ArticleBriefings in bioinformatics2021
Computational prediction and interpretation of both general and specific types of promoters in Escherichia coli by exploiting a stacked ensemble-learning framework.
Article in Briefings in bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed, 78 citations in OpenAlex.
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- GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs.Computational and structural biotechnology journal · 2024Article
- iProL: identifying DNA promoters from sequence information based on Longformer pre-trained model.BMC bioinformatics · 2024Article
- Recognition of cyanobacteria promoters via Siamese network-based contrastive learning under novel non-promoter generation.Briefings in bioinformatics · 2024Article
- INTEDE 2.0: the metabolic roadmap of drugs.Nucleic acids research · 2024Article
- A stacking ensemble model for predicting the occurrence of carotid atherosclerosis.Frontiers in endocrinology · 2024Article
- Time series-based hybrid ensemble learning model with multivariate multidimensional feature coding for DNA methylation prediction.BMC genomics · 2023Article
- PromGER: Promoter Prediction Based on Graph Embedding and Ensemble Learning for Eukaryotic Sequence.Genes · 2023Article
- PseU-ST: A new stacked ensemble-learning method for identifying RNA pseudouridine sites.Frontiers in genetics · 2023Article
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- Predicting Pseudouridine Sites with Porpoise.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Clarion is a multi-label problem transformation method for identifying mRNA subcellular localizations.Briefings in bioinformatics · 2022Article
- csORF-finder: an effective ensemble learning framework for accurate identification of multi-species coding short open reading frames.Briefings in bioinformatics · 2022Article
- Computational prediction and interpretation of druggable proteins using a stacked ensemble-learning framework.iScience · 2022Article
- Deepm5C: A deep-learning-based hybrid framework for identifying human RNA N5-methylcytosine sites using a stacking strategy.Molecular therapy : the journal of the American Society of Gene Therapy · 2022Article
- AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning.Scientific reports · 2022Article
Corrections and comments
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Authors and funding
9 authors at 9 institutions in 5 countries.
Funding
Abstract
Promoters are short consensus sequences of DNA, which are responsible for transcription activation or the repression of all genes. There are many types of promoters in bacteria with important roles in initiating gene transcription. Therefore, solving promoter-identification problems has important implications for improving the understanding of their functions. To this end, computational methods targeting promoter classification have been established; however, their performance remains unsatisfactory. In this study, we present a novel stacked-ensemble approach (termed SELECTOR) for identifying both promoters and their respective classification. SELECTOR combined the composition of k-spaced nucleic acid pairs, parallel correlation pseudo-dinucleotide composition, position-specific trinucleotide propensity based on single-strand, and DNA strand features and using five popular tree-based ensemble learning algorithms to build a stacked model. Both 5-fold cross-validation tests using benchmark datasets and independent tests using the newly collected independent test dataset showed that SELECTOR outperformed state-of-the-art methods in both general and specific types of promoter prediction in Escherichia coli. Furthermore, this novel framework provides essential interpretations that aid understanding of model success by leveraging the powerful Shapley Additive exPlanation algorithm, thereby highlighting the most important features relevant for predicting both general and specific types of promoters and overcoming the limitations of existing 'Black-box' approaches that are unable to reveal causal relationships from large amounts of initially encoded features.
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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 OpenQuestion graph.