Department of Computer Science
 Rutgers University

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GQL: Graphical Query Language

GQL is a suite of tools for analyizing time-course experiments. Currently, it is adapted to gene expression data. The two main tools are GQLQuery, for querying data sets, and GQLCluster, which provides a way for computing groupings based on a number of methods (model-based clustering using HMMs as cluster models and estimation of a mixture of HMMs).

GQLQuery: Querying time-courses

The GUI has been ported to Python using Tkinter and the brand-new Python bindings for GHMM. It runs on all Linux/Unix boxes. Executable binaries for MAC and Windows are provided.

GQLCluster: Finding groups in time-courses

Publications

Costa, Ivan G. and Schönhuth, Alexander and Schliep, Alexander. The Graphical Query Language: a tool for analysis of gene expression time-courses (2005) [details]

Schliep, Alexander and Costa, Ivan G. and Steinhoff, Christine and Schönhuth, Alexander. Analyzing gene expression time-courses (2005) [details]

Schliep, Alexander and Schönhuth, Alexander and Steinhoff, Christine. Using hidden Markov models to analyze gene expression time course data (2003) [details]

Schliep, Alexander and Steinhoff, Christine and Schönhuth, Alexander. Robust inference of groups in gene expression time-courses using mixtures of HMMs (2004) [details]

Costa, Ivan G.. Mixture Models for the Analysis of Gene Expression: Integration of Multiple Experiments and Cluster Validation (2008) [details]

Schliep, Alexander and Georgi, Benjamin and Rungsarityotin, Wasinee and Costa, Ivan G. and Schönhuth, Alexander. The General Hidden Markov Model Library: Analyzing Systems with Unobservable States (2005) [details]

Costa, Ivan G. and Schönhuth, Alexander and Hafemeister, Christoph and Schliep, Alexander. Constrained Mixture Estimation for Analysis and Robust Classification of Clinical Time Series (2009) [details]

Schönhuth, Alexander and Costa, Ivan G. and Schliep, Alexander. Semi-supervised Clustering of Yeast Gene Expression (2009) [details]

Costa, I. G. and Krause, R. and Optiz, L. and Schliep, A.. Semi-supervised learning for the identification of syn-expressed genes from fused microarray and in situ image data (2007) [details]