Papers

• J.S. Bodelet, G. Blanc, J. Shan, G.M. Terrera, and OY Chén. Statistical quantile learning for large, nonlinear, and additive latent variable models. (2024) arXiv 2003.13119v4 [Paper] [Software].

• D.T. Vũ, T.T. Le, P.M. Tuan, N.L. Trung, K. Abed-Meraim, M. Adel, N.V. Dung, N.T. Trung, D.D. Long, and OY Chén. Tensor kernel learning for classification of Alzheimer’s conditions using multimodal data. Multimedia Analysis and Pattern Recognition (2024)  [Paper] [Software].

• D.T. Vũ and OY Chén. Deep multi-kernel learning for data fusion. (2024) [Software].

• J. Gou, K. Wu, and OY Chén. Extending multiple testing with unknown test dependency via the CoCo test: with applications to cancer studies.  (2024) [Software].

• S. Stockman, A. Hu, B. Zhi, X. Wu, R. Wood, and OY Chén. Enhancing hospital capacity management for surge demands by integrating machine learning and queueing theory. (2024) [Paper].

• OY Chén, D.T. Vũ, C.S. Diaz, J.S. Bodelet, H. Phan, G. Allali, V-D Nguyen, H. Cao, X. He, Y. Müller, B. Zhi, H. Shou, H. Zhang, W. He, X. Wang, M. Munafò, N.L. Trung, G. Nagels, P. Ryvlin, G. Pantaleo. Residual Partial Least Squares Learning: Brain Cortical Thickness Simultaneously Predicts Eight Non-pairwise-correlated Behavioural and Disease Outcomes in Alzheimer's Disease. (2024) [Paper][Software].

• OY Chén, J.S. Bodelet, R. Saraiva, H. Phan, J. Di, G. Nagels, T. Schwantje, H. Cao, J. Gou, J. Reinen, B. Xiong, B. Zhi, X. Wang, and M. de Vos. The roles, challenges, and merits of the P-value. Patterns (2023) [Paper].

• OY Chén, D.T. Vũ, G. Greub, H. Cao, X. He, H. Shou, Y. Muller, C. Petrovas, V.-D. Nguyen, B. Zhi, L. Perez, J.-L. Raisaro, G. Nagels, M. de Vos, W. He, R. Gottardo, P. Smart, M. Munafò, and G. Pantaleo. The statistical analysis of brain variability. IEEE Statistical Signal Processing (2023) [Paper] [Poster].

• OY Chén, F. Lipsmeier, H. Phan, F. Dondelinger, A. Scotland, A. Creagh, C. Gossens, M. Lindemann, and M. de Vos. Personalized longitudinal assessment and monitoring of multiple sclerosis using smartphones. IEEE Journal of Biomedical and Health Informatics (JBHI) (2023) [Paper].

• OY Chén. Uniting machine intelligence, brain and behavioural sciences to assist criminal justice. arXiv 2207.01511v2 (2022) [Paper].

• OY Chén and M. Olejnik. Cognitive trust and stochastic multiagent systems. (2022).

• OY Chén, H. Phan, H. Cao, T. Qian, and M. de Vos. Probing potential priming: Defining, quantifying, and testing the causal priming effect using the potential outcomes framework. Frontiers in Psychology 13, 724498 (2022) [Paper].

• OY Chén, H. Cao, H. Phan, J. Reinen, J. Gou, J. Di, T. Qian, J. Prince, T. Cannon, and M. de Vos. Identifying neural signatures mediating behavioral symptoms and psychosis onset: High-dimensional whole brain functional mediation analysis. NeuroImage 226, 117508 (2021) [Paper][Supp. Materials].

• OY Chén and B. Roberts. Personalized healthcare and public health in the digital age. Frontiers in Digital Health 3, 595704 (2021) [Paper] [Longer version].

• OY Chén, H. Phan, G. Nagel, and M. de Vos. On statistical analysis of brain variability. Preprints 202008.0428.v1 (2020) [Paper].

• OY Chén. Big data in omics and imaging: Integrated analysis and causal inference. The Journal of the American Statistical Association 115, 487-488 (2020) [Paper].

