Marija Buljan

This study identifies molecular programs in macrophages stimulated by tumor-derived cues, linking them to clinically relevant TAM states and functions.

NOODAI is a webserver that integrates multi-omics data through network analysis to identify disease-relevant modules and provide analytical outputs.

RIPPLET converts somatic DNA variants into gene-impact scores and pathway-perturbation profiles, outperforming multi-omic tools in identifying cancer drivers and predicting drug response without RNA data.

This study uses micro-CT radiomics to classify thyroid tumors and identify biomarkers, showing potential for improving diagnostic accuracy.

This study identifies key kinases and transcription factors regulating immunosuppressive macrophages and validates proteomic signatures for detecting pro-inflammatory macrophages in tumors.

This study uses AI-based image analysis and multi-omics profiling to link morphological features with molecular changes in ccRCC, identifying unique molecular profiles in aggressive rhabdoid tumors.

This study uses proteomic and phosphoproteomic analysis to classify thyroid tumors of follicular origin, identifying molecular signatures that distinguish benign from malignant tissues.

PCfun annotates protein complex function using natural language processing and machine learning, integrating word embeddings from PubMed Central articles with supervised and unsupervised methods.