Profil

NGUYEN Thanh-Phuong

Main Referenced Co-authors
Priami, Corrado (9)
Caberlotto, Laura (8)
Ho, Tu Bao (5)
SAUTER, Thomas  (5)
Ho, Tu-Bao (4)
Main Referenced Keywords
active enhancer (1); Chronic mild stress (1); co-morbidities (1); complex biological systems (1); data mining (1);
Main Referenced Unit & Research Centers
ULHPC - University of Luxembourg: High Performance Computing (2)
Main Referenced Disciplines
Life sciences: Multidisciplinary, general & others (20)
Human health sciences: Multidisciplinary, general & others (9)
Computer science (2)
Engineering, computing & technology: Multidisciplinary, general & others (1)
Psychiatry (1)

Publications (total 35)

The most downloaded
180 downloads
CECCHINI, V. F., NGUYEN, T.-P., PFAU, T., DE LANDTSHEER, S., & SAUTER, T. (2019). An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction. In V. F. CECCHINI, An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction (1st ed, pp. 5). Da Nang, Vietnam: DA NANG PUBLISHING HOUSE. https://hdl.handle.net/10993/41512

The most cited

62 citations (Scopus®)

Caberlotto, L., NGUYEN, T.-P., Lauria, M., Corrado, P., Rimondini, R., Maioli, S., Cedazo-Minguez, A., Sita, G., Morroni, F., & Corsi, M. (2019). Cross-disease analysis of Alzheimer’s disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid diseases. Scientific Reports, 9 (1), 3965. doi:10.1038/s41598-019-39828-5 https://hdl.handle.net/10993/41315

Gergei, I., Pfau, T., Krämer, B. K., SCHNEIDER, J., NGUYEN, T.-P., März, W., & SAUTER, T. (2023). Cardiovascular risk prediction - a systems medicine approach. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/54719. doi:10.1101/2023.03.16.23287363

BADKAS, A., NGUYEN, T.-P., Caberlotto, L., SCHNEIDER, J., DE LANDTSHEER, S., & SAUTER, T. (2021). Degree Adjusted Large-Scale Network Analysis Reveals Novel Putative Metabolic Disease Genes. Biology, 10 (2). doi:10.3390/biology10020107
Peer reviewed

MARTINS CONDE, P., SAUTER, T., & NGUYEN, T.-P. (2020). An efficient machine learning-based approach for screening individuals at risk of hereditary haemochromatosis. Scientific Reports, 10 (1), 20613. doi:10.1038/s41598-020-77367-6
Peer Reviewed verified by ORBi

Caberlotto, L., NGUYEN, T.-P., Lauria, M., Corrado, P., Rimondini, R., Maioli, S., Cedazo-Minguez, A., Sita, G., Morroni, F., & Corsi, M. (2019). Cross-disease analysis of Alzheimer’s disease and type-2 Diabetes highlights the role of autophagy in the pathophysiology of two highly comorbid diseases. Scientific Reports, 9 (1), 3965. doi:10.1038/s41598-019-39828-5
Peer Reviewed verified by ORBi

CECCHINI, V. F., NGUYEN, T.-P., PFAU, T., DE LANDTSHEER, S., & SAUTER, T. (2019). An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction. In V. F. CECCHINI, An Efficient Machine Learning Method to Solve Imbalanced Data in Metabolic Disease Prediction (1st ed, pp. 5). Da Nang, Vietnam: DA NANG PUBLISHING HOUSE.
Peer reviewed

Giang, T. T., NGUYEN, T.-P., Tran, Q. V., & Tran, D. H. (2018). A Fast Multiple Kernel Learning Framework with Dimensionality Reduction. In Giang, Nguyen, Q. V. Tran, ... D. H. Tran, Integrated Uncertainty in Knowledge Modelling and Decision Making. IEEE.
Peer reviewed

Giang, T. T., NGUYEN, T.-P., & Tran, D. H. (2017). Stratifying cancer patients based on multiple kernel learning and dimensionality reduction. In T. T. Giang, T.-P. NGUYEN, ... Tran, 9th International Conference on Knowledge and Systems Engineering (KSE). IEE. doi:10.1109/KSE.2017.8119443
Peer reviewed

