Bence Bolgár

TL;DR: The substantially fewer number ofComorbidity relations in the BDMM compared to pairwise methods implies that biologically meaningful comorbid relations may be less frequent than earlier pairwise method suggested.

TL;DR: This novel method had significantly higher sensitivity and precision than the individual variant callers in all target region sizes, and it was demonstrated that VariantMetaCaller supports a quantitative, precision based filtering of variants under wider conditions.

TL;DR: This work summarizes the application of a computational repurposing method based on statistically rooted knowledge fusion and provides a step-by-step guide to the complete workflow, together with a case study in Parkinson's disease.

TL;DR: Variational Bayesian Multiple Kernel Logistic Matrix Factorization (VB-MK-LMF), which unifies the advantages of multiple kernel learning, weighted observations, graph Laplacian regularization, and explicit modeling of probabilities of binary drug-target interactions achieves significantly better predictive performance in standard benchmarks compared to state-of-the-art methods.

TL;DR: The results confirmed that kernel-based data fusion can integrate heterogeneous information sources significantly better than standard rank-based fusion can, and this method provides a unique solution for repositioning; it can also be utilized for de novo drug discovery.