Bioinformatics and Computational Biology
For a full list of our publications in bioinformatics, please see the list of research articles of the lab. Below, you can find additional information (code, data, supplement) on some of these projects:
Genome-Wide Association Studies
We develop efficient multivariate approaches for the genome-wide discovery of genetic loci that are associated with a phenotype, thereby trying to elucidate the multicausal basis of complex traits.
- chevron_right Epistasis tools
- chevron_right easyGWAS: webservice for computing GWAS
- chevron_right Lasso Model with Population Structure Correction (LMM-Lasso)
- chevron_right Network GWAS (SConES)
- chevron_right Multi-Trait Network GWAS (Multi-SConES)
- chevron_right Multi-Locus Mapping of Genetic Heterogeneity (FAIS)
- chevron_right Missing Phenotype Imputation/Prediction via Co-Training
- chevron_right Genome-wide genetic heterogeneity discovery with categorical covariates (FastCMH)
- chevron_right Combinatorial Association Mapping (CASMAP)
- chevron_right AraGWAS and AraPheno
Genome Annotation
We developed methods for detecting genomic insertions and deletions, and thoroughly assessed the difficulty of comparing the performance of pathogenicity prediction tools.
Molecular Graph Classification
We developed new, fast and scalable similarity measures on graphs, so-called graph kernels. Their prime purpose is to compare molecular graphs or protein structures and to classify them into functional categories.