BioWeka is an extension of the Weka data mining framework for bioinformatics applications. It provides additional tools for tasks like sequence analysis, gene expression analysis, and protein structure prediction. BioWeka implements these tools as extendable components within Weka's framework using common data formats and interfaces in order to improve interoperability and allow easy comparison of different methods. It has been used in applications like predicting the coding frame of sequences and distinguishing plant and pathogen genes.

1. BioWeka Extending the Weka framework for Bioinformatics Martin Szugat ( [email_address] ) http:// www.bioweka.org

5. Knowledge Discovery Process Data Selection and Preparation Transformation and Reduction Data Mining Evaluation and Visualization

7. Knowledge Discovery Experiment Data Selection and Preparation Transformation and Reduction Data Mining Evaluation and Visualization

9. Intermediate data format Format A Tool X Tool Y Tool Z Format C Format B Mediator

10. Unified interfaces Loader Data Classifier Filter 1 … Filter n FastaLoader CSVLoader … SymbolCounter SymbolAnalyzer … Alignment SVM …  Extendable and Customizable Execution Pipeline

12. Weka Explorer

13. Weka Experimenter

16. Implementation Patterns

26. Training Eclat Sequences Codon frequencies Norm- alization SVM Frames Factors 1 Model 1 Codon frequencies Norm- alization SVM Factors 2 Model 2

27. Evaluating Eclat Sequence Frames Norm- alization SVM Factors 2 Model 2 Codon frequencies Norm- alization SVM Factors 1 Model 1 Correct Frame

28. EclatClassifier/Filter Filtered Classifier SMO Random Forest Naive Bayes JRip J4.8 Multiple Filter Translate Terminator Symbol Counter Sum Normalizer Remove/ Copy Normalize MinMax Normalizer newMin = -1.0 newMax = 1.0 pseudoCount = 1.0 alphabet = DNA symbolWidth = 3.0

29. Classifier Comparison 1) LibSVM vs. SMO 10 x Sampling with Stratification (2:1)  10 x 3-fold CV 0.7 1.1 0.7 0.7 0.2 Deviation 81.5 (1.3) 84.8 (1.1) 87.1 (1.0) 88.2 (1.0) 93.1 (0.7) BioWeka 82.2 (1.6) 85.9 (1.8) 87.8 (0.9) 88.9 (1.0) 92.9 (0.6) Eclat J4.8 JRip Naïve Bayes Random Forest SVM 1 Accuracy [%] (SD)

30. EclatFrameFinder/Classifier EclatFrame Classifier EclatFrame Finder EclatFrame Shifter Frame Shifter Eclat Classifier

32. Eclat Eclat EclatFrame Finder Eclat Classifier