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6 July 2016
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.
1 November 2016
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.
30 January 2017
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.
18 October 2017
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.
11 June 2018
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.
23 July 2019
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.
13 July 2020
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 (Unpublished)
September 2020
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.