2025Article
- Liquid-Liquid Phase Separation Associated with Intrinsically Disordered Proteins: Experimental and Computational Tools.Current protein & peptide science · 2025Review
- A sequence-based model for identifying proteins undergoing liquid-liquid phase separation/forming fibril aggregates via machine learning.Protein science : a publication of the Protein Society · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 2 institutions in 1 country.
Funding
Abstract
Liquid-liquid phase separation (LLPS) and the solid aggregate (also referred to as amyloid aggregates) formation of proteins, have gained significant attention in recent years due to their associations with various physiological and pathological processes in living organisms. The systematic investigation of the differences and connections between proteins undergoing LLPS and those forming amyloid fibrils at the sequence level has not yet been explored. In this research, we aim to address this gap by comparing the two types of proteins across 36 features using collected data available currently. The statistical comparison results indicate that, 24 of the selected 36 features exhibit significant difference between the two protein groups. A LLPS-Fibrils binary classification model built on these 24 features using random forest reveals that the fraction of intrinsically disordered residues (F
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.