ArticleTheScientificWorldJournal2014
Prediction of DNase I hypersensitive sites by using pseudo nucleotide compositions.
Article in TheScientificWorldJournal, 2014. 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.
The trial behind it
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.
- Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting.Molecular genetics and genomics : MGG · 2020Article
- PseUI: Pseudouridine sites identification based on RNA sequence information.BMC bioinformatics · 2018Article
- pDHS-ELM: computational predictor for plant DNase I hypersensitive sites based on extreme learning machines.Molecular genetics and genomics : MGG · 2018Article
- DHSpred: support-vector-machine-based human DNase I hypersensitive sites prediction using the optimal features selected by random forest.Oncotarget · 2018Article
- Article
- Identifying NMolecular genetics and genomics : MGG · 2016Article
- Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
DNase I hypersensitive sites (DHS) associated with a wide variety of regulatory DNA elements. Knowledge about the locations of DHS is helpful for deciphering the function of noncoding genomic regions. With the acceleration of genome sequences in the postgenomic age, it is highly desired to develop cost-effective computational methods to identify DHS. In the present work, a support vector machine based model was proposed to identify DHS by using the pseudo dinucleotide composition. In the jackknife test, the proposed model obtained an accuracy of 83%, which is competitive with that of the existing method. This result suggests that the proposed model may become a useful tool for DHS identifications.
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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.