Evaluation of Supervised Learning Algorithms on Gene Expression Data CSCI 6505 – Machine Learning

1. Machine Learning Prediction Evaluation of Supervised Learning Algorithms on Gene Expression DataCSCI 6505 – Machine Learning Adan Cosgaya acosgaya@dal.ca Winter 2006 Dalhousie University

2. Outline • Introduction • Definition of the Problem • Related Work • Algorithms • Description of the Data • Methodology of Experiments • Results • Relevance of Results • Conclusions & Future Work

3. Introduction • ML has gained attention in the biomedical field. • Need to turn biomedical data into meaningful information. • Microarray technology is used to generate gene expression data. • Gene expression data involves a huge number of numeric attributes (gene expression measurements). • This kind of data is also characterized by consisting of a small numbers of instances. • This work investigates the classification problem on such data.

4. Definition of the Problem • Classifying Gene Expression Data • Number of features (n) is much greater than the number of sample instances (m). (n >> m) • Typical data: n > 5000, and m < 100 • High risk of overfitting the data due the abundance of attributes and shortage of available samples. • The datasets produced by Microarray experiments are highly dimensional and often noisy due to the process involved in the experiments.

5. Related Work • Using gene expression data for the task of classification, has recently gained attention in the biomedical community. • Golub et al. describe an approach to cancer classification based on gene expression applied to human acute Leukemia (ALL vs AML). • A. Rosenwald et al. developed a model predictor of patient survival after chemotherapy (Alive vs Dead). • Furey et al. present a method to analyze microarray expression data using SVM. • Guyon et al. experiment with reducing the dimensionality of gene expression data.

6. Algorithms • K-Nearest Neighbor (KNN) • It is one of the simplest and widely used algorithms for data classification. • Naive Bayes (NB) • It assumes that the effect of a feature value on a given class is independent of the values of other features. • Decision Trees (DT) • Internal nodes represent tests on one or more attributes and leaf nodes indicate decision outcomes. • Support Vector Machines (SVM) • Works well on high dimensional data

7. Description of the Data • Leukemia dataset. • A collection of 72 expression measurements. The samples are divided into two variants of leukemia: 25 samples of acute myeloid leukemia (AML) and 47 samples acute lymphoblastic leukemia (ALL). • Diffuse Large-B-Cell Lymphoma (DLBCL) dataset • Biopsy samples that were examined for gene expression with the use of DNA microarrays. Each sample corresponds to the prediction of survival after chemotherapy for diffuse large-B-cell lymphoma (Alive, Dead).

8. All features Feature Selection (gene subset) Algorithm Methodology of Experiments • Feature Selection • Remove irrelevant features (but may have biological meaning). • Use of GainRatio • Selecting a Supervised Learning Method • KNN, NB, DT, SVM • Testing Methodology • Evaluation over independent test set (train/test split) • Ratios: 66/34, 80/20, 90/10 • 10-fold Cross-Validation • Compare both methods and see if they are in logical agreement

9. Methodology of Experiments (cont…) • Measuring Performance • Accuracy • Precision (p) • Recall (r) • F-Measure • It is hard to compare two classifiers using two measures. F-Measure combines precision and recall into one measure. • F-Measure is the harmonic mean of precision, and recall. • For F to be large, both p and r must be large.

10. Results • Without Feature Selection • KNN and SVM perform better • Naive Bayes and SVM perform better Cross-validation results are lower; it uses nearly all the data for training and testing, giving a more realistic estimation.

11. Results (cont…) • With Feature Selection • NB and SVM perform better • KNN and SVM perform better There is an increase in the overall accuracy, more notorious in DLBCL

12. Results (cont…) • Summary of classification accuracies with cross-validation • F-Measures for both datasets with and without feature selection

13. Relevance of Results • Performance depends on the characteristics of the problem, the quality of the measurements in the data, and the capabilities of the classifier in finding regularities in the data. • Feature selection, helps to minimize the use of redundant and/or noisy features. • SVM gave the best results, they perform well with high dimensional data, and also benefit from feature selection. • Decision Trees had the overall worst performance, however, they still work at a competitive level.

14. Relevance of Results (cont…) • Surprisingly, KNN behaves relatively well despite its simplicity, this characteristic allows it to scale well for large feature spaces. • In the case of the Leukemia dataset, very high accuracies were achieved here for all the algorithms. Perfect accuracy was achieved in many cases. • The DLBCL dataset shows lower accuracies, although using feature selection improved them. • In the overall, the observations of the accuracy results are consistent with those from the F-measure, giving us confidence in the relevance of the results obtained.

15. Conclusions & Future Work • Supervised learning algorithms can be used to the classification of gene expression data from DNA microarrays with high accuracy. • SVM by its very own nature, deal well with high dimensional gene expression data. • We have verified that there are subsets of features (genes) that are more relevant than others and better separate the classes. • The use of one algorithm instead of others should be evaluated on a case by case basis

16. Conclusions & Future Work (cont…) • The use of feature selection proved to be beneficial to improve the overall performance of the algorithms. This idea can be extended to the use of other feature selection methods or data transformation such as PCA. • Analysis of the effect of noisy gene expression data on the reliability of the classifier. • While the scope of our experimental results is confined to a couple of datasets, the analysis can be used as a baseline for future use of supervised learning algorithms for gene expression data

17. References • T.R. Golub et al. Molecular classification of cancer: class discovery and class prediction by gene-expression monitoring.Science, Vol. 286, 531–537, 1999. • A. Rosenwald, G. Wright, W. C. Chan, et al. The use of molecular profiling to predict survival after chemotherapy for diffuse large B-cell lymphoma.New England Journal of Medicine,Vol. 346, 1937–1947, 2002. • Terrence S. Furey, Nello Cristianini, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data.Bioinformatics, Vol. 16, 906–914, 2001. • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines.BIOWulf Technical Report, 2000. • Ethem Alpaydin. Introduction to Machine Learning. The MIT Press, 2004. • Ian H. Witten, Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. Second Edition. Morgan Kaufmann Publishers , 2005 • Wikipedia: www.wikipedia.org • Alvis Brazma, Helen Parkinson, Thomas Schlitt, Mohammadreza Shojatalab. A quick introduction to elements of biology-cells, molecules, genes, functional genomics, microarrays. European Bioinformatics Institute.

18. Thank You!