1. 339 Case Studies

2. 340 Case Study: Diagnostic Model From Array Gene Expression Data Computational Models of Lung Cancer: Connecting Classification, Gene Selection, and Molecular Sub-typing C.Aliferis M.D., Ph.D., Pierre Massion M.D. I. Tsamardinos Ph.D., D. Hardin Ph.D.

3. 341 Case Study: Diagnostic Model From Array Gene Expression Data Specific Aim 1: “Construct computational models that distinguish between important cellular states related to lung cancer, e.g., (i) Cancerous vs Normal Cells; (ii) Metastatic vs Non-Metastatic cells; (iii) Adenocarcinomas vs Squamous carcinomas”. Specific Aim 2: “Reduce the number of gene markers by application of biomarker (gene) selection algorithms such that small sets of genes can distinguish among the different states (and ideally reveal important genes in the pathophysiology of lung cancer).”

4. 342 Case Study: Diagnostic Model From Array Gene Expression Data Bhattacharjee et al. PNAS, 2001 12,600 gene expression measurements obtained using Affymetrix oligonucleotide arrays 203 patients and normal subjects, 5 disease types, ( plus staging and survival information)

5. 343 Case Study: Diagnostic Model From Array Gene Expression Data Linear and polynomial-kernel Support Vector Machines (LSVM, and PSVM respectively) C optimized via C.V. from {10 -8, 10 -7, 10 -6, 10 -5, 10 -4, 10 -3, 10 -2, 0.1, 1, 10, 100, 1000} and degree from the set: {1, 2, 3, 4}. K-Nearest Neighbors (KNN) (k optimized via C.V.) Feed-forward Neural Networks (NNs). 1 hidden layer, number of units chosen (heuristically) from the set {2, 3, 5, 8, 10, 30, 50}, variable- learning-rate back propagation, custom-coded early stopping with (limiting) performance goal=10 -8 (i.e., an arbitrary value very close to zero), and number of epochs in the range [100,…,10000], and a fixed momentum of 0.001 Stratified nested n-fold cross-validation (n=5 or 7 depending on task)

6. 344 Case Study: Diagnostic Model From Array Gene Expression Data Area under the Receiver Operator Characteristic (ROC) curve (AUC) computed with the trapezoidal rule (DeLong et al. 1998). Statistical comparisons among AUCs were performed using a paired Wilcoxon rank sum test (Pagano et al. 2000). Scale gene values linearly to [0,1] Feature selection: –RFE (parameters as the ones used in Guyon et al 2002) –UAF (Fisher criterion scoring; k optimized via C.V.)

7. 345 Case Study: Diagnostic Model From Array Gene Expression Data Classification Performance

8. 346 Case Study: Diagnostic Model From Array Gene Expression Data Gene selection

9. 347 Case Study: Diagnostic Model From Array Gene Expression Data Novelty

10. 348 Case Study: Diagnostic Model From Array Gene Expression Data A more detailed look: –Specific Aim 3: “Study how aspects of experimental design (including data set, measured genes, sample size, cross-validation methodology) determine the performance and stability of several machine learning (classifier and feature selection) methods used in the experiments”.

11. 349 Case Study: Diagnostic Model From Array Gene Expression Data Overfitting: we replace actual gene measurements by random values in the same range (while retaining the outcome variable values). Target class rarity: we contrast performance in tasks with rare vs non-rare categories. Sample size: we use samples from the set {40,80,120,160, 203} range (as applicable in each task). Predictor info redundancy: we replace the full set of predictors by random subsets with sizes in the set {500, 1000, 5000, 12600}.

12. 350 Case Study: Diagnostic Model From Array Gene Expression Data Train-test split ratio: we use train-test ratios from the set {80/20, 60/40, 40/60} (for tasks II and III, while for task I modified ratios were used due to small number of positives, see Figure 1). Cross-validated fold construction: we construct n-fold cross- validation samples retaining the proportion of the rarer target category to the more frequent one in folds with smaller sample, or, alternatively we ensure that all rare instances are included in the union of test sets (to maximize use of rare-case instances). Classifier type: Kernel vs non-kernel and linear vs non-linear classifiers are contrasted. Specifically we compare linear and non- linear SVMs (a prototypical kernel method) to each other and to KNN (a robust and well-studied non-kernel classifier and density estimator).

