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Article

Alsaggaf, Ibrahim and Freitas, A.A. and Wan, Cen (2024) 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 6 (4), ISSN 2631-9268.

Rafi, Abdul Muntakim and Nogina, D. and Penzar, D. and Lee, D. and Lee, D. and Kim, N. and Kim, S. and Kim, D. and Shin, Y. and Kwak, I.-Y. and Meshcheryakov, G. and Lando, A. and Zinkevich, A. and Kim, B.-C. and Lee, J. and Kang, T. and Vaishnav, E.D. and Yadollahpour, P. and Bornelöv, S. and Svensson, F. and Trapotsi, M.-A. and Tran, D. and Nguyen, T. and Tu, X. and Zhang, W. and Qiu, W. and Ghotra, R. and Yu, Y. and Labelson, E. and Prakash, A. and Narayanan, A. and Koo, P. and Chen, X. and Jones, D.T. and Tinti, M. and Guan, Y. and Ding, M. and Chen, K. and Yang, Y. and Ding, K. and Dixit, G. and Wen, J. and Zhou, Z. and Dutta, P. and Sathian, R. and Surana, P. and Ji, Y. and Liu, H. and Davuluri, R.V. and Hiratsuka, Y. and Takatsu, M. and Chen, T.-M. and Huang, C.-H. and Wang, H.-K. and Shih, E.S.C. and Chen, S.-H. and Wu, C.-H. and Chen, J.-Y. and Huang, K.-L. and Alsaggaf, I. and Greaves, P. and Barton, Carl and Wan, Cen and Abad, N. and Körner, C. and Feuerbach, L. and Brors, B. and Li, Y. and Röner, S. and Dash, P.M. and Schubach, M. and Soylemez, O. and Møller, A. and Kavaliauskaite, G. and Madsen, J. and Lu, Z. and Queen, O. and Babjac, A. and Emrich, S. and Kardamiliotis, K. and Kyriakidis, K. and Malousi, A. and Palaniappan, A. and Gupta, K. and Kumar S, P. and Bradford, J. and Perrin, D. and Salomone, R. and Schmitz, C. and JiaXing, C. and JingZhe, W. and AiWei, Y. and Kim, S. and Albrecht, J. and Regev, A. and Gong, W. and Kulakovskiy, I.V. and Meyer, P. and de Boer, C.G. (2024) A community effort to optimize sequence-based deep learning models of gene regulation. Nature Biotechnology , ISSN 1087-0156.

Wan, Cen and Jones, D.T. (2020) Protein function prediction is improved by creating synthetic feature samples with generative adversarial networks. Nature Machine Intelligence 2 , pp. 540-550. ISSN 2522-5839.

Wan, Cen and Cozzetto, D. and Fa, R. and Jones, D. (2019) Using Deep Maxout Neural Networks to improve the accuracy of function prediction from Protein Interaction Networks. PLoS One , ISSN 1932-6203.

Fa, R. and Cozzetto, D. and Wan, Cen and Jones, D. (2018) Predicting human protein function with multitask deep neural networks. PLoS One , ISSN 1932-6203.

Wan, Cen and Lees, J. and Minneci, F. and Orengo, C. and Jones, D. (2017) Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster. PLoS Computational Biology , ISSN 1553-7358.

Wan, Cen and Freitas, A. (2017) An empirical evaluation of hierarchical feature selection methods for classification in Bioinformatics datasets with gene ontology-based features. Artificial Intelligence Review , ISSN 0269-2821.

Fernandes, M. and Wan, Cen and Tacutu, R. and Barardo, D. and Rajput, A. and Wang, J. and Thoppil, H. and Thornton, D. and Yang, C. and Freitas, A. and de Magalhaes, J.P. (2016) Systematic analysis of the gerontome reveals links between aging and age-related diseases. Human Molecular Genetics 25 (21), pp. 4804-4818. ISSN 0964-6906.

Conference or Workshop Item

Wan, Cen and Barton, Carl (2024) A novel hierarchy-based knowledge discovery framework for elucidating human aging-related phenotypic abnormalities. In: SAC '24 - 39th ACM/SIGAPP Symposium on Applied Computing, 08-12 Apr 2024, Avila, Spain.

Wan, Cen and Freitas, A. (2020) Hierarchical dependency constrained averaged one-dependence estimators classifiers for hierarchical feature spaces. In: The 10th International Conference on Probabilistic Graphical Models, 23-25 September 2020, Aalborg, Denmark. (Unpublished)

Wan, Cen and Freitas, A. (2016) A new hierarchical redundancy eliminated tree augmented naive Bayes classifier for coping with gene ontology-based features. In: The 33rd International Conference on Machine Learning (ICML) Workshop on Computational Biology, 24th June 2016, New York.