• Artificial Intelligence in Medicine

Invited SpeakersProfile Details

Bjorn Usadel
Bjorn Usadel Professor at RWTH Aachen University


Björn Usadel studied Biochemistry at the Free University Berlin and conducted his MSc research on Drosophila at the Rockefeller University.  Upon returning to Germany in 2001, he conducted his PhD studies in the lab of Dr. Markus Pauly at the Max Planck Institute of Molecular Plant Physiology, Potsdam on identifying and characterizing novel cell wall genes using bioinformatics approaches.

After a postdoc and group leader position with Prof Mark Stitt at the same institute, he became full professor and director at RWTH Aachen University and the Research Center Jülich in 2011. He has co-authored more than 120 publications and is an ISI highly cited researcher in Plant and Animal Sciences.

All sessions by Bjorn Usadel

  • Day 1Monday, February 18th
Session 1 : Opportunities and Challenges for AI in Medicine (Chair - Takashi Gojobori)
11:35 am

Learning gene functions based on expression: Know what you ask for

Recent updates in sequencing technology have made it possible to start looking at pangenomics questions comparing amongst others gene/presence absence variations in individuals or populations. However when doing so one frequently encounters that many genes lack a functional ontology. Many of these “unknown genes” exist in multiple species, but have not been characterized in any.
Typical approaches to predict the function of those genes comprise phylogenetical profiling and/or to analyze their expression behavior across a large set of experiments. The latter analysis had been perfected for microarray data taking into account sample selection to find genes associated with specific processes, but performance of the same method using modern RNASeq data might have lagged behind. In any case the application of adaptive performance tuning of RNASeq data was hindered by computation intensive procedures for RNASeq data analysis. This issues has been addressed by novel pseudomapping approaches such as those implemented in kallisto/salmon allowing individuals to analyze large expression data matrices. We present data showing that the careful selection of training data positively affects the performance of the outcome and show the effect of novel and fast RNASeq analysis pipelines on gene function predictions using biomedical ontologies.

Building 9 - Lecture hall 2 11:35 - 12:10 Details