BACKGROUND: Personalized and precision medicine aim to identify predictive biomarkers from patient-specific proteogenomic profiles and to uncover tailored therapeutic strategies by targeting deregulated proteins driving disease phenotypes. METHODS: To address these challenges, we developed MultiOmicsXplorer, a freely available and interactive R-based Shiny application designed to facilitate the exploration and analysis of multi-omics cancer datasets with a user-friendly interface. At its core, MultiOmicsXplorer builds on our recently developed SignalingProfiler pipeline to extract protein activities from proteogenomic data, thereby reducing data complexity and dimensionality while enabling mechanistic hypothesis generation. RESULTS: The application integrates two core functionalities. The first, OncoXplorer, enables the systematic inference and comparison of protein activities from 1492 samples corresponding to approximately 1000 patients across ten cancer types, leveraging harmonized datasets from the CPTAC portal to provide a functional and mechanistic interpretation of multi-layered data. Importantly, beyond activity estimation, it allows for comparative analysis of transcriptomic, proteomic, and phosphoproteomic data. The second module, Extract Protein Activity from Your Data, enables users to infer the activity of kinases, phosphatases, and transcription factors from custom multi-modal datasets. CONCLUSIONS: Overall, MultiOmicsXplorer facilitates the exploration and interpretation of CPTAC multi-omics cancer datasets, supporting comparative analyses and protein activity inference within a unified and user-friendly environment.

SUMMARY: We present NetworkCommons, a platform for integrating prior knowledge, omics data, and network inference methods, facilitating their usage and evaluation. NetworkCommons aims to be an infrastructure for the network biology community that supports the development of better methods and benchmarks, by enhancing interoperability and integration. AVAILABILITY AND IMPLEMENTATION: NetworkCommons is implemented in Python and offers programmatic access to multiple omics datasets, network inference methods, and benchmarking setups. It is a free software, available at https://github.com/saezlab/networkcommons, and deposited in Zenodo at https://doi.org/10.5281/zenodo.14719118.