ArticleJournal of computer-aided molecular design2021
IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.
Article in Journal of computer-aided molecular design, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed, 9 citations in OpenAlex.
- Association between Diagnostic History and Cancer Incidence within 5 Years: A Real-world Observational Analysis.Cancer research communications · 2026Observational
- Integrating Redox Proteomics and Computational Modeling to Decipher Thiol-Based Oxidative Post-Translational Modifications (oxiPTMs) in Plant Stress Physiology.International journal of molecular sciences · 2025Review
- PMTPred: machine-learning-based prediction of protein methyltransferases using the composition of k-spaced amino acid pairs.Molecular diversity · 2024Article
- Meta-2OM: A multi-classifier meta-model for the accurate prediction of RNA 2'-O-methylation sites in human RNA.PloS one · 2024Article
- PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.International journal of molecular sciences · 2021Article
- PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations.International journal of molecular sciences · 2021Article
Corrections and comments
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Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Redox-sensitive cysteine (RSC) thiol contributes to many biological processes. The identification of RSC plays an important role in clarifying some mechanisms of redox-sensitive factors; nonetheless, experimental investigation of RSCs is expensive and time-consuming. The computational approaches that quickly and accurately identify candidate RSCs using the sequence information are urgently needed. Herein, an improved and robust computational predictor named IRC-Fuse was developed to identify the RSC by fusing of multiple feature representations. To enhance the performance of our model, we integrated the probability scores evaluated by the random forest models implementing different encoding schemes. Cross-validation results exhibited that the IRC-Fuse achieved accuracy and AUC of 0.741 and 0.807, respectively. The IRC-Fuse outperformed exiting methods with improvement of 10% and 13% on accuracy and MCC, respectively, over independent test data. Comparative analysis suggested that the IRC-Fuse was more effective and promising than the existing predictors. For the convenience of experimental scientists, the IRC-Fuse online web server was implemented and publicly accessible at http://kurata14.bio.kyutech.ac.jp/IRC-Fuse/ .
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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 Socratic graph.