P. Horvatovich

This study integrates histopathology and proteogenomic data from melanoma metastases to identify subtypes and risk factors, offering a basis for personalized treatment strategies.

This study compares quantification methods for tumor tissue phosphoproteomics and finds TMT offers highest precision, spike-in-SILAC balances features best, and LFQ provides most identifications.

This paper analyzes proteogenomic data from melanoma patients to show how metastatic melanoma alters metabolism and immune responses to evade immune detection.

The paper develops a dynamic binning method for improved peak detection in lipidomics LC-MS data and compares pre-processing tools for differential compound detection.