However, high accuracy often comes at the price of loss of interpretability, i.e. many of these models are built as black-boxes that fail to provide new biological insights.
This tutorial focuses on illustrating some of the recent advancements in the field of Interpretable Artificial Intelligence. We will show how explainable, smaller models can achieve similar levels of performance than cumbersome ones, while shedding light on the underlying biological principles driving model decisions.
We will demonstrate how to build and extract knowledge using interpretable approaches in different domains of computational biology, including analysis of single-cell data, functional sequences of raw DNA, and drug sensitivity prediction models.
The choice of these applications is motivated by the availability of adequately large datasets that can support deep learning (DL) approaches and by their high relevance for personalized medicine. We will exploit both publicly available deep learning models as well as in-house developed models.