Lecca, P., Ihekawaba, A., Re, A., Mura, I., & NGUYEN, T.-P. (2016). Computational Systems Biology: Inference and Modelling. Amsterdam, Netherlands: Elsevier. doi:10.1016/C2014-0-01062-3

Carboni, L., NGUYEN, T.-P., & Caberlotto, L. (2016). Systems biology integration of proteomic data in rodent models of depression reveals involvement of the immune response and glutamatergic signalling. Proteomics. Clinical Applications. doi:10.1002/prca.201500149
Peer Reviewed verified by ORBi

Jordan, F., Lauria, M., Scotti, M., NGUYEN, T.-P., Praveen, P., Morine, M., & Priami, C. (November 2015). Diversity of key players in the microbial ecosystems of the human body. Scientific Reports, 5, 15920. doi:10.1038/srep15920
Peer Reviewed verified by ORBi

GALHARDO, M. S., Berninger, P., NGUYEN, T.-P., SAUTER, T., & SINKKONEN, L. (03 September 2015). Cell type-selective disease-association of genes under high regulatory load. Nucleic Acids Research, 43 (18), 8839-8855. doi:10.1093/nar/gkv863
Peer Reviewed verified by ORBi

NGUYEN, T.-P., Priami, C., & Caberlotto, L. (July 2015). Novel drug target identification for the treatment of dementia using multi-relational association mining. Scientific Reports, 5. doi:10.1038/srep11104
Peer reviewed

NGUYEN, T.-P., Caberlotto, L., Morine, M. J., & Priami, C. (2014). Network analysis of neurodegenerative disease highlights a role of toll-like receptor signaling. BioMed Research International, 2014. doi:10.1155/2014/686505
Peer Reviewed verified by ORBi

Caberlotto, L., & NGUYEN, T.-P. (2014). A systems biology investigation of neurodegenerative dementia reveals a pivotal role of autophagy. BMC Systems Biology, 8 (1), 65. doi:10.1186/1752-0509-8-65
Peer Reviewed verified by ORBi

Tran, D. H., NGUYEN, T.-P., Caberlotto, L., & Priami, C. (2014). Inference of Autism-Related Genes by Integrating Protein-Protein Interactions and miRNA-Target Interactions. In Knowledge and Systems Engineering (pp. 299--311). Springer. doi:10.1007/978-3-319-02741-8_26
Peer reviewed

Caberlotto, L., Lauria, M., NGUYEN, T.-P., & Scotti, M. (2013). The central role of AMP-kinase and energy homeostasis impairment in Alzheimer’s disease: a multifactor network analysis. PLoS ONE. doi:10.1371/journal.pone.0078919
Peer Reviewed verified by ORBi

NGUYEN, T.-P., & Ho, T.-B. (2012). Detecting disease genes based on semi-supervised learning and protein--protein interaction networks. Artificial Intelligence in Medicine, 54 (1), 63--71. doi:10.1016/j.artmed.2011.09.003
Peer Reviewed verified by ORBi

Jordán, F., NGUYEN, T.-P., & Liu, W.-C. (2012). Studying protein--protein interaction networks: a systems view on diseases. Briefings in Functional Genomics, 11 (6), 497--504. doi:10.1093/bfgp/els035
Peer reviewed

NGUYEN, T.-P., & Ho, T.-B. (2012). Mining multiple biological data for reconstructing signal transduction networks. In Data Mining: Foundations and Intelligent Paradigms (pp. 163--185). Springer.
Peer reviewed

NGUYEN, T.-P., Liu, W.-C., & Jordán, F. (2011). Inferring pleiotropy by network analysis: linked diseases in the human PPI network. BMC Systems Biology, 5 (1), 179. doi:10.1186/1752-0509-5-179
Peer Reviewed verified by ORBi

NGUYEN, T.-P., Scotti, M., Morine, M. J., & Priami, C. (2011). Model-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins. BMC Systems Biology, 5 (1), 195. doi:10.1186/1752-0509-5-195
Peer Reviewed verified by ORBi

