References of "Nguyen, Thanh Phuong 50002756"
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See detailComputational Systems Biology: Inference and Modelling
Lecca, Paola; Ihekawaba, Adaoha; Re, Angela et al

Book published by Elsevier (2016)

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of ... [more ▼]

Computational Systems Biology: Inference and Modelling provides an introduction to, and overview of, network analysis inference approaches which form the backbone of the model of the complex behavior of biological systems. This book addresses the challenge to integrate highly diverse quantitative approaches into a unified framework by highlighting the relationships existing among network analysis, inference, and modeling. The chapters are light in jargon and technical detail so as to make them accessible to the non-specialist reader. The book is addressed at the heterogeneous public of modelers, biologists, and computer scientists. [less ▲]

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See detailSystems biology integration of proteomic data in rodent models of depression reveals involvement of the immune response and glutamatergic signalling
Carboni, Lucia; Nguyen, Thanh Phuong UL; Caberlotto, Laura

in PROTEOMICS – Clinical Applications (2016)

Purpose The pathophysiological basis of major depression is incompletely understood. Recently, numerous proteomic studies have been performed in rodent models of depression to investigate the molecular ... [more ▼]

Purpose The pathophysiological basis of major depression is incompletely understood. Recently, numerous proteomic studies have been performed in rodent models of depression to investigate the molecular underpinnings of depressive-like behaviours with an unbiased approach. The objective of the study was to integrate the results of these proteomic studies in depression models to shed light on the most relevant molecular pathways involved in the disease. Experimental design Network analysis was performed integrating pre-existing proteomic data from rodent models of depression. The IntAct mouse and the HRPD were used as reference protein-protein interaction databases. The functionality analyses of the networks were then performed by testing over-represented GO biological process terms and pathways. Results Functional enrichment analyses of the networks revealed an association with molecular processes related to depression in humans, such as those involved in the immune response. Pathways impacted by clinically effective antidepressants were modulated, including glutamatergic signalling and neurotrophic responses. Moreover, dysregulations of proteins regulating energy metabolism and circadian rhythms were implicated. The comparison with protein pathways modulated in depressive patients revealed significant overlapping. Conclusions and clinical relevance This systems biology study supports the notion that animal models could contribute to the research into the biology and therapeutics of depression. [less ▲]

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See detailDiversity of key players in the microbial ecosystems of the human body.
Jordan, Ferenc; Lauria, Mario; Scotti, Marco et al

in Scientific reports (2015), 5

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability ... [more ▼]

Coexisting bacteria form various microbial communities in human body parts. In these ecosystems they interact in various ways and the properties of the interaction network can be related to the stability and functional diversity of the local bacterial community. In this study, we analyze the interaction network among bacterial OTUs in 11 locations of the human body. These belong to two major groups. One is the digestive system and the other is the female genital tract. In each local ecosystem we determine the key species, both the ones being in key positions in the interaction network and the ones that dominate by frequency. Beyond identifying the key players and discussing their biological relevance, we also quantify and compare the properties of the 11 networks. The interaction networks of the female genital system and the digestive system show totally different architecture. Both the topological properties and the identity of the key groups differ. Key groups represent four phyla of prokaryotes. Some groups appear in key positions in several locations, while others are assigned only to a single body part. The key groups of the digestive and the genital tracts are totally different. [less ▲]

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See detailCell type-selective disease-association of genes under high regulatory load
Galhardo, Mafalda Sofia UL; Berninger, Philipp; Nguyen, Thanh Phuong UL et al

in Nucleic Acids Research (2015), 43(18), 8839-8855

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines ... [more ▼]

We previously showed that disease-linked metabolic genes are often under combinatorial regulation. Using the genome-wide ChIP-Seq binding profiles for 93 transcription factors in nine different cell lines, we show that genes under high regulatory load are significantly enriched for disease-association across cell types. We find that transcription factor load correlates with the enhancer load of the genes and thereby allows the identification of genes under high regulatory load by epigenomic mapping of active enhancers. Identification of the high enhancer load genes across 139 samples from 96 different cell and tissue types reveals a consistent enrichment for disease-associated genes in a cell type-selective manner. The underlying genes are not limited to super-enhancer genes and show several types of disease-association evidence beyond genetic variation (such as biomarkers). Interestingly, the high regulatory load genes are involved in more KEGG pathways than expected by chance, exhibit increased betweenness centrality in the interaction network of liver disease genes, and carry longer 3'UTRs with more microRNA (miRNA) binding sites than genes on average, suggesting a role as hubs integrating signals within regulatory networks. In summary, epigenetic mapping of active enhancers presents a promising and unbiased approach for identification of novel disease genes in a cell type-selective manner. [less ▲]