• OY Chén, F. Lipsmeier, H. Phan, J. Prince, K.I. Taylor, M. Lindemann, C. Gossens, and M. de Vos. Building a machine-learning framework to remotely assess Parkinson’s disease using smartphones. IEEE Transactions on Biomedical Engineering 67, 3491-3500 (2020) [Paper].

• OY Chén. The roles of statistics in human neuroscience. Brain Sci. 9, 194 (2019). I dedicate this paper to Michael Jacroux, who showed me the excitement of statistics, in honour of his emeritus retirement. [Paper] [Erratum].

• OY Chén, H. Cao, J. Reinen, T. Qian, J. Gou, H. Phan, M. de Vos, and T. Cannon. Resting-state brain information flow predicts cognitive flexibility in humans. Scientific Reports 9, 3879 (2019) [Paper].

• S. Zeki and OY Chén. The Bayesian-Laplacian brain. European Journal of Neuroscience 51, 1441-1462 (2019) [Paper].

• OY Chén, E. Ogburn, C. Crainiceanu, B. Caffo, T. Wager, and M. Lindquist. High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics 19, 121–136 (2015) [Paper] [Asymptotic Theory][Code].

• OY Chén, L. Xiao, B. Caffo, M. Lindquist, J. Schrack, L. Ferrucci, and C. Crainiceanu. A longitudinal functional data analysis for underlying daily physical activity change. (2015) [Paper].

• OY Chén and M.A. Jacroux. On the use of semi-folding in regular blocks two-level factorial designs. Communications in Statistics - Theory and Methods 44, 2473-2506 (2015) [Paper].

• OY Chén and J. Di. Penalised iterative sparse partial correlation estimation (Π-SPaCE) - with an application to whole-brain graph estimation. (2021+)

• OY Chén. The generative representational similarity analysis. (2021+)

• H. Cao, OY Chén, Y. Chung, S.C. McEwen, C.E. Bearden, J. Addington, B. Goodyear, K.S. Cadenhead, H. Mirzakhanian, B.A. Cornblatt, D.M. Olvet, D.H. Mathalon, T.H. McGlashan, D.O. Perkins, A. Belger, L.J. Seidman, H. Thermenos, M.T. Tsuang, T.G.M. van Erp, E.F. Walker, S. Hamann, A. Anticevic, S.W. Woods, and T.D. Cannon. Cerebello-thalamo-cortical hyperconnectivity: A state-independent functional neural signature for psychosis prediction and characterization. Nature Communications 9, 3836 (2018) [Paper][Supp. Materials].

• J. Reinen, OY Chén, J. Baker, T. Yeo, K. Anderson, R. Hutchison, M. Sabuncu, D. Öngür, J. Roffman, J. Smoller, and A. Holmes. The human cerebral cortex possesses a reconfigurable dynamic network architecture that is disrupted in psychotic illness. Nature Communications 9, 1157 (2018) [Paper] [Supp. Materials].

• H. Phan, OY Chén, P. Koch, A. Mertins, and M. de Vos. XSleepNet: Multi-view sequential model for automatic sleep staging. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) (2021) [Paper]

• S. Denissen, OY Chén, J. de Mey, M. de Vos, J. Van Schependom, D. Sima, and G. Nagels. Towards multimodal machine learning prediciton of individual cognitive evolution in multiple sclerosis. Journal of Personalized Medicine (2021) [Paper].

• J. Gou and OY Chén. Critical boundary refinement in a group sequential trial when the primary endpoint data accumulate faster than the secondary endpoint. In ICSA Book Series in Statistics. Springer, Berlin, Germany (2018) [Chapter] [Book].

• S. Zeki, OY Chén, and J. Romaya. The biological basis of mathematical beauty. Frontiers in Human Neuroscience 12, 467 (2018) [Paper].

• H. Phan, OY Chén, P. Koch, L. Pham, I. McLoughlin, A. Mertins, and M. de Vos. Revisiting convolutional neural networks for automated sleep staging. EMBC (2020).

• H. Phan, OY Chén, L. Pham, P. Koch, M. de Vos, I. McLoughlin, and A. Mertins. Spatial-temporal attention pooling for audio scene classification. Interspeech (2019) [Paper].

