Academic Publications by Cen Wan

(* corresponding author)

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    Preprint

  1. Alsaggaf, I. and Wan, C. *
    Functional yeast promoter sequence design using temporal convolutional generative language models
    bioRxiv, 2024.
    DOI: 10.1101/2024.10.22.619701. Preprint

    2. Research-oriented Books

  2. Wan, C.
    Hierarchical Feature Selection for Knowledge Discovery: Application of Data Mining to the Biology of Ageing
    Springer, 2019. ISBN: 978-3-319-97918-2. Publisher's webpage

    3. Journals

  3. Alsaggaf, I., Freitas, A.A. and Wan, C. *
    Predicting the pro-longevity or anti-longevity effect of model organism genes with enhanced Gaussian noise augmentation-based contrastive learning on protein–protein interaction networks
    NAR Genomics and Bioinformatics, lqae153, 2024.
    DOI: 10.1093/nargab/lqae153. PubMed
    (SJR quartile 1).

  4. Rafi, A., Penzar, D., ..., Random Promoter DREAM Challenge Consortium (including Wan, C.), ... and de Boer, C.
    A community effort to optimize sequence-based deep learning models of gene regulation
    Nature Biotechnology, 2024.
    DOI: 10.1038/s41587-024-02414-w. Publisher's webpage
    (SJR quartile 1)

  5. Alsaggaf, I., Buchan, D. and Wan, C. *
    Improving cell-type identification with Gaussian noise-augmented single-cell RNA-seq contrastive learning
    Briefings in Functional Genomics, elad059, 2024.
    DOI: 10.1093/bfgp/elad059. PubMed
    (SJR quartile 1).

  6. Wan, C. and Jones, D.T.
    Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks
    Nature Machine Intelligence, 2:540-550, 2020.
    DOI: 10.1038/s42256-020-0222-1. Reprint
    (SJR quartile 1, the 2nd top-ranked artificial intelligence journal).

  7. Zhou, N., Jiang, Y., ..., Wan, C., ..., and Friedberg, I.
    The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
    Genome Biology, 20(1):244, 2019.
    DOI:10.1186/s13059-019-1835-8. PubMed
    (SJR quartile 1, the 5th top-ranked genetics journal).

  8. Wan, C., Cozzetto, D., Fa, R. and Jones, D.T.
    Using Deep Maxout Neural Networks to Improve the Accuracy of Function Prediction from Protein Interaction Networks
    PLOS One, 14(7): e0209958, 2019.
    DOI:10.1371/journal.pone.0209958. PubMed
    (SJR quartile 1).

  9. Wan, C. and Freitas, A.A.
    An Empirical Evaluation of Hierarchical Feature Selection Methods for Classification in Bioinformatics Datasets with Gene Ontology-based Features
    Artificial Intelligence Review, 50(2):201-240, 2018.
    DOI:10.1007/s10462-017-9541-y. Preprint (original research article, source code available onGitHub)
    (SJR quartile 1).

  10. Fa, R., Cozzetto, D. Wan, C., and Jones, D.T.
    Predicting Human Protein Function with Multi-task Deep Neural Networks
    PLOS One, 13(6): e0198216, 2018.
    DOI:10.1371/journal.pone.0198216. PubMed
    (SJR quartile 1).

  11. Wan, C., Lees, J.G., Minneci, F., Orengo, C.A. and Jones, D.T.
    Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster
    PLOS Computational Biology, 13(10): e1005791, 2017.
    DOI:10.1371/journal.pcbi.1005791. PubMed (novel fly protein function predictions)
    (SJR quartile 1, the 8th top-ranked computational theory and mathematics journal).