13. 351 Case Study: Diagnostic Model From Array Gene Expression Data  Random gene values

14. 352 Case Study: Diagnostic Model From Array Gene Expression Data  varying sample size

15. 353 Case Study: Diagnostic Model From Array Gene Expression Data  Random gene selection

16. 354 Case Study: Diagnostic Model From Array Gene Expression Data  Split ratio

17. 355 Case Study: Diagnostic Model From Array Gene Expression Data  Use of rare categories

18. 356 Case Study: Diagnostic Model From Array Gene Expression Data Questions: –What would you do differently? –How to interpret the biological significance of the selected genes? –What is wrong with having so many and robust good classification models? –Why do we have so many good models?

19. 357 Case Study: Diagnostic Model From Array Gene Expression Data We have recently completed an extensive analysis of all multi-category gene expression-based cancer datasets in the public domain. The analysis spans >75 cancer types and >1,000 patients in 12 datasets. On the basis of this study we have created a tool (GEMS) that automatically analyzes data to create diagnostic systems and identify biomarker candidates using a variety of techniques. The present incarnation of the tool is oriented toward the computer-savvy researcher; a more biologist-friendly web- accessible version is under development.

20. 358 Case Study: Diagnostic Model From Array Gene Expression Data GEMS System “Methods for Multi-Category Cancer Diagnosis from Gene Expression Data: A Comprehensive Evaluation to Inform Decision Support System Development” A. Statnikov, C.F. Aliferis, I. Tsamardinos AMIA/MEDINFO 2004

21. 359 Case Study: Diagnostic Modeling From Mass Spectrometry Data

22. 360 Creating a Tool (FAST-AIMS) for Cancer Diagnostic Decision Support Using Mass Spectrometry Data Nafeh Fananapazir Department of Biomedical Informatics Vanderbilt University Academic Committee: Constantin Aliferis (Primary Advisor), Dean Billheimer, Douglas Hardin, Shawn Levy, Daniel Liebler, Ioannis Tsamardinos

23. 361Introduction Problem: In the last two years, we have seen the emergence of mass spectrometry in the domain of cancer diagnosis Mass spectrometry on biological samples produces data with a size and complexity that defies simple analysis. There is a need for clinicians without expertise in the field of machine learning to have access to intelligent software that permits at least a first pass analysis as to the diagnostic capabilities of data obtained from mass spectrometry analysis.

24. 362 MS Studies in Cancer Research: Types Blood Serum Tissue Biopsy Nipple Aspirate Fluid Pancreatic Juice Ovarian Prostate Renal Breast Head & Neck Lung Pancreatic Specimen Types Cancer Types

25. 363 MS Studies in Cancer Research: Problems 1. Lack of disclosure of key methods components 2. Overfitting 3. One-time partitioning of data 4. Lack of randomization when allocating to test/train sets 5. Lack of an appropriate performance metric

26. 364 Data Source: Blood Serum Advantages –Relatively non-invasive –Easily obtained –Access to most tissues in the body –Screening possibilities Composition/Derivation –Blood Plasma Protein Constituents –Albumins –Globulins –Fibrinogen –Low Molecular Weight (LMW) Proteins

27. 365 Data Representation: Mass Spectrometry MALDI-TOF/SELDI-TOF 1 –Relatively little sample purification is required –Direct measurement of proteins from serum, tissue, other bio. samples –Relatively rapid analysis time –Production of intact molecular ions with little fragmentation –Detection of proteins with m/z ranging from 2000-100,000 daltons –Collection of useful spectra from complex mixtures –Accuracies approaching 1 part in 10,000 Data Characteristics –Parameters »Mass/Charge (M/Z) »Intensity –Format »Continuous »Peak Detection 1 Billheimer D., A Functional Data Approach to MALDI-TOF MS Protein Analysis

