Tool stats & trends
Looking to identify usage trends or leading experts?
|Interface||Command line interface|
|Restrictions to use||None|
|Operating system||Unix/Linux, Mac OS, Windows|
No version available
- person_outline Kai Tan
Publication for CSI-ANN
Identifying noncoding risk variants using disease relevant gene regulatory networks
[…] 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
[…] 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
[…] 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 […]
Looking to check out a full list of citations?
Be the first to review CSI-ANN