A deep learning Swiss Army knife for genomics and multi-omics
Name: Carl Nettelblad
Project title: A deep learning Swiss Army knife for genomics and multi-omics
Department: Department of Information Technology
Area of research: Scientific Computing
Neural networks have proven immensely successful in analysing image, video, and text data. So far, success has been more limited for genomic data. Together with my PhD student, I have created new adapted networks for genomic data. So far, we have applied those to questions in population genetics, showing how they can learn to identify the geographic origin of individuals with greater accuracy than the prevailing method of principal-component analysis (PCA). However, what’s most exciting about these networks is that they are general.
In conventional bioinformatics, each data type and problem formulation requires fully unique algorithms, but again, the examples from image analysis show how transfer learning can be used to adapt a network to a new problem. During the year, we will explore how our foundational network design can be adapted to address different genomic questions and even include proteomic and transcriptomic data.
What do you look forward to the most during your sabbatical?
I look forward to the new research ideas and renewed approaches to existing ones that will be possible in a truly cross-disciplinary environment.