  12. Fernandes, M., Wan, C., Tacutu, R., Barardo, D., Rajput, A., Wang, J., Thoppil, H., Yang, C., Freitas, A.A. and de Magalhaes, J.P.
    Systematic analysis of the gerontome reveals links between aging and age-related diseases
    Human Molecular Genetics, 25(21), 4804-4818, 2016.
    DOI:10.1093/hmg/ddw307. PubMed
    (SJR quartile 1, the 8th top-ranked Genetics (clinical) journal).

  13. Wan, C., Freitas, A.A. and de Magalhaes, J.P.
    Predicting the pro-longevity or anti-longevity effect of model organism genes with new hierarchical feature selection methods
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12(2):262-275, 2015.
    DOI:10.1109/TCBB.2014.2355218. PubMed (Datasets Used in the Experiments)
    (SJR quartile 2).

    14. Conferences/Workshops (focusing on machine learning algorithmic novelty)

  14. Wan, C. and Barton, C.
    A novel hierarchy-based knowledge discovery framework for elucidating human aging-related phenotypic abnormalities
    In: Proceedings of the 39th ACM/SIGAPP Symposium On Applied Computing (ACM SAC 2024), Avila, Spain, 2024.

  15. Wan, C.
    Predicting the effect of genes on longevity with novel hierarchical dependency-constrained tree augmented naive Bayes classifiers
    In: Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023), Istanbul, Turkey, 2023.

  16. Wan, C.
    Positive Feature Values Prioritized Hierarchical Dependency Constrained Averaged One-dependence Estimators for Gene Ontology Feature Spaces
    In: Proceedings of the 2022 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2022), Las Vegas, USA, pages: 826-829, 2022.
    DOI:10.1109/BIBM55620.2022.9995482

  17. Wan, C.
    Positive Feature Values Prioritized Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Hierarchical Feature Spaces
    In: Proceedings of the 2022 IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2022), Prague, Czech Republic, pages: 106-110, 2022.
    DOI:10.1109/SMC53654.2022.9945578. Preprint

  18. Wan, C. and Freitas, A.A.
    Hierarchical Dependency Constrained Averaged One-Dependence Estimators Classifiers for Hierarchical Feature Spaces
    In: Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), online (Aalborg, Denmark), PMLR, 138:557-568, 2020. Reprint

  19. Wan, C. and Freitas, A.A.
    A New Hierarchical Redundancy Eliminated Tree Augmented Naive Bayes Classifier for Coping with Gene Ontology-based Features
    In: Proceedings of the 33rd International Conference on Machine Learning (ICML 2016) Workshop on Computational Biology, New York, USA.
    (paper, poster, selected for spotlight talk). Reprint

  20. Wan, C. and Freitas, A.A.
    Two Methods for Constructing a Gene Ontology-based Feature Selection Network for a Bayesian Network Classifier and Applications to Datasets of Aging-related Genes.
    In: Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics (ACM BCB 2015), Atlanta, USA, pages: 27-36, 2015.
    DOI:10.1145/2808719.2808722. Reprint

  21. Wan, C. and Freitas, A.A.
    Prediction of the pro-longevity or anti-longevity effect of Caenorhabditis Elegans genes based on Bayesian classification methods
    In: Proceedings of the 2013 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2013), Shanghai, China, pages: 373-380, 2013.
    DOI:10.1109/BIBM.2013.6732521. PubMed

    22. Newsletter & Others

  22. Wan, C.
    Novel Hierarchical Feature Selection Algorithms for Predicting Genes' Aging-related Function
    AI Matters, 2(3):23-24, 2016.
    DOI:10.1145/2911172.2911180. Reprint

  23. Wan, C. and Biktasheva, I.V. and Lane, S.
    The application of a perceptron model to classify an individual's response to a proposed loading dose regimen of Warfarin
    arXiv:1211.2945, 2012. Reprint

    24. PhD Thesis

  24. Wan, C.
    Novel Hierarchical Feature Selection Methods for Classification and Their Application to Datasets of Ageing-Related Genes
    Doctor of Philosophy (PhD) thesis, University of Kent, 2015. Reprint