Canh Hao Nguyen
Bio-Knowledge Engineering Research Laboratory
Institute for Chemical Research, Kyoto University
Gokasho, Uji, Kyoto, 611-0011, Japan
Email: $myaccoutname @kuicr.kyoto-u.ac.jp
I am interested in Machine Learning in/for Bioinformatics, specially Machine Learning on Graphs.
I am working on models for underlying mechanisms of biological networks.
- D.H. Nguyen, C.H. Nguyen, H. Mamitsuka, "SIMPLE: Sparse Interaction Model over Peaks of MoLEcules for Fast, Interpretable Metabolite Identification from Tandem Mass Spectra". To appear in Bioinformatics, (Proceedings of the 26th International Conference on Intelligent Systems for Molecular Biology (ISMB 2018), Chicago, IL, USA, July, 2018), 2018.
- N. Wicker, C.H. Nguyen, H. Mamitsuka, "Some Properties of a Dissimilarity Measure for Labeled Graphs". Publications Mathematiques de Besancon. pp. 85-94, 2016.
- A. Mohamed, C.H. Nguyen, H. Mamitsuka, "NMRPro: An integrated web component for interactive processing and visualization of NMR spectra." Bioinformatics, vol. 32, no. 13, pp. 2067-2068, 2016.
- C.H. Nguyen, H. Mamitsuka, "New Resistance Distances with Global Information on Large Graphs". JMLR Workshop and Conference Proceedings. Volume 51: Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, pp. 639-647, May 2016.
- A. Mohamed, C.H. Nguyen, H. Mamitsuka, "Current status and prospects of computational resources for natural product dereplication: A review" Briefings in Bioinformatics, vol. 17, no. 2, pp. 309-321, 2016.
- A. Mohamed, T. Hancock, C.H. Nguyen, H. Mamitsuka, "NetPathMiner: R/Bioconductor package for network path mining through gene expression", Bioinformatics. vol. 30, no. 21, pp. 3139-3141, Nov 01, 2014.
- C. H. Nguyen, N. Wicker and H. Mamitsuka, "Selecting Graph Cut Solutions via Global Graph Similarity", IEEE Transactions on Neural Networks and Learning Systems. vol. 25, no. 7, pp. 1407-1412, 2014.
- N. Wicker, C. H. Nguyen and H. Mamitsuka, "A new dissimilarity measure for comparing labeled graphs", Linear Algebra and its Applications. vol. 438, no. 5, pp. 2331-2338. Mar 01, 2013.
- C. H. Nguyen and H. Mamitsuka, "Latent Feature Kernels for Link Prediction on Sparse Graphs" IEEE Transactions on Neural Networks and Learning Systems. vo. 23, no. 11, pp. 1793-1804, 2012.
- C. H. Nguyen and H. Mamitsuka, "Kernels for link prediction with latent feature models," The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2) (ECML/PKDD 2011), pp. 517-532, 2011.
- C. H. Nguyen and H. Mamitsuka, "Discriminative graph embedding for label propagation," IEEE Transactions on Neural Networks, vol. 22, no. 9, pp. 1395-1405, 2011.
- C. H. Nguyen, T. B. Ho, and V. Kreinovich, "Estimating quality of support vector machines learning under probabilistic and interval uncertainty: Algorithms and computational complexity," Interval / Probabilistic Uncertainty and Non-Classical Logics, pp. 57-69, 2008.
- C. H. Nguyen and T. B. Ho, "An efficient kernel matrix evaluation measure," Pattern Recognition, vol. 41, no. 11, pp. 3366-3372, 2008.
- H. Tanabe, T. B. Ho, C. H. Nguyen, and S. Kawasaki, "Simple but effective methods for combining kernels in computational biology," RIVF, pp. 71-78, 2008.
- C. H. Nguyen and T. B. Ho, "Kernel matrix evaluation," International Joint Conference on Artificial Intelligence (IJCAI2007), pp. 987-992, 2007.
- T. B. Ho, C. H. Nguyen, S. Kawasaki, S. Q. Le, and K. Takabayashi, "Exploiting temporal relations in mining hepatitis data," New Generation Computing, vol. 25, no. 3, pp. 247-262, 2007.
- T. B. Ho, C. H. Nguyen, S. Kawasaki, and K. Takabayashi, "Temporal relations extraction in mining hepatitis data," The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD2007), pp. 523-530, 2007.
- C. H. Nguyen and T. B. Ho, "Sampling for imbalanced data learning," The International Workshop on Data-Mining and Statistical Science (DMSS2006), pp. 12-19, 2006.
- C. H. Nguyen and T. B. Ho, "An imbalanced data rule learner," The European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD2005), pp. 617-624, 2005.