28. 366 Data Analysis: Paradigm

29. 367 Data Analysis: Preparations a. Get Mass Spectra b. Data Pre-Processing Baseline subtraction Peak detection [Coombes 2003] Feature Selection Normalization of intensities Peak alignment

30. 368 Data Analysis: Experimental Design c. Classification: Parameter Optimization

31. 369 Data Analysis: Classifiers c. Classification: Classifiers KNN: Optimize K SVM: Optimize cost, kernel, gamma LSVMRBF-SVMPSVM

32. 370 Preliminary Studies 1. Datasets Petricoin Ovarian Petricoin Prostate Adam Prostate 2. Feature Selection RFE 3. Experimental Design 10-fold nested cross-validation 4. Performance Metric ROC (rationale for selecting)

33. 371 A. Average ROC valuesClassifier Feature Selection Method All FeaturesLSVM - RFEPSVM - RFE Adam_Prostate_070102 KNN 0.844250.968630.93739 LSVM 0.994600.995950.99796 PSVM 0.996590.993160.99747 Petricoin_Ovarian_021402 KNN 0.881500.913820.79238 LSVM 0.954550.918980.85350 PSVM 0.944090.915640.84032 Petricoin_Prostate_070302 KNN 0.854980.922190.83498 LSVM 0.929810.930260.85102 PSVM 0.931210.927880.83679 B. ROC RangeClassifier Feature Selection Method All FeaturesLSVM - RFEPSVM - RFE Adam_Prostate_070102 KNN 0.58847 - 0.972630.93157 - 1.000000.87928 - 0.99413 LSVM 0.98534 - 1.000000.99022 - 1.000000.99413 - 1.00000 PSVM 0.99120 - 0.999650.98240 - 1.000000.98925 - 1.00000 Petricoin_Ovarian_021402 KNN 0.50588 - 0.995450.62273 - 1.000000.40455 - 0.96471 LSVM 0.80000 - 1.000000.49091 - 1.000000.52727 - 0.99412 PSVM 0.75455 - 1.000000.50909 - 1.000000.43636 - 0.99412 Petricoin_Prostate_070302 KNN 0.59643 - 0.996670.66190 - 1.000000.28333 - 1.00000 LSVM 0.57143 - 1.000000.57262 - 1.000000.36667 - 1.00000 PSVM 0.59881 - 1.00000 0.31667 - 1.00000 C. Number of Features Selected Number of Features Selected by Feature Selection Method All FeaturesLSVM - RFEPSVM - RFE Adam_Prostate_070102Range (Avg.) 779 - 779 (779)24 - 97 (65.2)97 - 389 (194.1) Petricoin_Ovarian_021402Range (Avg.) 15154-15154 (15154)14 - 118 (49.9)14 - 1894 (1421.9) Petricoin_Prostate_070302Range (Avg.) 15154-15154 (15154)14 - 236 (70.5)3 - 59 (16.8) Preliminary Studies: Results

34. 372 Case Study: Categorizing Text Into Content Categories Automatic Identification of Purpose and Quality of Articles In Journals Of Internal Medicine Yin Aphinyanaphongs M.S., Constantin Aliferis M.D., Ph.D. (presented in AMIA 2003)

35. 373 Case Study: Categorizing Text Into Content Categories The problem: classify Pubmed articles as [high quality & treatment specific] or not Same function as the current Clinical Quality Filters of Pubmed (in the treatment category)

36. 374

37. 375 Case Study: Categorizing Text Into Content Categories Overview: –Select Gold Standard –Corpus Construction –Document representation –Cross-validation Design –Train classifiers –Evaluate the classifiers

38. 376 Case Study: Categorizing Text Into Content Categories Select Gold Standard: –ACP journal club. Expert reviewers strictly evaluate and categorize in each medical area articles from the top journals in internal medicine. –Their mission is “to select from the biomedical literature those articles reporting original studies and systematic reviews that warrant immediate attention by physicians.” The treatment criteria -ACP journal club –“ Random allocation of participants to comparison groups. ” –“ 80% follow up of those entering study. ” –“ Outcome of known or probable clinical importance. ” If an article is cited by the ACP, it is a high quality article.

