Presentation Open Access

Leveraging Open Science in Machine Learning and Bioinformatics

Batool Almarzouq

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.

This talk was presented in UK Conference of Bioinformatics and Computational Biology (UK-CBCB) on the 29th of Sept 2022
Files (3.5 MB)
Name Size
Leveraging Open Science in Machine Learning and Bioinformatics.pdf
md5:5532d85e71ab9c05d2401cf1dba17ef9
3.5 MB Download
235
168
views
downloads
All versions This version
Views 235235
Downloads 168168
Data volume 582.9 MB582.9 MB
Unique views 205205
Unique downloads 150150

Share

Cite as