Deep learning has become an essential element in various applications of technology over the past decades. Deep neural networks are now reaching performance on par with, or even beyond, human-level on a broad range of tasks.  However, there are still several concerns and deficiencies that make these models impractical for some real-world applications.
One of the important issues comes from a data-efficiency perspective. Most of the deep learning techniques need a large number of training samples in order to achieve a high performance on a given problem. This procedure is far from human general intelligence. Humans are good at learning from a few number of samples and quickly adapting to new tasks.
In this work, we leverage the meta-learning framework in which the model can learn novel tasks by developing prior knowledge over past experiences. For this purpose, we propose a Meta-Dataset that contains 174 genomics and clinical tasks. Furthermore, we suggest a meta-model under the few-shot learning regime that can learn new genomics tasks. Finally, a comparison between the performance of the meta-learner and the performance of other classical baselines is also presented.