Christopher Blum

Artificial Intelligence Research | High Throughput Biology

Interests

My focus is to enhance understanding of complex systems in molecular biology.
To achieve this, I develop AI algorithms that learn from high throughput data.


Affiliation

Postdoc at Institute for Mathematical Modeling of Biological Systems, University of Düsseldorf, Germany.


Publications

2019 Motif search
Blum and Kollmann. Neural networks with circular filters enable data efficient inference of sequence motifs. Bioinformatics, 35(20), 3937–3943.
2018 Gene networks
Blum*, Heramvand*, Khonsari and Kollmann. Experimental noise cutoff boosts inferability of transcriptional networks in large-scale gene-deletion studies. Nature Communications, 9(133).
*first authors
2013 Pathogenesis of Ankylosing spondilitis
Seregin et al. Endoplasmic reticulum aminopeptidase-1 alleles associated with increased risk of ankylosing spondylitis reduce HLA-B27 mediated presentation of multiple antigens. Autoimmunity, 46(8), 497–508.