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See detailNovel drug target identification for the treatment of dementia using multi-relational association mining
Nguyen, Thanh Phuong UL; Priami, Corrado; Caberlotto, Laura

in Scientific Reports (2015), 5

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia ... [more ▼]

Dementia is a neurodegenerative condition of the brain in which there is a progressive and permanent loss of cognitive and mental performance. Despite the fact that the number of people with dementia worldwide is steadily increasing and regardless of the advances in the molecular characterization of the disease, current medical treatments for dementia are purely symptomatic and hardly effective. We present a novel multi-relational association mining method that integrates the huge amount of scientific data accumulated in recent years to predict potential novel targets for innovative therapeutic treatment of dementia. Owing to the ability of processing large volumes of heterogeneous data, our method achieves a high performance and predicts numerous drug targets including several serine threonine kinase and a G-protein coupled receptor. The predicted drug targets are mainly functionally related to metabolism, cell surface receptor signaling pathways, immune response, apoptosis, and long-term memory. Among the highly represented kinase family and among the G-protein coupled receptors, DLG4 (PSD-95), and the bradikynin receptor 2 are highlighted also for their proposed role in memory and cognition, as described in previous studies. These novel putative targets hold promises for the development of novel therapeutic approaches for the treatment of dementia. [less ▲]

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See detailInference of Autism-Related Genes by Integrating Protein-Protein Interactions and miRNA-Target Interactions
Tran, Dang Hung; Nguyen, Thanh Phuong UL; Caberlotto, Laura et al

in Knowledge and Systems Engineering (2014)

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See detailNetwork analysis of neurodegenerative disease highlights a role of toll-like receptor signaling
Nguyen, Thanh Phuong UL; Caberlotto, Laura; Morine, Melissa J. et al

in BioMed Research International (2014), 2014

Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs), the etiology is still enigmatic and the distinctions ... [more ▼]

Despite significant advances in the study of the molecular mechanisms altered in the development and progression of neurodegenerative diseases (NDs), the etiology is still enigmatic and the distinctions between diseases are not always entirely clear. We present an efficient computationalmethod based on protein-protein interaction network (PPI) tomodel the functional network of NDs. The aim of this work is fourfold: (i) reconstruction of a PPI network relating to the NDs, (ii) construction of an association network between diseases based on proximity in the disease PPI network, (iii) quantification of disease associations, and (iv) inference of potentialmolecularmechanisminvolved in the diseases.The functional links of diseases not only showed overlap with the traditional classification in clinical settings, but also offered new insight into connections between diseases with limited clinical overlap. To gain an expanded view of the molecular mechanisms involved in NDs, both direct and indirect connector proteins were investigated. The method uncovered molecular relationships that are in common apparently distinct diseases and provided important insight into the molecular networks implicated in disease pathogenesis. In particular, the current analysis highlighted the Toll-like receptor signaling pathway as a potential candidate pathway to be targeted by therapy in neurodegeneration. [less ▲]

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See detailA systems biology investigation of neurodegenerative dementia reveals a pivotal role of autophagy
Caberlotto, Laura; Nguyen, Thanh Phuong UL

in BMC Systems Biology (2014), 8(1), 65

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See detailThe central role of AMP-kinase and energy homeostasis impairment in Alzheimer’s disease: a multifactor network analysis
Caberlotto, Laura; Lauria, Mario; Nguyen, Thanh Phuong UL et al

in PLoS ONE (2013)

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See detailMining multiple biological data for reconstructing signal transduction networks
Nguyen, Thanh Phuong UL; Ho, Tu-Bao

in Data Mining: Foundations and Intelligent Paradigms (2012)

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See detailDetecting disease genes based on semi-supervised learning and protein--protein interaction networks
Nguyen, Thanh Phuong UL; Ho, Tu-Bao

in Artificial Intelligence in Medicine (2012), 54(1), 63--71

Objective Predicting or prioritizing the human genes that cause disease, or “disease genes”, is one of the emerging tasks in biomedicine informatics. Research on network-based approach to this problem is ... [more ▼]

