ArticleBioinformatics (Oxford, England)2009
Probabilistic retrieval and visualization of biologically relevant microarray experiments.
Article in Bioinformatics (Oxford, England), 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
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- A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury.Nature communications · 2017Article
- MetaTopics: an integration tool to analyze microbial community profile by topic model.BMC genomics · 2017Article
- Review
- Retrieving relevant time-course experiments: a study on Arabidopsis microarrays.IET systems biology · 2016Article
- Review
- Probe Region Expression Estimation for RNA-Seq Data for Improved Microarray Comparability.PloS one · 2015Article
- Exploration and retrieval of whole-metagenome sequencing samples.Bioinformatics (Oxford, England) · 2014Article
- Article
- GeneTopics--interpretation of gene sets via literature-driven topic models.BMC systems biology · 2013Article
- Comprehensive data-driven analysis of the impact of chemoinformatic structure on the genome-wide biological response profiles of cancer cells to 1159 drugs.BMC bioinformatics · 2012Article
- Data-driven information retrieval in heterogeneous collections of transcriptomics data links SIM2s to malignant pleural mesothelioma.Bioinformatics (Oxford, England) · 2012Article
- Alignment of gene expression profiles from test samples against a reference database: New method for context-specific interpretation of microarray data.BioData mining · 2011Article
- Generalized random set framework for functional enrichment analysis using primary genomics datasets.Bioinformatics (Oxford, England) · 2011Article
- Content-based microarray search using differential expression profiles.BMC bioinformatics · 2010Article
- Article
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5 authors.
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Abstract
motivationAs ArrayExpress and other repositories of genome-wide experiments are reaching a mature size, it is becoming more meaningful to search for related experiments, given a particular study. We introduce methods that allow for the search to be based upon measurement data, instead of the more customary annotation data. The goal is to retrieve experiments in which the same biological processes are activated. This can be due either to experiments targeting the same biological question, or to as yet unknown relationships.
resultsWe use a combination of existing and new probabilistic machine learning techniques to extract information about the biological processes differentially activated in each experiment, to retrieve earlier experiments where the same processes are activated and to visualize and interpret the retrieval results. Case studies on a subset of ArrayExpress show that, with a sufficient amount of data, our method indeed finds experiments relevant to particular biological questions. Results can be interpreted in terms of biological processes using the visualization techniques. AVAILABILITY: The code is available from http://www.cis.hut.fi/projects/mi/software/ismb09.
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