  Current machine learning has made great progress on computer vision and many
other fields attributed to the large amount of high-quality training samples,
while it does not work very well on genomic data analysis, since they are
notoriously known as small data. In our work, we focus on few-shot disease
subtype prediction problem, identifying subgroups of similar patients that can
guide treatment decisions for a specific individual through training on small
data. In fact, doctors and clinicians always address this problem by studying
several interrelated clinical variables simultaneously. We attempt to simulate
such clinical perspective, and introduce meta learning techniques to develop a
new model, which can extract the common experience or knowledge from
interrelated clinical tasks and transfer it to help address new tasks. Our new
model is built upon a carefully designed meta-learner, called Prototypical
Network, that is a simple yet effective meta learning machine for few-shot
image classification. Observing that gene expression data have specifically
high dimensionality and high noise properties compared with image data, we
proposed a new extension of it by appending two modules to address these
issues. Concretely, we append a feature selection layer to automatically filter
out the disease-irrelated genes and incorporate a sample reweighting strategy
to adaptively remove noisy data, and meanwhile the extended model is capable of
learning from a limited number of training examples and generalize well.
Simulations and real gene expression data experiments substantiate the
superiority of the proposed method for predicting the subtypes of disease and
identifying potential disease-related genes.