39. 377 Case Study: Categorizing Text Into Content Categories Corpus construction: 12/2000 8/1998 9/1999 Get all articles from the 49 journals in the study period. Review ACP Journal from 8/1998 to 12/2000 for articles that are cited by the ACP.  15,803 total articles, 396 positives (high quality treatment related)

40. 378 Case Study: Categorizing Text Into Content Categories Document representation: –“Bag of words” –Title, abstract, Mesh terms, publication type –Term extraction and processing: e.g. “The clinical significance of cerebrospinal.” 1.Term extraction »“The”, “clinical”, “significance”, “of”, “cerebrospinal” 2.Stop word removal »“Clinical”, “Significance”, “Cerebrospinal” 3.Porter Stemming (i.e. getting the roots of words) »“Clinic*”, “Signific*”, “Cerebrospin*” 4.Term weighting »log frequency with redundancy.

41. 379 Case Study: Categorizing Text Into Content Categories Cross-validation design 15803 articles 20% reserve 80% train validation test 10 fold cross Validation to measure error

42. 380 Case Study: Categorizing Text Into Content Categories Classifier families –Naïve Bayes (no parameter optimization) –Decision Trees with Boosting (# of iterations = # of simple rules) –Linear & Polynomial Support Vector Machines (cost from {0.1, 0.2, 0.4, 0.7, 0.9, 1, 5, 10, 20, 100, 1000}, degree from {1,2,3,5,8})

43. 381 Case Study: Categorizing Text Into Content Categories Evaluation metrics (averaged over 10 cross- validation folds): –Sensitivity for fixed specificity –Specificity for fixed sensitivity –Area under ROC curve –Area under 11-point precision-recall curve –“Ranked retrieval”

44. 382 Case Study: Categorizing Text Into Content Categories ClassifiersAverage AUC over 10 folds Range over 10 folds p-value compar ed to largest LinSVM0.965 0.948 – 0.978 0.01 PolySVM0.976 0.970 – 0.983 N/A Na ï ve Bayes 0.948 0.932 – 0.963 0.001 Boost Raw0.957 0.928 – 0.969 0.001 Boost Wght0.941 0.900 – 0.958 0.001

45. 383 Case Study: Categorizing Text Into Content Categories SensitivitySpecificityPrecision Number Needed to Read (Average) CQF0.3670.9590.1496.7 Poly SVM 0.8181 (0.641-0.93) 0.959 (0.948-0.97) 0.2816 (0.191-0.388) 3.55 SensitivitySpecificityPrecision Number Needed to Read (Average) CQF0.960.750.07114 Poly SVM 0.9673 (0.830-0.99) 0.8995 (0.884-0.914) 0.1744 (0.120-0.240) 6

46. 384 Case Study: Categorizing Text Into Content Categories Clinical Query Filter Performance 36 18 9 27 Clinical Query Filter

47. 385 Case Study: Categorizing Text Into Content Categories Clinical Query Filter

48. 386 Case Study: Categorizing Text Into Content Categories Alternative/additional approaches? –Negation detection –Citation analysis –Sequence of words –Variable selection to produce user- understandable models –Analysis of ACPJ potential bias –Others???

49. 387 Supplementary: Case Study: Imputation for Machine Learning Models For Lung Cancer Classification Using Array Comparative Genomic Hybridization C.F. Aliferis M.D., Ph.D., D. Hardin Ph.D., P. P. Massion M.D. AMIA 2002

50. 388 Case Study: A Protocol to Address the Missing Values Problem Context: –Array comparative genomic hybridization (array CGH): recently introduced technology that measures gene copy number changes of hundreds of genes in a single experiment. Gene copy number changes (deletion, amplification) are often characteristic of disease and cancer in particular. –aCGH as been shown in studies published during the last years that it enables development of powerful classification models, facilitate selection of genes for array design, and identification of likely oncogenes in a variety of cancers (e.g., esophageal, renal, head/neck, lymphomas, breast, and glioblastomas). –Interestingly a recent study (Fritz et al. June 2002) has shown that aCGH enables better classification of liposarcoma differentiation than gene expression information.

