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Since the introduction of ChIP-seq experiments in 2007, many statistical and computational methods have been developed to support the analysis of the massive datasets from these experiments.
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The microarray dataset obtained from these experiments contains expression levels for 22690 Affymetrix probe set IDs.
In the case of the iPSCS example, we analyzed the dataset from the experiments performed by [ 36].
Large gene expression datasets from microarray experiments already exist and many of these can be used to help assign potential functions to these genes.
All Pareto optimal datasets generated during these experiments are available from the website http://discovery.dartmouth.edu/model_free_data/.edu/model_free_data/
The sheer size of the datasets from HTS experiments requires tools to visualize, analyse, summarize and interpret these large-scale datasets.
The tool handles large-scale image datasets from HCS experiments by providing a dataset preview.
Multiple ChIP-Seq datasets from independent experiments were seldom compiled and incorporated into computational network inference.
We could obtain 92 datasets from 50 experiments covering 13 distinct cancer types.
This is beneficial when large datasets from microarray experiments are searched.
High-confidence datasets from two experiments for yeast (Ito et al., 2001; Uetz et al., 2000) have only 9% overlap.
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