Vladimir B. Bajić

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

The paper identifies widespread transcription initiation at microsatellites across species and develops deep learning models to predict STR-associated transcription and its impact on disease variants.

The paper identifies LD motifs in the human proteome, reveals their role in cell adhesion and migration, and traces their evolutionary origin to a common ancestor.

The paper presents a large-scale cancer pharmacogenomic screen with 11,576 experiments on 910 drug combinations across 85 cancer cell lines, enabling crowd-sourced prediction of synergistic drug pairs and biomarkers.

This study characterizes long non-coding RNAs (lncRNAs) by analyzing feature relationships and introduces LGC, an algorithm that accurately identifies lncRNAs across species without prior knowledge.

The paper introduces TELS, a computational method to identify cell type/tissue specific motif signatures of transcribed enhancers, and compiles a catalog of these signatures across 112 human primary cells and tissues.

The paper identifies shared expression patterns of genetic variants across tissues and cell types, enabling fine mapping of disease-associated variants and revealing key cell types involved in disease pathogenesis.

The paper identifies CpG traffic lights as markers of regulatory regions by showing their correlation with gene expression and evolutionary conservation.