ArticleFrontiers in immunology2020
A Hybrid Model for Predicting Pattern Recognition Receptors Using Evolutionary Information.
Article in Frontiers in immunology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities and challenges with artificial intelligence in allergy and immunology: a bibliometric study.Frontiers in medicine · 2025Pooled it
- Integrative immunoinformatics and structural modeling for the rational design of a multi-epitope vaccine candidate against human cytomegalovirus.Scientific reports · 2026Article
- Utilizing plant synthetic biology to accelerate plant-microbe interactions research.Biodesign research · 2025Review
- Prognostic Biomarker-Based Identification of Drugs for Managing the Treatment of Endometrial Cancer.Molecular diagnosis & therapy · 2021Article
- Prognostic biomarkers for predicting papillary thyroid carcinoma patients at high risk using nine genes of apoptotic pathway.PloS one · 2021Article
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Authors and funding
3 authors.
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Abstract
This study describes a method developed for predicting pattern recognition receptors (PRRs), which are an integral part of the immune system. The models developed here were trained and evaluated on the largest possible non-redundant PRRs, obtained from PRRDB 2.0, and non-pattern recognition receptors (Non-PRRs), obtained from Swiss-Prot. Firstly, a similarity-based approach using BLAST was used to predict PRRs and got limited success due to a large number of no-hits. Secondly, machine learning-based models were developed using sequence composition and achieved a maximum MCC of 0.63. In addition to this, models were developed using evolutionary information in the form of PSSM composition and achieved maximum MCC value of 0.66. Finally, we developed hybrid models that combined a similarity-based approach using BLAST and machine learning-based models. Our best model, which combined BLAST and PSSM based model, achieved a maximum MCC value of 0.82 with an AUROC value of 0.95, utilizing the potential of both similarity-based search and machine learning techniques. In order to facilitate the scientific community, we also developed a web server "PRRpred" based on the best model developed in this study (http://webs.iiitd.edu.in/raghava/prrpred/).
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Registered trials
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