Objective Predicting or prioritizing the human genes that cause disease, or “disease genes”, is one of the emerging tasks in biomedicine informatics. Research on network-based approach to this problem is carried out upon the key assumption of “the network-neighbour of a disease gene is likely to cause the same or a similar disease”, and mostly employs data regarding well-known disease genes, using supervised learning methods. This work aims to find an effective method to exploit the disease gene neighbourhood and the integration of several useful omics data sources, which potentially enhance disease gene predictions. Methods We have presented a novel method to effectively predict disease genes by exploiting, in the semi-supervised learning (SSL) scheme, data regarding both disease genes and disease gene neighbours via protein–protein interaction network. Multiple proteomic and genomic data were integrated from six biological databases, including Universal Protein Resource, Interologous Interaction Database, Reactome, Gene Ontology, Pfam, and InterDom, and a gene expression dataset. Results By employing a 10 times stratified 10-fold cross validation, the SSL method performs better than the k-nearest neighbour method and the support vector machines method in terms of sensitivity of 85%, specificity of 79%, precision of 81%, accuracy of 82%, and a balanced F-function of 83%. The other comparative experimental evaluations demonstrate advantages of the proposed method given a small amount of labeled data with accuracy of 78%. We have applied the proposed method to detect 572 putative disease genes, which are biologically validated by some indirect ways. Conclusion Semi-supervised learning improved ability to study disease genes, especially a specific disease when the known disease genes (as labeled data) are very often limited. In addition to the computational improvement, the analysis of predicted disease proteins indicates that the findings are beneficial in deciphering the pathogenic mechanisms. [less ▲]

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See detailStudying protein--protein interaction networks: a systems view on diseases
Jordán, Ferenc; Nguyen, Thanh Phuong UL; Liu, Wei-Chung

in Briefings in Functional Genomics (2012), 11(6), 497--504

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See detailInferring pleiotropy by network analysis: linked diseases in the human PPI network
Nguyen, Thanh Phuong UL; Liu, Wei-Chung; Jordán, Ferenc

in BMC Systems Biology (2011), 5(1), 179

Background: Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these ... [more ▼]

Background: Earlier, we identified proteins connecting different disease proteins in the human protein-protein interaction network and quantified their mediator role. An analysis of the networks of these mediators shows that proteins connecting heart disease and diabetes largely overlap with the ones connecting heart disease and obesity. Results: We quantified their overlap, and based on the identified topological patterns, we inferred the structural disease-relatedness of several proteins. Literature data provide a functional look of them, well supporting our findings. For example, the inferred structurally important role of the PDZ domain-containing protein GIPC1 in diabetes is supported despite the lack of this information in the Online Mendelian Inheritance in Man database. Several key mediator proteins identified here clearly has pleiotropic effects, supported by ample evidence for their general but always of only secondary importance. Conclusions: We suggest that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy. Network analysis provides potentially useful tools here, as well as helps in improving databases. [less ▲]

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See detailDetecting Crosstalk Modules of Combined Networks: the Case for the NF-kappaB and p53
Nguyen, Thanh Phuong UL; Ihekwaba, Adaoha E. C.; Priami, Corrado

in Proceedings of IEEE International Conference on Bioscience, Biochemistry and Bioinformatics (2011)

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See detailModel-based clustering reveals vitamin D dependent multi-centrality hubs in a network of vitamin-related proteins
Nguyen, Thanh Phuong UL; Scotti, Marco; Morine, Melissa J. et al

in BMC Systems Biology (2011), 5(1), 195

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See detailNetwork Inference from Time-Dependent Omics Data
Lecca, Paola; Nguyen, Thanh Phuong UL; Priami, Corrado et al

in Bioinformatics for Omics Data Methods in Molecular Biology (2011)

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See detailEM Clustering Based Approach to Decipher Functional Modules in Cross-Talking Signaling systems
Nguyen, Thanh Phuong UL; Ihekwaba, Adaoha E. C.; Priami, Corrado

in International Journal of Bioscience, Biochemistry and Bioinformatics (2011), 1(1), 1

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See detailA quantitative approach to study indirect effects among disease proteins in the human protein interaction network
Nguyen, Thanh Phuong UL; Jordán, Ferenc

in BMC Systems Biology (2010), 4(1), 103

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See detailElucidation of functional consequences of signalling pathway interactions
Ihekwaba, Adaoha E. C.; Nguyen, Thanh Phuong UL; Priami, Corrado

in BMC Bioinformatics (2009), 10(1), 370

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See detailAn integrative domain-based approach to predicting protein--protein interactions
Nguyen, Thanh Phuong UL; Ho, Tu-Bao

in Journal of Bioinformatics & Computational Biology (2008), 6(06), 1115--1132

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