Interpretable Machine Learning for life science data

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The recent surge of interest in artificial intelligence (AI) applications has brought to light several new opportunities for the Canadian industries. Although several successful applications have recently emerged in many key sectors, important hindrances remain to be addressed in other sectors such as life science and bioinformatics, where innovations may have significant social and ethical impacts. One such issue is interpretability. Indeed, most AI applications are developed with the Machine Learning (ML) paradigm, which consists in designing algorithms to learn a task by themselves rather than explicitly programming the solution. For harder tasks, we often grant the learning algorithm with greater capacity of transforming the data to a suitable representation. Although this permits the task to be performed, it comes at a cost: there exists an inverse relationship between the capacity and the effective interpretability of the model, potentially making it an opaque model difficult to understand by a human. Motivations for interpretable ML methods are numerous: build trust by making the model challengeable, contribute to acceptance by explaining decisions, serve as a diagnostic tool to drive future data collections, help in certification processes to uncover corner cases, assess the presence of undesired biases in the model to ensure fairness and reveal obfuscated decision mechanisms for knowledge discovery. All these aspects are crucial and are part of the long-term goal of the research program to reach a unified methodology and general understanding of ML interpretability. The field of bioinformatics and life science in general offers great potential for research on ML interpretability. In fact, the field is facing some specific realities that make the problems more difficult than in other areas where ML is already fruitful. For instance, the amount of available data can be limited and costly to gather; the data might originate from different labs, making the fusion of multiple dataset a nontrivial task; over- and under-representation of some populations can lead to unacceptable biases that must be identified and compensated; and potential discoveries made by ML algorithms must be humanly intelligible to be used. The present project proposes to address these issues by developing specialized ML methods by covering the bias, diagnostic and knowledge discovery aspects of interpretability. It is expected that advances in ML interpretability will contribute to the general acceptance and trust of using AI in life science. The initial impact will be to promote the use of ML to accelerate life science research by making it more cost effective and scalable. The improved practices will eventually foster the development of AI applications that will be beneficial to Canadians once introduced in the healthcare systems.