Classification of Tumor Samples from Expression Data Using Decision Trunks. (January 2013)

• Record Type: Journal Article
• Authors: Ulfenborg, Benjamin; Klinga-Levan, Karin; Olsson, Björn
• Is Part Of: Cancer informatics. Volume 12(2013)
• Publication Date: 2013-01
• Subjects: classification -- machine learning -- gene expression -- biomarkers; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; Cancer -- Periodicals; Cancer -- Research -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.4137/CIN.S10356
• Languages: English
• ISSNs: 1176-9351

Abstract

We present a novel machine learning approach for the classification of cancer samples using expression data. We refer to the method as "decision trunks, " since it is loosely based on decision trees, but contains several modifications designed to achieve an algorithm that: (1) produces smaller and more easily interpretable classifiers than decision trees; (2) is more robust in varying application scenarios; and (3) achieves higher classification accuracy. The decision trunk algorithm has been implemented and tested on 26 classification tasks, covering a wide range of cancer forms, experimental methods, and classification scenarios. This comprehensive evaluation indicates that the proposed algorithm performs at least as well as the current state of the art algorithms in terms of accuracy, while producing classifiers that include on average only 2–3 markers. We suggest that the resulting decision trunks have clear advantages over other classifiers due to their transparency, interpretability, and their correspondence with human decision-making and clinical testing practices.