Machine Learning
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Finding accurate biomarkers is key to early diagnosis of many otherwise incurable diseases. We study the problem of finding biomarkers for mass spectrometry (SELDI-TOF) spectra from cancerous and normal tissues. In contrast to the common practice of using vague methods, such as genetic algorithms, or un-interpretable (as biomarker) methods, such as SVM, we looked for a method that is simple, intuitive, interpretable, usable, and more accurate. We introduce decision-lists to this domain. Our experiments on clinical cancer datasets show decision lists give more accurate results than other methods. More interestingly, the resulting decision lists are more interpretable, for possible causal relationship between cancer and differentially expressed proteins, and directly usable in clinical biomarker design.