Projects: Difference between revisions
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* [[Dimont]]: general approach for discriminative de-novo motif discovery from high-throughput data | * [[Dimont]]: general approach for discriminative de-novo motif discovery from high-throughput data | ||
* [[AUC-PR]]: area under ROC and PR curves for weighted and unweighted data | * [[AUC-PR]]: area under ROC and PR curves for weighted and unweighted data | ||
* [[Slim]]: Sparse local inhomogenous mixture (Slim) models and dependency logos |
Revision as of 16:22, 5 July 2015
This site contains projects that use Jstacs.
- MotifAdjuster: a tool for computational reassessment of transcription factor binding site annotations
- Prior: apples and oranges: avoiding different priors in Bayesian DNA sequence analysis
- GenDisMix: unifying generative and discriminative learning principles
- Dispom: de-novo discovery of differentially abundant transcription factor binding sites including their positional preference
- MiMB: probabilistic approaches to transcription factor binding site prediction
- SHMM: utilizing gene-pair orientations for improved analysis of ChIP-chip promoter array data
- DSHMM: exploiting prior knowledge and gene distances in the analysis of tumor expression profiles
- PHHMM: improved analysis of Array-CGH data
- MeDIP-HMM: HMM-based analysis of DNA methylation profiles
- ARHMM: integrating local chromosomal dependencies into the analysis of tumor expression profiles
- FlowCap: molecular classification of acute myeloid leukaemia (AML) using flow cytometry data
- TALgetter: prediction of TAL effector target sites
- TALENoffer: genome-wide TALEN off-target prediction
- Dimont: general approach for discriminative de-novo motif discovery from high-throughput data
- AUC-PR: area under ROC and PR curves for weighted and unweighted data
- Slim: Sparse local inhomogenous mixture (Slim) models and dependency logos