  The key to success in machine learning (ML) is the use of effective data
representations. Traditionally, data representations were hand-crafted.
Recently it has been demonstrated that, given sufficient data, deep neural
networks can learn effective implicit representations from simple input
representations. However, for most scientific problems, the use of deep
learning is not appropriate as the amount of available data is limited, and/or
the output models must be explainable. Nevertheless, many scientific problems
do have significant amounts of data available on related tasks, which makes
them amenable to multi-task learning, i.e. learning many related problems
simultaneously. Here we propose a novel and general representation learning
approach for multi-task learning that works successfully with small amounts of
data. The fundamental new idea is to transform an input intrinsic data
representation (i.e., handcrafted features), to an extrinsic representation
based on what a pre-trained set of models predict about the examples. This
transformation has the dual advantages of producing significantly more accurate
predictions, and providing explainable models. To demonstrate the utility of
this transformative learning approach, we have applied it to three real-world
scientific problems: drug-design (quantitative structure activity relationship
learning), predicting human gene expression (across different tissue types and
drug treatments), and meta-learning for machine learning (predicting which
machine learning methods work best for a given problem). In all three problems,
transformative machine learning significantly outperforms the best intrinsic
representation.
