Literature Database Entry

pal2026tumor

Saswati Pal, Regine Wendt, Cyrus Khandanpour, Malte Sieren, Stefan Fischer and Falko Dressler, "Toward Clinically-Inspired Validation of ML-Driven Source Localization in Molecular Communication," Proceedings of 10th Workshop on Molecular Communications (MolCom 2026), Istanbul, Turkey, April 2026.

Abstract

Accurate localization of tumor sources in the human circulatory system is essential for precision oncology. In prior work, we developed a machine learning (ML) framework to localize anomaly sources using temporal biomarker profiles measured at receiver sites. However, the dataset was generated in a generic source-receiver setting, limiting physiological realism. This work-in-progress paper extends the framework by validating the ML model on a clinically-inspired dataset that emulates endocrine signaling in a controlled synthetic environment. The model is retrained and evaluated using a stratified held-out split with 25% reserved for testing. Preliminary results show an accuracy of 90%, indicating the potential of ML-driven approaches for tumor source localization in clinically relevant molecular communication settings.

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Saswati Pal
Regine Wendt
Cyrus Khandanpour
Malte Sieren
Stefan Fischer
Falko Dressler

BibTeX reference

@inproceedings{pal2026tumor,
    author = {Pal, Saswati and Wendt, Regine and Khandanpour, Cyrus and Sieren, Malte and Fischer, Stefan and Dressler, Falko},
    title = {{Toward Clinically-Inspired Validation of ML-Driven Source Localization in Molecular Communication}},
    address = {Istanbul, Turkey},
    booktitle = {10th Workshop on Molecular Communications (MolCom 2026)},
    month = {4},
    year = {2026},
   }
   
   

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Last modified: 2026-04-25