ArticleBMC bioinformatics2019
Computational prediction of MoRFs based on protein sequences and minimax probability machine.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Predicting molecular recognition features in protein sequences with MoRFchibi 2.0.BMC bioinformatics · 2026Article
- MoRFs_TransFuse: a MoRFs predictor based on multimodal feature fusion and the lightweight Transformer network.BioData mining · 2025Article
- Computational prediction of disordered binding regions.Computational and structural biotechnology journal · 2023Review
- Intrinsically disordered proteins play diverse roles in cell signaling.Cell communication and signaling : CCS · 2022Review
- Predicting Protein Conformational Disorder and Disordered Binding Sites.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Computational predictions for protein sequences of COVID-19 virus via machine learning algorithms.Medical & biological engineering & computing · 2021Article
- Prediction of MoRFs based on sequence properties and convolutional neural networks.BioData mining · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundMolecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods.
resultsIn this study, a new sequence-based method, named as MoRF
conclusionsThe features used in MoRF
Indexed as
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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.