iRSpot-EL statistics

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Citations per year

Citations chart

Popular tool citations

chevron_left DNA recombination site prediction chevron_right
Popular tools chart

Tool usage distribution map

Tool usage distribution map

Associated diseases

Associated diseases

iRSpot-EL specifications


Unique identifier OMICS_14575
Name iRSpot-EL
Interface Web user interface
Restrictions to use None
Input data A DNA sequence.
Input format FASTA
Output data The predicted results, the hotspots and coldspots contained in the input sequence, the sequence information, the detailed results, the result visualization.
Computer skills Basic
Stability Stable
Maintained Yes



  • person_outline Bin Liu <>

Publication for iRSpot-EL

iRSpot-EL in publication

PMCID: 5441603
PMID: 28542342
DOI: 10.1371/journal.pone.0177726

[…] tree models [] such as c5.0 [,] show good interpretability and poor prediction power. logistic regression and na├»ve bayes are algorithms used for probabilistic classification []. idhs-el [] and irspot-el [] are predictors developed for identifying the location of dnase i hypersensitive sites (dhss) and dna recombination spots in human genomes. the goal of this study is to develop a machine […]

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iRSpot-EL institution(s)
School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China; Gordon Life Science Institute, Belmont, MA, USA; Center of Excellence in Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia
iRSpot-EL funding source(s)
This work was supported by the National High Technology Research and Development Program of China (863 Program) (2015AA015405), the National Natural Science Foundation of China (No. 61300112, 61573118 and 61272383), the Natural Science Foundation of Guangdong Province (2014A030313695), Guangdong Natural Science Funds for Distinguished Yong Scholars (2016A030306008), and Scientific Research Foundation in Shenzhen (Grant No. JCYJ20150626110425228).

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