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Over the last few years, AICML researchers (including PIs, PDFs, students and research programmers) have been actively engaged with various teams of medical researchers, exploring ways to produce classifiers that can make accurate predictions about future patients.   We seek ways to learn these predictors from historical data, often augmented with other prior biological data (such as metabolic or signaling pathways).  This requires addressing many challenges, such as missing and censored data, "large p, small n", and integrating diverse types of information. These projects involve using various information about a patient with the goal of predicting some relevant property of that patient. We seek ways to "learn" these predictors, from historical data, often augmented with other prior biological data (such as metabolic or signaling pathways).