Workshop: Difference between revisions
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''Information about'': Alignment, HMM package, Discriminative Sampling | ''Information about'': Alignment, HMM package, Discriminative Sampling | ||
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| 10:45 - 11:15 || | | 10:45 - 11:15 || Lasse Feldhahn || Talk | ||
|align="left"| ''''' | |align="left"| '''''Discriminative Metropolis sampler for motif discovery''''' | ||
|- | |- | ||
| 11:15 - 13:00 | | 11:15 - 13:00 | ||
|colspan="3" align="left" style="background-color:#eeeeee;"| '''Lunch''' | |colspan="3" align="left" style="background-color:#eeeeee;"| '''Lunch''' | ||
|- | |- | ||
|align=" | | 13:00 - 13:30 || Ralf Eggeling || Talk | ||
|align="left"| '''''Maximum Tsallis Entropy Models''''' | |||
''Abstract'': Classifying short sequence signals is a standard task in genome analysis. One important application is to distinguish splice donor and acceptor sites from decoy. A standard solution of the problem utilizes the maximum entropy principle. Here, we discuss a generalizion of this approach based on the Tsallis entropy and its implementation in Jstacs. | |||
|- | |||
| 13:30 - 14:00 || Michael Seifert || Talk | |||
|align="left"| '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models''''' | |align="left"| '''''Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models''''' | ||
''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. | ''Abstract'': Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. | ||
|- | |- | ||
| | | 14:00 - 14:30 || Michael Scharfe || Talk | ||
|align="left"| ''''' | |align="left"| '''''A Hidden Markov Model for Phylogenetic Footprinting''''' | ||
''Abstract'': | ''Abstract'': Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version. | ||
|- | |- | ||
| 14: | | 14:30 - 15:00 || Martin Gleditzsch || Talk | ||
|align="left"| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks''''' | |align="left"| '''''PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks''''' | ||
|- | |- | ||
| | | 15:00 - 16:00 || || Discussion | ||
|align="left"| '''''Discussion on future directions and improvements of Jstacs''''' | |align="left"| '''''Discussion on future directions and improvements of Jstacs''''' | ||
|} | |} | ||
Revision as of 12:37, 6 May 2011
The first Jstacs workshop will be held on May, 19th at Martin Luther University. The aim of the workshop is to increase the visibility of Jstacs and the implemented projects, to bring developers and users together, as well as to identify further needs.
Depending on the audience the workshop will be held in English or German.
Program
| Time | Presenter | Type | Title & Infos |
|---|---|---|---|
| 09:00 - 10:00 | Jan Grau & Jens Keilwagen | Tutorial | Introduction to Jstacs
Information about: Alphabet, AlphabetContainer, Sequence, Sample, Model, ScoringFunction, Classifier, Optimization, Parameter, ParameterSet, StrandModel |
| 10:00 - 10:45 | Jan Grau & Jens Keilwagen | Tutorial | New Features of Jstacs 1.5
Information about: Alignment, HMM package, Discriminative Sampling |
| 10:45 - 11:15 | Lasse Feldhahn | Talk | Discriminative Metropolis sampler for motif discovery |
| 11:15 - 13:00 | Lunch | ||
| 13:00 - 13:30 | Ralf Eggeling | Talk | Maximum Tsallis Entropy Models
Abstract: Classifying short sequence signals is a standard task in genome analysis. One important application is to distinguish splice donor and acceptor sites from decoy. A standard solution of the problem utilizes the maximum entropy principle. Here, we discuss a generalizion of this approach based on the Tsallis entropy and its implementation in Jstacs. |
| 13:30 - 14:00 | Michael Seifert | Talk | Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models
Abstract: Changes in gene expression levels play a central role in tumors. Additional information about the distribution of gene expression levels and distances between adjacent genes on chromosomes should be integrated into the analysis of tumor expression profiles. We use a Hidden Markov Model with distance-scaled transition matrices (DSHMM) to incorporate chromosomal distances of adjacent genes on chromosomes into the identification of differentially expressed genes in breast cancer. |
| 14:00 - 14:30 | Michael Scharfe | Talk | A Hidden Markov Model for Phylogenetic Footprinting
Abstract: Phylogenetic footprinting is an important method to discover overrepresented and cross-species conserved sequence motifs (e.g. TFBSs). An extension of the classical Hidden Markov Model that implements phylogenetic emissions was developed using the HMM-Package of the current Jstacs version. |
| 14:30 - 15:00 | Martin Gleditzsch | Talk | PhyloBayes: An approach to model simultaneously evolutionary dependencies and statistical dependencies between neighbored DNA-Positions with Bayesian Networks |
| 15:00 - 16:00 | Discussion | Discussion on future directions and improvements of Jstacs | |
Location
The workshop will be held on May, 19th in room 5.09 at the Institute of Computer Science at Martin Luther University Halle (Von-Seckendorff-Platz 1).
Contact
If you have any questions or comments, please do not hesitate to contact us by mail jstacs@informatik.uni-halle.de.
