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Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National.

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Presentation on theme: "Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National."— Presentation transcript:

1 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Assessing the Performance of Macromolecular Sequence Classifiers Cornelia Caragea (cornelia@cs.iastate.edu) Iowa State University Joint work with Jivko Sinapov, Drena Dobbs, and Vasant Honavar October 15, 2007

2 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Background and Motivation  Machine Learning methods offer some of the most cost- effective approaches to building predictive models  One problem – multiple approaches  Needed: comparing the effectiveness of different predictive classifiers  Difficulty: different data selection and evaluation procedures

3 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Outline  Macromolecular Sequence Classification  Performance Evaluation  Window-Based Cross-Validation  Sequence-Based Cross-Validation  Experiments  Conclusions

4 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Macromolecular Sequence Classification  Predict a label for each element in a given sequence  Example:  Identify post-translational modification residues M K LI TI L C F L S R L L P S L T Q E S S Q EID Glycosylated? H3N+H3N+ COO - Phosphorylated?

5 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Macromolecular Sequence Classification  Example:  Identify RNA-binding residues 1T0K_B SINQKLALVIKSGKYTLGYKSTVKSLRQGKSKLIIIAANTPVLRKSELEYYAMLSKTKVYYFQGGNNELGTAVGKLFRVGVVSILEAGD SDILTTLA 0000000000000000111110010000000000000001100100000000000000000000010000000001111100000000000000000

6 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Macromolecular Sequence Classification Training Data Test Data Learning System Resulting Classifier Validation Performance on test set All Data

7 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Macromolecular Sequence Classification  Sliding Window Approach: Sequence: DSNPKYLGVKKFGGEVVKAGNILVRQRGTKFKAGQGVGMGRDHTLFALSDGK Class: 1111110011111110011111001011111100000001111101000000 Target residue Class label. VKKFGGEVVKAGNIL,0 KKFGGEVVKAGNILV,0 KFGGEVVKAGNILVR,1 FGGEVVKAGNILVRQ,1.

8 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Outline  Macromolecular Sequence Classification  Performance Evaluation  Window-Based Cross-Validation  Sequence-Based Cross-Validation  Experiments  Conclusions

9 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Performance Evaluation K-Fold Cross-Validation: S1S1 S k-1 S2S2 SkSk Learn classifier C Evaluate classifier C repeat k times

10 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Window-Based Cross-Validation Procedure:  Extract windows from all sequences in the dataset  Partition the set of windows into k disjoint subsets  Perform standard cross-validation S1S1 S k-1 S2S2 SkSk Learn classifier C Evaluate classifier C repeat k times windows

11 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Sequence-Based Cross-Validation Procedure:  Partition the set of sequences into k disjoint subsets  Extract windows from sequences in each subset  Perform standard cross-validation S1S1 S k-1 S2S2 SkSk Learn classifier C Evaluate classifier C repeat k times sequences

12 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Window-Based vs. Sequence-Based Cross-Validation  Window-Based Cross-Validation:  Train and test sets are likely to contain some windows that originate from the same sequence.  This violates the independence assumption between train and test sets.  Sequence-Based Cross-Validation:  Windows belonging to the same sequence end up in the same set.

13 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Machine Learning Classifiers  Support Vector Machine:  0/1 String Kernel  Example:  Naïve Bayes:  Identity Window: x = VKKFGGEVVKAGNIL y = KKFGGEVVKAGNILV I[x i =y i ] = 010010010000000 x = V,K,K,F,G,G,E,V,V,K,A,G,N,I,L VKKFGGEVVKAGNIL

14 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Datasets  O-GlycBase dataset:  contains experimentally verified glycosylation sites  http://www.cbs.dtu.dk/databases/OGLYCBASE/ http://www.cbs.dtu.dk/databases/OGLYCBASE/  RNA-Protein Interface dataset, RB147 :  consists of RNA-binding protein sequences extracted from structures of known RNA-protein complexes solved by X-ray crystallography in the Protein Data Bank.  http://bindr.gdcb.iastate.edu/RNABindR/  Protein-Protein Interface dataset:  consists of protein-binding protein sequences

15 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Datasets Number of positive and negative instances used in our experiments DatasetNumber of Sequences Number of + Instances Number of - Instances O-GlycBase216216812147 RNA-Protein147433627988 Protein-Protein4223509204

16 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Outline  Macromolecular Sequence Classification  Performance Evaluation  Window-Based Cross-Validation  Sequence-Based Cross-Validation  Experiments  Conclusions

17 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Experimental Design Questions:  How does Sequence-Based Cross-Validation compare with Window-Based Cross-Validation?  How do the results vary when we vary the size of the dataset?

18 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Results Receiver Operating Characteristic (ROC) Curves for Window-Based and Sequence-Based 10-Fold Cross-Validation using SVM O-glycBase

19 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Results a) O-glycBase b) RNA-Protein Interface c) Protein-Protein Interface AUC CC

20 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Outline  Macromolecular Sequence Classification  Performance Evaluation  Window-Based Cross-Validation  Sequence-Based Cross-Validation  Experiments  Conclusions

21 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Conclusions  Compared two variants of k-fold cross-validation: window-based and sequence-based k-fold cross-validation.  The comparison shows that Window-Based CV overestimates the performance of the classifiers relative to Sequence-Based CV.  Sequence-Based CV provides more realistic estimates of performance, because predictors trained on labeled sequence data have to predict the labels for residues in a novel sequence.

22 Iowa State University Department of Computer Science Artificial Intelligence Research Laboratory Research supported in part by a grant from the National Institutes of Health (GM066387). Jivko Sinapov Drena Dobbs Vasant Honavar

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