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- ARHMM
- AUC-PR
- Acknowledgements
- AnnoTALE
- CRISPRer
- Catchitt
- ChIPper
- Code examples
- Compute and plot Receiver Operating Characteristic (ROC) and Precision-Recall (PR) curve
- Cookbook
- Cookbook – Starter: Data handling
- Create a discrete alphabet
- DBcorrDB
- DSHMM
- DerTALEv2
- Dessert: Alignments, Utils, and goodies
- Dimont
- Disentangler
- Dispom
- Downloads
- Dream5
- EpiTALE
- FAQs
- First main course: SequenceScores
- FlowCap
- FlowCap/
- GeMoMa
- GeMoMa-Docs
- GeMoRNA
- GenDisMix
- Getting started
- Hauptseite
- Implementation of a homogeneous Markov model of order 0 based on AbstractModel
- Imprint
- InMoDe
- InhPMM
- Intermediate course: Optimization
- Intermediate course: XMLParser, Parameters, and Results
- Loading data
- Main Page
- MeDIP-HMM
- MeDeMo
- MiMB
- MiMB Example
- MiRNAs
- MotifAdjuster
- No Access
- PCTLearn
- PHHMM
- PMM
- PMMdeNovo
- Performing a 10-fold cross validation
- PrediTALE
- Prior
- Projects
- Quick start: Jstacs in a nutshell
- Recent changes
- Recipes
- SHMM
- Saving and loading a model
- Second main course: Classifiers
- Slim
- Starter: Data handling
- TALENoffer
- TALgetter
- Test
- Train classifiers using GenDisMix (a hybrid learning principle)
- Train classifiers using MCL and MSP
- Training a classifier and classifying new sequences
- Using the REnvironment
- Version history
- Workshop
