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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 citations


Multi categorical deep learning neural network to classify retinal images: A pilot study employing small database

PLoS One
PMCID: 5667846
PMID: 29095872
DOI: 10.1371/journal.pone.0187336

[…] the disease-labeled image data and 4096 input features from the last covered layer of pre-trained VGG-19 model, we trained ensemble SVM classifiers by complying with clustering and voting approaches (iRSpot-EL) [], K-means clustering with dynamic selection strategy (D3C) [], multiple kernel learning [], and AdaBoost (deep SVM) []. We replaced the previous ensemble classifier with preserving the st […]


iSS PC: Identifying Splicing Sites via Physical Chemical Properties Using Deep Sparse Auto Encoder

Sci Rep
PMCID: 5557945
PMID: 28811565
DOI: 10.1038/s41598-017-08523-8

[…] achine learning algorithms, and found that the SAE classification algorithm was stable and reliable. Therefore, the new approach could be used to solve many important tasks in bioinformatics, such as iRSpot-EL, iDHS-EL, iEnhancer-2L. And these are the work which should be completed in the next phase. In fact, we had constructed a predictor called “iDHSs-PseTNC” to identify DNase I hypersensitive s […]


Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project

PLoS One
PMCID: 5524285
PMID: 28738059
DOI: 10.1371/journal.pone.0179805

[…] resented another ensemble learning framework, called iDHS-EL, for identifying the location of DHS in human genome by fusing three individual Random Forest (RF) classifiers into an ensemble predictor. iRSpot-EL [] is another ensemble learning framework which has been designed to identify recombination spots by fusing different modes of pseudo K-tuple nucleotide composition and mode of dinucleotide- […]


Development of machine learning models for diagnosis of glaucoma

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

[…] ision tree models [] such as C5.0 [,] show good interpretability and poor prediction power. Logistic Regression and Naïve 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 l […]

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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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