NGUYEN, T.-P., Ihekwaba, A. E. C., & Priami, C. (2011). EM Clustering Based Approach to Decipher Functional Modules in Cross-Talking Signaling systems. International Journal of Bioscience, Biochemistry and Bioinformatics, 1 (1), 1.
Peer reviewed

NGUYEN, T.-P., Ihekwaba, A. E. C., & Priami, C. (2011). Detecting Crosstalk Modules of Combined Networks: the Case for the NF-kappaB and p53. In Proceedings of IEEE International Conference on Bioscience, Biochemistry and Bioinformatics.
Peer reviewed

Lecca, P., NGUYEN, T.-P., Priami, C., & Quaglia, P. (2011). Network Inference from Time-Dependent Omics Data. In Bioinformatics for Omics Data Methods in Molecular Biology (pp. 435-455). Springer.
Peer reviewed

NGUYEN, T.-P., & Jordán, F. (2010). A quantitative approach to study indirect effects among disease proteins in the human protein interaction network. BMC Systems Biology, 4 (1), 103. doi:10.1186/1752-0509-4-103
Peer Reviewed verified by ORBi

Ihekwaba, A. E. C., NGUYEN, T.-P., & Priami, C. (2009). Elucidation of functional consequences of signalling pathway interactions. BMC Bioinformatics, 10 (1), 370. doi:10.1186/1471-2105-10-370
Peer Reviewed verified by ORBi

NGUYEN, T.-P., Satou, K., Ho, T.-B., & Takabayashi, K. (2008). Constructing Signal Transduction Networks Using Multiple Signaling Feature Data [Paper presentation]. Statistical and Relational Learning in Bioinformatics 2008, PKDD/ECML'08 satellite workshop.

NGUYEN, T.-P., & Ho, T.-B. (2008). An integrative domain-based approach to predicting protein--protein interactions. Journal of Bioinformatics and Computational Biology, 6 (06), 1115--1132. doi:10.1142/S0219720008003874
Peer reviewed

NGUYEN, T.-P., & Ho, T. B. (2007). A semi-supervised learning approach to disease gene prediction. In Proceeding of the IEEE International Conference on Bioinformatics and Biomedicine 2007. IEEE Society Press. doi:10.1109/BIBM.2007.30
Peer reviewed

NGUYEN, T.-P., Ho, T. B., & Nguyen, N. B. (2007). Prediction of Protein-Protein Interactions Using Bayesian Networks. In Proceeding of the 2nd International Conference on Knowledge, Information and Creativity Support Systems (KICSS 2007). JAIST Press.
Peer reviewed

Ho, T. B., NGUYEN, T.-P., & Tran, T. N. (2007). Study of Protein-Protein Interactions from Multiple Data Sources. In D. Taniar (Ed.), Advances in Data Warehousing and Mining. IGC Publishers.
Peer reviewed

NGUYEN, T.-P., & Ho, T. B. (2006). Discovering signal transduction networks using signaling domain-domain interactions. Genome Informatics, 17 (2), 35--45.
Peer reviewed

NGUYEN, T.-P., & Ho, T. B. (2006). Prediction of domain-domain interactions using inductive logic programming from multiple genome databases. In Lecture Notes in Artificial Intelligence LNAI 4265 (pp. 185--196). Springer.
Peer reviewed

Nguyen, N. B., Than, V. C., NGUYEN, T.-P., & Bao, H. T. (2004). A Mixed Similarity Measure for Data with Numeric, Symbolic and Ordinal Attributes. In The proceeding of Japan-Vietnam Joint Workshop on Active Mining (Artificial Intelligence I) (pp. 67--72). 一般社団法人情報処理学会.
Peer reviewed

NGUYEN, T.-P., & Nguyen, N. B. (2004). A Specification for Underground Tank Monitoring System (UTMS) Using Real-Time Process Algebra (RTPA). In The procedding of the IEEE RIVF 2004 conference.
Peer reviewed

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