Leveraging Open Science in Machine Learning and Bioinformatics

Biology has become a rich, data-intensive science dependent on complex, computational, and statistical methods, where machine learning and deep learning algorithms can be leveraged to provide novel insights for complex biological questions.  Open Science has been instrumental in making these methods accessible to researchers while ensuring scientific results remain reproducible. This talk will discuss how open science practices can boost bioinformatics research and open new avenues for promoting scientific discovery by extending the principle of openness to the whole research cycle.

We will explore examples of how Open Science is applied in the field of machine learning and computational biology. We will also look into both the challenges and advantages of applying Open Science practices in the ever-evolving field of machine learning. We aim to prompt attendees to reflect on specific concerns and practices that impact data science and bioinformatics research. We will be using a shared document for collaborative note-taking to capture the diverse experience of the participants and derive actions to help more communities adopt open science practices.

The Slides can be accessed here.