Clinically Inspired Endocrine Signaling Simulation Dataset for ML-Driven Source Localization in Molecular Communication

Description This record contains the simulation data used in the work "Toward Clinically-Inspired Validation of ML-Driven Source Localization in Molecular Communication." The dataset supports a 2-page abstract submitted to the 10th Workshop on Molecular Communications (MolCom 2026), Istanbul, Turkey, 15-17 April 2026. This work extends our prior Zenodo record, DOI: 10.5281/zenodo.15408120. The archive includes simulation runs generated with the MEHLISSA simulator and associated files used for ML-based source localization experiments. Short Description of the Paper Building on our prior work on source localization in the human circulatory system (HCS), this work-in-progress paper extends the framework to a clinically relevant endocrine-signaling setting for tumor-source localization. Specifically, it models endocrine signaling and translates adrenal venous sampling (AVS)-based clinical measurement into MELISSA simulations. The adrenal glands are modeled as hormone (aldosterone and cortisol) secreting transmitters, while the HCS serves as the communication channel and the adrenal veins and inferior vena cava act as passive receivers analogous to clinical AVS setting. Continuous aldosterone and cortisol secretion, together with advection- and diffusion-driven transport, generates receiver-side time-series concentration signals that are used as inputs to the machine leraning (ML) model. The resulting dataset enables validation of our earlier stacked ensemble ML model using LightGBM and BernoulliNB in this clinically grounded setting. Data and Code We publish our simulation data and the Python code to process it as described in the paper here on Zenodo under the CC BY and MIT licenses, respectively. Contact If you have any questions or suggestions for improvements, feel free to contact me. Saswati Pal Email: [e-mail]