@misc{zhapacamacho2026hottnesy,
      title={A homotopy-type-theoretic generalization of neurosymbolic inference},
      author={Fernando Zhapa-Camacho and Robert Hoehndorf},
      year={2026},
      eprint={2606.17851},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

@article{Zhapa_Camacho_2026_indigena,
  title={INDIGENA: inductive prediction of disease–gene associations using phenotype ontologies},
  volume={42},
  ISSN={1367-4811},
  url={http://dx.doi.org/10.1093/bioinformatics/btag325},
  DOI={10.1093/bioinformatics/btag325},
  number={6},
  journal={Bioinformatics},
  publisher={Oxford University Press (OUP)},
  author={Zhapa-Camacho, Fernando and Hoehndorf, Robert},
  editor={Robinson, Peter},
  year={2026},
  month=May
}

@inbook{Zhapa_Camacho_2026_geometre,
  title={Fully Geometric Multi-hop Reasoning on Knowledge Graphs with Transitive Relations},
  ISBN={9783032251565},
  ISSN={1611-3349},
  url={http://dx.doi.org/10.1007/978-3-032-25156-5_14},
  DOI={10.1007/978-3-032-25156-5_14},
  booktitle={The Semantic Web},
  publisher={Springer Nature Switzerland},
  author={Zhapa-Camacho, Fernando and Hoehndorf, Robert},
  year={2026},
  pages={258–277}
}

@inproceedings{Zhapa_Camacho_2025,
title={LLM Agent Based Protein Function Prediction},
url={http://dx.doi.org/10.1142/9789819824755_0036},
DOI={10.1142/9789819824755_0036},
booktitle={Biocomputing 2026},
publisher={WORLD SCIENTIFIC},
author={Zhapa-Camacho,
Fernando and Mashkova, Olga and Hoehndorf, Robert and Kulmanov, Maxat},
year={2025},
month=dec,
pages={508–519} }

@article{Zhapa_Camacho_2025,
title={Lattice-Based ALC Ontology Embeddings With Saturation},
volume={1},
ISSN={2949-8732},
url={http://dx.doi.org/10.1177/29498732251340186},
DOI={10.1177/29498732251340186},
journal={Neurosymbolic Artificial Intelligence},
publisher={SAGE Publications},
author={Zhapa-Camacho, Fernando and Hoehndorf, Robert},
year={2025},
month=jun }

@article{Chen_2025,
  title={Ontology Embedding: A Survey of Methods, Applications and Resources},
  ISSN={2326-3865},
  url={http://dx.doi.org/10.1109/TKDE.2025.3559023},
  DOI={10.1109/tkde.2025.3559023},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  publisher={Institute of Electrical and Electronics Engineers (IEEE)},
  author={Chen, Jiaoyan and Mashkova, Olga and Zhapa-Camacho, Fernando and Hoehndorf, Robert and He, Yuan and Horrocks, Ian},
  year={2025},
  pages={1–20}
}

@inbook{Hoehndorf_2025,
title={Neuro-Symbolic AI in Life Sciences},
ISBN={9781643685793},
ISSN={1879-8314},
url={http://dx.doi.org/10.3233/FAIA250239},
DOI={10.3233/faia250239},
booktitle={Handbook on Neurosymbolic AI and Knowledge Graphs},
publisher={IOS Press},
author={Hoehndorf,
Robert and Pesquita, Catia and Zhapa-Camacho, Fernando},
year={2025}, month=mar }

@InProceedings{10.1007/978-3-031-71167-1_19,
author="Zhapa-Camacho, Fernando
and Hoehndorf, Robert",
editor="Besold, Tarek R.
and d'Avila Garcez, Artur
and Jimenez-Ruiz, Ernesto
and Confalonieri, Roberto
and Madhyastha, Pranava
and Wagner, Benedikt",
title="Lattice-Preserving {\$}{\$}{\backslash}mathcal {\{}ALC{\}}{\$}{\$}Ontology Embeddings",
booktitle="Neural-Symbolic Learning and Reasoning",
year="2024",
publisher="Springer Nature Switzerland",
address="Cham",
pages="355--369",
isbn="978-3-031-71167-1"
}

@article {Zhapa-Camacho2024.01.28.577662,
	  author = {Fernando Zhapa-Camacho and Zhenwei Tang and Maxat Kulmanov and Robert Hoehndorf},
          title = {Predicting protein functions using positive-unlabeled ranking with ontology-based priors},
          elocation-id = {2024.01.28.577662},
          year = {2024},
          doi = {10.1101/2024.01.28.577662},
          publisher = {Cold Spring Harbor Laboratory},
          abstract = {Automated protein function prediction is a crucial and widely studied problem in bioinformatics. Computationally, protein function is a multilabel classification problem where only positive samples are defined and there is a large number of unlabeled annotations. Most existing methods rely on the assumption that the unlabeled set of protein function annotations are negatives, inducing the false negative issue, where potential positive samples are trained as negatives. We introduce a novel approach named PU-GO, wherein we address function prediction as a positive-unlabeled ranking problem. We apply empirical risk minimization, i.e., we minimize the classification risk of a classifier where class priors are obtained from the Gene Ontology hierarchical structure. We show that our approach is more robust than other state-of-the-art methods on similarity-based and time-based benchmark datasets. Data and code are available at https://github.com/bio-ontology-research-group/PU-GO.Competing Interest StatementThe authors have declared no competing interest.},
        URL = {https://www.biorxiv.org/content/early/2024/01/31/2024.01.28.577662},
        eprint = {https://www.biorxiv.org/content/early/2024/01/31/2024.01.28.577662.full.pdf},
        journal = {bioRxiv}
}

@inproceedings{zhapa2023axioms,
  author       = {Fernando Zhapa{-}Camacho and
                  Robert Hoehndorf},
  editor       = {Artur S. d'Avila Garcez and
                  Tarek R. Besold and
                  Marco Gori and
                  Ernesto Jim{\'{e}}nez{-}Ruiz},
  title        = {From Axioms over Graphs to Vectors, and Back Again: Evaluating the
                  Properties of Graph-based Ontology Embeddings},
  booktitle    = {Proceedings of the 17th International Workshop on Neural-Symbolic
                  Learning and Reasoning, La Certosa di Pontignano, Siena, Italy, July
                  3-5, 2023},
  series       = {{CEUR} Workshop Proceedings},
  volume       = {3432},
  pages        = {85--102},
  publisher    = {CEUR-WS.org},
  year         = {2023},
  url          = {https://ceur-ws.org/Vol-3432/paper7.pdf},
  timestamp    = {Tue, 11 Jul 2023 17:14:10 +0200},
  biburl       = {https://dblp.org/rec/conf/nesy/Zhapa-CamachoH23.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

@article{10.1093/bioinformatics/btac811,
	author = {Zhapa-Camacho, Fernando and Kulmanov, Maxat and Hoehndorf, Robert},
    	title = "{mOWL: Python library for machine learning with biomedical ontologies}",
    	journal = {Bioinformatics},
    	year = {2022},
    	month = {12},
    	issn = {1367-4803},
    	doi = {10.1093/bioinformatics/btac811},
    	url = {https://doi.org/10.1093/bioinformatics/btac811},
    	note = {btac811},
   	}