A deep learning Swiss Army knife for genomics and multi-omics

Project title: A deep learning Swiss Army knife for genomics and multi-omics

Department: Department of Information Technology

Area of research: Scientific Computing

Project summary

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.

Two diagrammes. One showing PCA (principle component analysis) of populations where population dots are spread out along two lines, one nearly vertical and one slanted to the right, meeting near the x axis. The second diagramme shows populations analysed using an autoencoder. Here the dots are grouped together mimicking their geographical locations. — PCA clustering of populations (top) compared to that of our autoencoder (bottom).