ArticleBMC bioinformatics2022
Machine learning and statistics shape a novel path in archaeal promoter annotation.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- RNA polymerase II transcription initiation with and without XPB translocase activity.Communications biology · 2026Review
- Supervised clustering of bacterial promoter identifies two groups with different relevant positions at -10.Briefings in functional genomics · 2026Article
- Deep Generative Model-Driven Design of Microbial Synthetic Promoters.Journal of microbiology and biotechnology · 2025Review
- Article
- Characterization of archaeal promoters using explainable and web-based CNN model.BMC genomics · 2025Article
- Machine learning classification of archaea and bacteria identifies novel predictive genomic features.BMC genomics · 2024Article
- Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.Briefings in bioinformatics · 2024Article
- Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.International journal of molecular sciences · 2024Article
- CDBProm: the Comprehensive Directory of Bacterial Promoters.NAR genomics and bioinformatics · 2024Article
- Explainable artificial intelligence as a reliable annotator of archaeal promoter regions.Scientific reports · 2023Article
- Model-interpreted outcomes of artificial neural networks classifying immune biomarkers associated with severe infections in ICU.Frontiers in immunology · 2023Article
- Article
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundArchaea are a vast and unexplored domain. Bioinformatic techniques might enlighten the path to a higher quality genome annotation in varied organisms. Promoter sequences of archaea have the action of a plethora of proteins upon it. The conservation found in a structural level of the binding site of proteins such as TBP, TFB, and TFE aids RNAP-DNA stabilization and makes the archaeal promoter prone to be explored by statistical and machine learning techniques. RESULTS AND DISCUSSIONS: In this study, experimentally verified promoter sequences of the organisms Haloferax volcanii, Sulfolobus solfataricus, and Thermococcus kodakarensis were converted into DNA duplex stability attributes (i.e. numerical variables) and were classified through Artificial Neural Networks and an in-house statistical method of classification, being tested with three forms of controls. The recognition of these promoters enabled its use to validate unannotated promoter sequences in other organisms. As a result, the binding site of basal transcription factors was located through a DNA duplex stability codification. Additionally, the classification presented satisfactory results (above 90%) among varied levels of control. CONCLUDING REMARKS: The classification models were employed to perform genomic annotation into the archaea Aciduliprofundum boonei and Thermofilum pendens, from which potential promoters have been identified and uploaded into public repositories.
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