ArticlePloS one2009
RBF-TSS: identification of transcription start site in human using radial basis functions network and oligonucleotide positional frequencies.
Article in PloS one, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 16 citations in OpenAlex.
- Deep learning and support vector machines for transcription start site identification.PeerJ. Computer science · 2023Article
- Nonlinear physics opens a new paradigm for accurate transcription start site prediction.BMC bioinformatics · 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
- Article
- Unfolding SARS-CoV-2 viral genome to understand its gene expression regulation.Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases · 2020Article
- Eukaryotic and prokaryotic promoter prediction using hybrid approach.Theory in biosciences = Theorie in den Biowissenschaften · 2011Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Accurate identification of promoter regions and transcription start sites (TSS) in genomic DNA allows for a more complete understanding of the structure of genes and gene regulation within a given genome. Many recently published methods have achieved high identification accuracy of TSS. However, models providing more accurate modeling of promoters and TSS are needed. A novel identification method for identifying transcription start sites that improves the accuracy of TSS recognition for recently published methods is proposed. This method incorporates a metric feature based on oligonucleotide positional frequencies, taking into account the nature of promoters. A radial basis function neural network for identifying transcription start sites (RBF-TSS) is proposed and employed as a classification algorithm. Using non-overlapping chunks (windows) of size 50 and 500 on the human genome, the proposed method achieves an area under the Receiver Operator Characteristic curve (auROC) of 94.75% and 95.08% respectively, providing increased performance over existing TSS prediction methods.
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What OpenQuestion holds
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.