iDHS-EL specifications

Information


Unique identifier OMICS_11431
Name iDHS-EL
Interface Web user interface
Restrictions to use None
Input data DNA sequences
Input format FASTA
Computer skills Basic
Stability No
Maintained No

Maintainer


This tool is not available anymore.

Publication for iDHS-EL

iDHS-EL in publications

 (4)
PMCID: 5744982
PMID: 29281700
DOI: 10.1371/journal.pone.0189541

[…] of the svm method. pse-analysis carries out the following five key tasks: feature extraction, parameter selection, model training, cross validation and evaluation. another example is the idhs-el method mainly designed for dna sequence data []. idhs-el uses three different ways to extract feature vector to represent sequence data, which leads to three different basic random forest […]

PMCID: 5444793
PMID: 28542398
DOI: 10.1371/journal.pone.0178217

[…] numbers obtained from binarized ct images at the various threshold levels. the method of the current study is similar to those used in bioinformatics, such as pse-in-one, pse-analysis, repdna, and idhs-el [–]. these studies and the current study focused on how to create the feature vector which can be easily and effectively combined with machine learning algorithm., the current study used […]

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

[…] decision 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 […]

PMCID: 5370107
PMID: 28350835
DOI: 10.1371/journal.pone.0174386

[…] of protein fold classification. moreover, a multi-objective evolutionary algorithm, bases on membranes [], is invented to solve the network clustering problem. furthermore, a new predictor, named idhs-el, is proposed by using the strategy of ensemble learning framework [], to identify the location of dhs in human genome. these supervised classification methods largely depend on the usage […]


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iDHS-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
iDHS-EL funding source(s)
This work was supported by the National Natural Science Foundation of China (No. 61300112, 61573118 and 61272383), the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry, and the Natural Science Foundation of Guangdong Province (2014A030313695), Shenzhen Foundational Research Funding (Grant No. JCYJ20150626110425228), and Development Program of China (863 Program) [2015AA015405].

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