Author

Luca Arrigo Zammataro

The University of Texas MD Anderson Cancer CenterORCID 0000-0002-4348-6341Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Immunology· 1 Systems Biology· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 2 Research Focus:benchmarking· 1 Approach:biophysical modeling· 1 Approach:deep learning· 1 Research Focus:dimensionality reduction· 1 Research Focus:drug response / drug discovery· 1 Approach:experimental assay· 1 Organism:human· 1

Frequent Co-Authors

Duncheng WangiD· 1 shared Eduardo Cisneros de la Rosa· 1 shared Ho NgaiiD· 1 shared Jason Thomas GeorgeiD· 1 shared Jeffrey J. MolldremiD· 1 shared Noah J. TuboiD· 1 shared Priya KoppikariD· 1 shared Qiuming He· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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1 shared paperImmunology

Duncheng WangORCID

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Author profile →

3 papers

Recent·Most cited

Biophysical modeling for accurate T cell specificity prediction of viral and tumor antigens

Zahra S. Ghoreyshi, Noah J. Tubo, Luca Arrigo Zammataro, Xizeng Mao, Ho Ngai, Duncheng Wang, Yibin Chen, Qiuming He, Eduardo Cisneros de la Rosa, Shoudan Liang, Priya Koppikar, Xingcheng Lin, Jeffrey J. Molldrem, Jason Thomas George

The paper develops a biophysical model to predict T cell receptor specificity for viral and tumor antigens using structural data and sequence-based clustering.

Nature Communications · Jun 23, 2026Peer-reviewedImmunology

A Minimal Regulatory Spatial Model for Emergent Multicellular Organization in Dissipative Environments

Luca Arrigo Zammataro

The paper introduces Evoscope, a computational framework that generates multicellular organization from minimal regulatory rules and demonstrates that morphology encodes internal regulatory dynamics through representation learning.

bioRxiv · May 4, 2026 · PreprintSystems Biology

Molecular Learning of a Soft-Disks Fluid

Luca Arrigo Zammataro

This paper demonstrates that a Lennard-Jones fluid can be trained using logic-gate patterns, showing its ability to learn and predict outputs through intrinsic minimizing properties.

bioRxiv · Jul 24, 2021 · PreprintBioinformatics