• H. Phan, OY Chén, P. Koch, A. Mertins, and M. de Vos. Deep transfer learning for single-channel automatic sleep staging with channel mismatch. EUSIPCO (IEEE European Association for Signal Processing (2019) [Paper].

• H. Phan, OY Chén, P. Koch, A. Mertins, and M. de Vos. Fusion of end-to-end deep learning models for sequence-to-sequence sleep staging. IEEE Engineering in Medicine and Biology Society (2019) [Paper].

• H. Phan, OY Chén, P. Koch, L. Pham, I. McLoughlinz, A. Mertins, and M. de Vos. Unifying isolated and overlapping audio event detection with multi-label multi-task convolutional recurrent neural networks. In ICASSP 51-55 (2018) [Paper].

• H. Cao H, OY Chén, S.C. McEwen, Y. Chung, C.E. Bearden, J. Addington, B. Goodyear, K.S. Cadenhead, H. Mirzakhanian, B.A. Cornblatt, D.M. Olvet, D.H. Mathalon, T.H. McGlashan, D.O. Perkins, A. Belger, L.J. Seidman, H. Thermenos, M.T. Tsuang, T.G.M. van Erp, E.F. Walker, S. Hamann, A. Anticevic, S.W. Woods, and T.D. Cannon. Altered brain activation during memory retrieval precedes and predicts conversion to psychosis in individuals at clinical high risk. Schizophrenia Bulletin 45, 924-933 (2018) [Paper].

• H. Phan, OY Chén, P. Koch, L. Pham, I. McLoughlin, A. Mertins, and M. de Vos. Beyond equal-length snippets: How long is sufficient to recognize an audio scene? (2018) [Paper].

• H. Phan, OY Chén, P. Koch, Z. Lu, I. McLoughlin, A. Mertins, and M. de Vos. Towards more accurate automatic sleep staging via deep transfer learning. IEEE Transactions on Biomedical Engineering (2019) [Paper].

• H. Cao, OY Chén, S. McEwen, J. Forsyth, G. Dylan, C. Bearden, J. Addington, B. Goodyear, K. Cadenhead, H. Mirzakhanian, B. Cornblatt, R. Carrión, D. Mathalon, T. McGlashan, D. Perkins, A. Belger, H. Thermenos, M. Tsuang, T. van Erp, E. Walker, S. Hamann, A. Anticevic, S. Wood, and T.D. Cannon. Cross-paradigm connectivity: Reliabiltiy, stability, and utility. Brain Imaging and Behavior (2020) [Paper].

• H. Phan, OY Chén, F. Andreotti, N. Cooray, and M. de Vos. Towards better practice of deep learning for automatic sleep staging. (2018) [Paper].

• S. Denissen, M. Grothe, M. Vaneckova, T. Uher, J. Laton, M. Kudrna, D. Horakova, M. Kirsch, J. Motyl, M. de Vos, OY Chén, J. Van Schependom, D.M. Sima, and G. Nagels. Transfer learning on structural brain age models to decode cognition in MS: A federated learning approach. medRxiv 2023.04.22.23288741 (2023) [Paper].

• V.-D. Nguyen, H. Phan, OY Chén, A. Mansour, A. Coatanhay, T. Marsault. On the relationship between Vogler algorithm derivation and parabolic equation for multiple knife edge diffraction. IEEE Transactions on Antennas and Propagation (2023).

• H. Phan, E. Heremans, OY Chén, P. Koch, A. Mertins, M. de Vos. Improving automatic sleep staging via temporal smoothness regularization. IEEE ICASSP (2023) [Paper].

• H. Phan, K. Mikkelsen, OY Chén, P. Koch, A. Mertins, and M. de Vos. SleepTransformer: Automatic sleep staging with interpretability and uncertainty quantification. IEEE Transactions on Biomedical Engineering 69, 2456-2467 (2021) [Paper]

• H. Phan, K. P. Lorenzen, E. Heremans, OY Chén, M. C. Tran, P. Koch, A. Mertins, M. Baumert, K, Mikkelsen, M. de Vos. L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Staging. (2023) [Paper].

