id:"swepub:oai:gup.ub.gu.se/253110"
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 03104naa a2200313 4500 | |
| 001 | oai:gup.ub.gu.se/253110 | |
| 003 | SwePub | |
| 008 | 240528s2011 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://gup.ub.gu.se/publication/2531102 URI |
| 024 | 7 | a https://doi.org/10.1186/1756-0381-4-92 DOI |
| 040 | a (SwePub)gu | |
| 041 | a eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Schilling, Ruben4 aut |
| 245 | 1 0 | a pGQL: A probabilistic graphical query language for gene expression time courses. |
| 264 | c 2011-04-18 | |
| 264 | 1 | b Springer Science and Business Media LLC,c 2011 |
| 520 | a Timeboxes are graphical user interface widgets that were proposed to specify queries on time course data. As queries can be very easily defined, an exploratory analysis of time course data is greatly facilitated. While timeboxes are effective, they have no provisions for dealing with noisy data or data with fluctuations along the time axis, which is very common in many applications. In particular, this is true for the analysis of gene expression time courses, which are mostly derived from noisy microarray measurements at few unevenly sampled time points. From a data mining point of view the robust handling of data through a sound statistical model is of great importance.We propose probabilistic timeboxes, which correspond to a specific class of Hidden Markov Models, that constitutes an established method in data mining. Since HMMs are a particular class of probabilistic graphical models we call our method Probabilistic Graphical Query Language. Its implementation was realized in the free software package pGQL. We evaluate its effectiveness in exploratory analysis on a yeast sporulation data set.We introduce a new approach to define dynamic, statistical queries on time course data. It supports an interactive exploration of reasonably large amounts of data and enables users without expert knowledge to specify fairly complex statistical models with ease. The expressivity of our approach is by its statistical nature greater and more robust with respect to amplitude and frequency fluctuation than the prior, deterministic timeboxes. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskapx Bioinformatik0 (SwePub)102032 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciencesx Bioinformatics0 (SwePub)102032 hsv//eng |
| 700 | 1 | a Costa, Ivan G4 aut |
| 700 | 1 | a Schliep, Alexander,d 1967u Gothenburg University,Göteborgs universitet,Institutionen för data- och informationsteknik, datavetenskap (GU),Department of Computer Science and Engineering, Computing Science (GU)4 aut0 (Swepub:gu)xscale |
| 710 | 2 | a Göteborgs universitetb Institutionen för data- och informationsteknik, datavetenskap (GU)4 org |
| 773 | 0 | t BioData miningd : Springer Science and Business Media LLCg 4q 4x 1756-0381 |
| 856 | 4 | u https://biodatamining.biomedcentral.com/track/pdf/10.1186/1756-0381-4-9 |
| 856 | 4 8 | u https://gup.ub.gu.se/publication/253110 |
| 856 | 4 8 | u https://doi.org/10.1186/1756-0381-4-9 |
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