The spectrum of modern molecular high-throughput assaying includes diverse technologies such as microarray gene expression, miRNA expression, proteomics, DNA methylation, among many others. Now that these technologies have matured and become increasingly accessible, the next frontier is to collect “multi-modal” data for the same set of subjects and conduct integrative, multi-level analyses. While multi-modal data does contain distinct biological information that can be useful for answering complex biology questions, its value for predicting clinical phenotypes and contributions of each type of input remain unknown. We obtained 47 datasets/predictive tasks that in total span over 9 data modalities and executed analytic experiments for predicting various clinical phenotypes and outcomes. First, we analyzed each modality separately using uni-modal approaches based on several state-of-the-art supervised classification and feature selection methods. Then, we applied integrative multi-modal classification techniques. We have found that gene expression is the most predictively informative modality. Other modalities such as protein expression, miRNA expression, and DNA methylation also provide highly predictive results, which are often statistically comparable but not superior to gene expression data. Integrative multi-modal analyses generally do not increase predictive signal compared to gene expression data.
An important aspect to performing text categorization is selecting appropriate supervised classification and feature selection methods. A comprehensive benchmark is needed to inform best practices in this broad application field. Previous benchmarks have evaluated performance for a few supervised classification and feature selection methods and limited ways to optimize them. The present work updates prior benchmarks by increasing the number of classifiers and feature selection methods order of magnitude, including adding recently developed, state‐of‐the‐art methods. Specifically, this study used 229 text categorization data sets/tasks, and evaluated 28 classification methods (both well‐established and proprietary/commercial) and 19 feature selection methods according to 4 classification performance metrics. We report several key findings that will be helpful in establishing best methodological practices for text categorization.
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