Extending Classification Algorithms to Case-Control Studies

Abstract

Classification is a common technique applied to ’omics data to build predictive models and identify potential markers of biomedical outcomes. Despite the prevalence of case-control studies, the number of classification methods available to analyze data generated by such studies is extremely limited. Conditional logistic regression is the most commonly used technique, but the associated modeling assumptions limit its ability to identify a large class of sufficiently complicated ’omic signatures. We propose a data preprocessing step which generalizes and makes any linear or nonlinear classification algorithm, even those typically not appropriate for matched design data, available to be used to model case-control data and identify relevant biomarkers in these study designs. We demonstrate on simulated case-control data that both the classification and variable selection accuracy of each method is improved after applying this processing step and that the proposed methods are comparable to or outperform existing variable selection methods. Finally, we demonstrate the impact of conditional classification algorithms on a large cohort study of children with islet autoimmunity.

Prediction of type 1 diabetes at birth: cord blood metabolites versus genetic risk score in the MoBa cohort

“…Stanfill et al used classification algorithms to determine the most predictive features of islet autoimmunity in the TEDDY study (504 samples) and reported adipic acid, creatinine, and leucine as influential metabolites. [44] Webb-Robertson et al used a machine learning approach to predict islet autoimmunity using data from the TEDDY study (157 case-control pairs), reporting azelaic acid and adipic acid as important features. [45] Frohnert et al predicted seroconversion to islet autoimmunity in the DAISY study (22 cases and 25 controls) and reported 3-methyl-oxobutyrate (a precursor to valine for leucine synthesis) and pyroglutamatic acid (a derivative of glutamic acid) as features that were often selected by the algorithm used.…”

Prediction of Type 1 Diabetes at Birth: Cord Blood Metabolites vs Genetic Risk Score in the Norwegian Mother, Father, and Child Cohort

“…There are also 2 publications from TEDDY and 1 from the Diabetes Autoimmunity Study in the Young (DAISY) using polar metabolites to predict later islet autoimmunity. Stanfill et al used classification algorithms to determine the most predictive features of islet autoimmunity in the TEDDY study (504 samples) and reported adipic acid, creatinine, and leucine as influential metabolites ( 45 ). Webb-Robertson et al used a machine learning approach to predict islet autoimmunity using data from the TEDDY study (157 case-control pairs), reporting azelaic acid and adipic acid as important features ( 46 ).…”

Quantitative Radiomic Features from Computed Tomography Can Predict Pancreatic Cancer up to 36 Months Before Diagnosis

“…Conditional support vector machine (SVM) 30 was applied to develop prediction algorithms based on DS1. The reason for using “ conditional” SVM is to account for the paired structure of the matched data.…”

“…More specifically, we centered the within pair data by its mean. 30 For example, for a specific feature, the values were 0.4 and 0.6, respectively, from the images of a case and one of his/her controls, the centered values became -0.1 and 0.1. SVM is a high-performing non-linear classifier to map input data into higher dimensional space with the purpose of better ability of data separation 31 Moreover, SVM can ignore outliers.…”