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Current machine learning has made great progress on computer vision and manyother fields attributed to the large amount of high-quality training samples,while it does not work very well on genomic data analysis, since they arenotoriously known as small data. In our work, we focus on few-shot diseasesubtype prediction problem, identifying subgroups of similar patients that canguide treatment decisions for a specific individual through training on smalldata. In fact, doctors and clinicians always address this problem by studyingseveral interrelated clinical variables simultaneously. We attempt to simulatesuch clinical perspective, and introduce meta learning techniques to develop anew model, which can extract the common experience or knowledge frominterrelated clinical tasks and transfer it to help address new tasks. Our newmodel is built upon a carefully designed meta-learner, called PrototypicalNetwork, that is a simple yet effective meta learning machine for few-shotimage classification. Observing that gene expression data have specificallyhigh dimensionality and high noise properties compared with image data, weproposed a new extension of it by appending two modules to address theseissues. Concretely, we append a feature selection layer to automatically filterout the disease-irrelated genes and incorporate a sample reweighting strategyto adaptively remove noisy data, and meanwhile the extended model is capable oflearning from a limited number of training examples and generalize well.Simulations and real gene expression data experiments substantiate thesuperiority of the proposed method for predicting the subtypes of disease andidentifying potential disease-related genes.

Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction