Computational Challenges and Artificial Intelligence in Precision Medicine.

Afanasiev, Olga; Berghout, Joanne; Brenner, Steven E; Bulyk, Martha L; Crawford, Dana C; Chen, Jonathan H; Daneshjou, Roxana; Kidziński, Łukasz

Continuously decreasing cost, speed and efficiency of DNA and RNA sequencing, coupled with advances in real-world sensing, storage of electronic health records, publicly available databases, and new data processing techniques enable precision medicine at unprecedented scale. Machine learning and artificial intelligence emerge naturally as tools for analyzing and summarizing data, supporting clinical decisions with data-driven insights and further unlocking genetically driven mechanisms underlying individualized risk. While these computational tools allow modeling of complex relations in large datasets, they pose new challenges especially because a patient's health is at stake. Due to an often black-box nature and high reliance on the training data, these new tools are prone to biases and most commonly provide correlational rather than causal insights. Results of these analyses have been difficult to validate, interpret, and explain to practitioners, and most genetic studies have struggled to encompass the full spectrum of human diversity. In this work, we summarize recent research trends in addressing these issues with examples from submissions to the "Computational Challenges and Artificial Intelligence in Precision Medicine" session at Pacific Symposium on Biocomputing 2021. We observe growing research interest in identifying biases, deriving causal and interpretable relations, tuning parameters of models for production, and using artificial intelligence for quality control. We expect further upsurge in work on interpretability and low-risk applications of advanced computational tools.