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CSI-ANN specifications


Unique identifier OMICS_06228
Software type Framework/Library
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages MATLAB
Computer skills Advanced
Stability Stable
Maintained Yes


No version available


  • person_outline Kai Tan

Publication for CSI-ANN

CSI-ANN citations


Identifying noncoding risk variants using disease relevant gene regulatory networks

Nat Commun
PMCID: 5816022
PMID: 29453388
DOI: 10.1038/s41467-018-03133-y

[…] Enhancers were predicted using the Chromatin Signature Inference by Artificial Neural Network CSI-ANN algorithm. The input to the algorithm is the normalized ChIP-Seq signals of three histone marks (H3K4me1, H3K4me3, and H3K27ac). The algorithm combines signals of all histone marks and uses an […]


eRFSVM: a hybrid classifier to predict enhancers integrating random forests with support vector machines

PMCID: 5226099
PMID: 28096768
DOI: 10.1186/s41065-016-0012-2

[…] n immune precipitation sequencing (ChIP-Seq) datasets [, ], such as the chromatin modification loci and the TF binding sites (TFBs) [, ]. Single classifiers used supervised learning algorithms, e.g., CSI-ANN [] introduced an artificial neural network approach; RFECS [] identified enhancers with RF; ChromaGenSVM [] applied SVMs with a Genetic Algorithm (GA) to optimize the parameters of SVMs; Enhan […]


Progress and challenges in bioinformatics approaches for enhancer identification

Brief Bioinform
PMCID: 5142011
PMID: 26634919
DOI: 10.1093/bib/bbv101

[…] In particular, CSI-ANN [] is one of the first enhancer classification systems that rely on an ANN using chromatin signatures as input. Putative enhancers derived from human CD4T cell data from Wang et al. [] based o […]


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CSI-ANN institution(s)
Department of Internal Medicine, University of Iowa, Iowa City, IA, USA

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