ArticleTheory in biosciences = Theorie in den Biowissenschaften2011
Eukaryotic and prokaryotic promoter prediction using hybrid approach.
Article in Theory in biosciences = Theorie in den Biowissenschaften, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 67 citations in OpenAlex.
- Identification of co-localised transcription factors based on paired motifs analysis.IET systems biology · 2024Article
- Exploring objective feature sets in constructing the evolution relationship of animal genome sequences.BMC genomics · 2023Article
- Genomic benchmarks: a collection of datasets for genomic sequence classification.BMC genomic data · 2023Article
- Computational identification of promoters inFrontiers in microbiology · 2023Article
- Database of Potential Promoter Sequences in theBiology · 2022Article
- MAGE genes encoding for embryonic development in cattle is mainly regulated by zinc finger transcription factor family and slightly by CpG Islands.BMC genomic data · 2022Article
- Critical assessment of computational tools for prokaryotic and eukaryotic promoter prediction.Briefings in bioinformatics · 2022Article
- Sigma70Pred: A highly accurate method for predicting sigma70 promoter inFrontiers in microbiology · 2022Article
- In silico analysis of promoter region and regulatory elements of mitogenome co-expressed trn gene clusters encoding for bio-pesticide in entomopathogenic fungus, Metarhizium anisopliae: strain ME1.Journal, genetic engineering & biotechnology · 2021Article
- Predicting Bacteriophage Enzymes and Hydrolases by Using Combined Features.Frontiers in bioengineering and biotechnology · 2020Article
- iProEP: A Computational Predictor for Predicting Promoter.Molecular therapy. Nucleic acids · 2019Article
- iPro70-FMWin: identifying Sigma70 promoters using multiple windowing and minimal features.Molecular genetics and genomics : MGG · 2019Article
- iDNA6mA-Rice: A Computational Tool for Detecting N6-Methyladenine Sites in Rice.Frontiers in genetics · 2019Article
- Image-based promoter prediction: a promoter prediction method based on evolutionarily generated patterns.Scientific reports · 2018Article
- 70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features.BMC systems biology · 2018Article
- iRSpot-Pse6NC: Identifying recombination spots inInternational journal of biological sciences · 2018Article
- SpotOn: High Accuracy Identification of Protein-Protein Interface Hot-Spots.Scientific reports · 2017Article
- Evolutionary mechanism and biological functions of 8-mers containing CG dinucleotide in yeast.Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology · 2017Article
- High-Performance Prediction of Human Estrogen Receptor Agonists Based on Chemical Structures.Molecules (Basel, Switzerland) · 2017Article
- A novel feature ranking method for prediction of cancer stages using proteomics data.PloS one · 2017Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Promoters are modular DNA structures containing complex regulatory elements required for gene transcription initiation. Hence, the identification of promoters using machine learning approach is very important for improving genome annotation and understanding transcriptional regulation. In recent years, many methods have been proposed for the prediction of eukaryotic and prokaryotic promoters. However, the performances of these methods are still far from being satisfactory. In this article, we develop a hybrid approach (called IPMD) that combines position correlation score function and increment of diversity with modified Mahalanobis Discriminant to predict eukaryotic and prokaryotic promoters. By applying the proposed method to Drosophila melanogaster, Homo sapiens, Caenorhabditis elegans, Escherichia coli, and Bacillus subtilis promoter sequences, we achieve the sensitivities and specificities of 90.6 and 97.4% for D. melanogaster, 88.1 and 94.1% for H. sapiens, 83.3 and 95.2% for C. elegans, 84.9 and 91.4% for E. coli, as well as 80.4 and 91.3% for B. subtilis. The high accuracies indicate that the IPMD is an efficient method for the identification of eukaryotic and prokaryotic promoters. This approach can also be extended to predict other species promoters.
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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.