ArticleBMC bioinformatics2019
A model to predict the function of hypothetical proteins through a nine-point classification scoring schema.
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 18 papers.
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Who cites it
18 citing papers in PubMed, 51 citations in OpenAlex.
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- Structural Characterization and Functional Annotation of Hypothetical Proteins in the Multidrug-Resistant Strains ofBioMed research international · 2026Article
- FungiProteomeDB: a database for the molecular weight and isoelectric points of the fungal proteomes.Database : the journal of biological databases and curation · 2023Article
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- Characterization of siderophores from Escherichia coli strains through genome mining tools: an antiSMASH study.AMB Express · 2022Article
- Advancement of Metatranscriptomics towards Productive Agriculture and Sustainable Environment: A Review.International journal of molecular sciences · 2022Review
- In Silico Identification and Characterization of a Hypothetical Protein FromBioinformatics and biology insights · 2022Article
- Investigating the Functional Role of Hypothetical Proteins From an Antarctic BacteriumFrontiers in genetics · 2022Article
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- Prediction of Potential Drug Targets and Vaccine Candidates Against Antibiotic-ResistantInternational journal of peptide research and therapeutics · 2022Article
- Chemogenomic Approaches for Revealing Drug Target Interactions in Drug Discovery.Current genomics · 2021Review
- Augmented sequence features and subcellular localization for functional characterization of unknown protein sequences.Medical & biological engineering & computing · 2021Article
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- Hypothetical Proteins as Predecessors of Long Non-coding RNAs.Current genomics · 2020Review
- Homology modeling and molecular dynamics based insights into3 Biotech · 2020Article
- Current Status and Potential Applications of Underexplored Prokaryotes.Microorganisms · 2019Review
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHypothetical proteins [HP] are those that are predicted to be expressed in an organism, but no evidence of their existence is known. In the recent past, annotation and curation efforts have helped overcome the challenge in understanding their diverse functions. Techniques to decipher sequence-structure-function relationship, especially in terms of functional modelling of the HPs have been developed by researchers, but using the features as classifiers for HPs has not been attempted. With the rise in number of annotation strategies, next-generation sequencing methods have provided further understanding the functions of HPs.
resultsIn our previous work, we developed a six-point classification scoring schema with annotation pertaining to protein family scores, orthology, protein interaction/association studies, bidirectional best BLAST hits, sorting signals, known databases and visualizers which were used to validate protein interactions. In this study, we introduced three more classifiers to our annotation system, viz. pseudogenes linked to HPs, homology modelling and non-coding RNAs associated to HPs. We discuss the challenges and performance of these classifiers using machine learning heuristics with an improved accuracy from Perceptron (81.08 to 97.67), Naive Bayes (54.05 to 96.67), Decision tree J48 (67.57 to 97.00), and SMO_npolyk (59.46 to 96.67).
conclusionWith the introduction of three new classification features, the performance of the nine-point classification scoring schema has an improved accuracy to functionally annotate the HPs.
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