How is machine learning and deep learning used across bioinformatics? Do all ML models necessarily need to be explainable? How can trust from end-users of ML powered applications be fostered? In this virtual panel discussion, first of a series (see box), invited speakers present use cases and perspectives on ML from biocuration, digital pathology, biomarker discovery and algorithm development. One transversal message: the importance of good data science relying on domain expertise, and of the interaction between human and machine intelligence.
From challenges to fostering end-users’ trust
The discussion will also take you further in addressing some of the specificities of ML and the challenges it represents. Speakers will share their perspectives on the importance of knowing and getting the right data – and the role of annotation and structuring to obtain representative datasets in this context. They will then discuss the need (or not) for a model to be explainable, depending on the context of use or application, and how to get there.
Finally, the discussion will include a brief ‘reality-check’ to have in mind when ML models become applications routinely used by end-users – from clinicians and life scientists to biocurators – with ‘trust’ being the key word.
The take-home message? As Aitana Lebrand puts it: “Good data science is at the core of bioinformatics: strong scripting and statistical skills, combined with domain experts who have the substantive expertise to curate and make sense of the data. These are the key ingredients to ensure the trust of our end-users, from clinicians to life scientists and chemists.”