• H. Phan, F. Andreotti, N. Cooray, OY Chén, and M. de Vos. SeqSleepNet: End-to-end hierarchical recurrent neural network for sequence-to-sequence automatic sleep staging. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 400-410 (2018) [Paper] [Journal Cover].

• H. Phan, F. Andreotti, N. Cooray, OY Chén, and M. de Vos. Joint classification and prediction CNN framework for automatic sleep stage classification. IEEE Transactions on Biomedical Engineering 66, 1285-1296 (2018) [Paper].

• H. Phan, H. Nguyen, OY Chén, P. Koch, N. Duong, I. McLoughlin, and A. Mertins. Self-attention generative adversarial network for speech enhancement. ICASSP, (2021) [Paper].

• H. Phan, H. Nguyen, OY Chén, P. Koch, N. Duong, I. McLoughlin, and A. Mertins. Multi-view audio and music classification. ICASSP, (2021) [Paper].

• H. Phan, K. Mikkelsen, OY Chén, P. Koch, A. Mertins, P. Kidmose, and M. de Vos. Personalized automatic sleep staging with single-night data: A pilot study with Kullback–Leibler divergence regularization. Physiological Measurements (2020) [Paper].

• H. Phan, F. Andreotti, N. Cooray, OY Chén, and M. de Vos. Automatic sleep stage classification using single-channel EEG: Learning sequential features with attention-based recurrent neural networks. In IEEE Engineering in Medicine and Biology Society (EMBC), 1452-1455 (2018). [Paper].

• H. Phan, I. McLoughlin, L. Pham, OY Chén, P. Koch, M. de Vos, and A. Mertins. Improving GANs for speech enhancement. IEEE Signal Processing Letters (2020) [Paper]

• H. Phan, F. Andreotti, N. Cooray, OY Chén, and M. de Vos. DNN filter bank improves 1-max pooling CNN for automatic sleep stage classification. IEEE Engineering in Medicine and Biology Society (EMBC), 453-456 (2018) [Paper].

• A. Chantziou, C. Brenna, K. Ioannidou, OY Chén, P.A. Korkolopoulou, A. Antoniadou, M. Psichogiou, M. Papaioannou, P. Tsirigotis, P.G. Foukas, L.L. de Leval, and C. Petrovas. HIV infection is associated with compromised tumor microenvironment adaptive immune reactivity in Hodgkin Lymphoma. Blood Advances, 2023012116 (2024).

• C. Brenna, B. Bramon Mora, K. Ioannidou, S. Burgermeister, J. Bodelet, M. Siebmanns, S. Georgakis, M. Orfanakis, N. Sedille, M. Feinstein, J.W. Lomasney, OY Chén, G. Pantaleo, S. Berezowska, L. De Leval, R. Gottardo, and C. Petrovas. Follicular Bcl6 reactivity is associated with a unique immune landscape and spatial transcriptome in COVID-19. bioRxiv 2024.11.07.622471 [Paper].

• S. Georgakis, M. Orfanakis, C. Brenna, S. Burgermeister, P.M. Del Rio Estrada, M. González-Navarro, F. Torres-Ruiz, G. Reyes-Terán, S. Avila-Rios, Y.A. Luna-Villalobos, OY Chén, G. Pantaleo, R.A. Koup, and C. Petrovas. Follicular immune landscaping reveals a distinct profile of FOXP3hiCD4hi T cells in treated compared to untreated HIV. Vaccines 12, 912 (2024).

• OY Chén. A generalized and drifting time corrected approach using Wiener-Granger causality and VAR(p) process for detecting high-dimensional directed functional communication between brain regions and predicting behavior.

• N. Dasgupta, OY Chén, R. Basu, and S.S. Daoud. Unsupervised learning methods to proteomic data from colon cancer. In Contemporary Topics in Mathematics and Statistics with Applications. New Delhi, India (2013) [Chapter].

• N. Dasgupta, OY Chén, R. Basu, and S.S. Daoud. Comparison of clustering algorithms: An example with proteomic data. Advances and Applications in Statistics 33, 63 (2013) [Paper].

• N. Dasgupta, OY Chén, R. Basu, and S.S. Daoud. Comparison of methods for unsupervised learning methods – an applied study using proteomic data from colon cancer and simulations. In CIAS, Indian Statistical Institute (2012).