51. 389 Case Study: A Protocol to Address the Missing Values Problem Context: –While significant experience has been gathered so far in the application of various machine learning/data mining approaches to explore development of diagnostic/classification models with gene expression Microarray Data for lung cancer and of aCGH in a variety of cancers, little was known about the feasibility of using machine learning methods with aCGH data to create such models. –In this study we conducted such an experiment for the classification of non-small Lung Cancers (NSCLCs) as squamus carcinomas (SqCa) or adenocarcinomas (AdCa)). A related goal was to compare several machine learning methods in this learning task.

52. 390 Case Study: A Protocol to Address the Missing Values Problem Context: –DNA from tumors of 37 patients (21 squamous carcinomas, (SqCa) and 16 adenocarcinomas (AdCa)) were extracted after microdissection and hybridized onto a 452 BAC clone array (printed in quadruplicate) carrying genes of potential importance in cancer. –aCGH is a technology in formative stages of development. As a result a high percentage of missing values was observed in most gene measurements.

53. 391 We decided to create a protocol for gene inclusion/exclusion for analysis on the basis of three criteria: –(a) percentage of missing values, –(b) a priori importance of a gene (based on known functional role in pathways that are implicated in carcinogenesis such as the PI3- kinase pathway), and –(c) whether the existence of missing values was statistically significantly associated with the class to be predicted (at the 0.05 level and determined by a G 2 test). Case Study: A Protocol to Address the Missing Values Problem

54. 392 1. For each gene G i compute an indicator variable MG i s.t. MG i is 1 in cases where G i is missing, and 0 in cases where G i was observed 2. Compute the association of MG i to the class variable C, assoc(MG i, C) for every i. (C takes values in {SqCa, AdCa}) 3. Accept a set of important genes I 4. if assoc(MG i, C) is statistically significant then reject gene G i else if G i  I then accept G i else if fraction of missing values of G i is >15% then reject G i else accept G i 388 variables were selected according to this protocol and were imputed before analysis. Case Study: A Protocol to Address the Missing Values Problem

55. 393 K-Nearest Neighbors (KNN) method for imputation – for each instance of a gene that had a missing value the case closest to the case containing that missing value (i.e., the closest neighbor) that did have an observed value for the gene was found using Euclidean Distance (ED). That value was substituted for the missing one. –To compute distances between cases with missing values, if one of the two corresponding gene measurements was missing, the mean of the observed values for this gene across all cases was used to compute the ED component. –When both values were missing, the mean observed difference was used for the ED component. The above procedure because it is non-parametric and multivariate. More naïve approaches (such as imputing with the mean or with a random value from the observed distribution for the gene) typically produced uniformly worse models. A variant of the above method iterates the KNN imputation until convergence is attained. Case Study: A Protocol to Address the Missing Values Problem

56. 394 Important note: Clear understanding of what types of missing values we have and what are the mechanisms that generate them is required and sometimes translates into ability to effectively replace missing values as well as avoid serious biases. Example types of missing values: –Value is not produced by device or respondent etc. –Value was produced but not entered in the study database –Value was produced and entered but is deemed invalid on substantive grounds –Value was produced and entered but was subsequently corrupted due to transmission/storage or data conversion operations, etc. Example where knowing process that generates missing values may be sufficient to fill them in: physician does not measure a lab value (say x-ray) because an equivalent or more informative test has been conducted (e.g., MRI) or because it is physiologically impossible for a change to have occurred from last measurement, etc. Case Study: A Protocol to Address the Missing Values Problem