Thanh Phuong Nguyen
Other affiliations: Japan Advanced Institute of Science and Technology, University of Victoria, Vietnam National University, Ho Chi Minh City ...read more
Bio: Thanh Phuong Nguyen is an academic researcher from Ho Chi Minh City University of Technology. The author has contributed to research in topics: Interaction network & Systems biology. The author has an hindex of 13, co-authored 79 publications receiving 650 citations. Previous affiliations of Thanh Phuong Nguyen include Japan Advanced Institute of Science and Technology & University of Victoria.
Papers published on a yearly basis
TL;DR: It is shown that a prebiotic as part of a synbiotic is hydrolyzed to mono- or disaccharides as the sole carbon source with diverse mechanisms, thereby increasing biomass and colonization that is established by specific crosstalk between probiotic bacteria and the surface of intestinal epithelial cells of the host.
••17 Aug 2008
TL;DR: An empirical study of communication structures and delay, as well as task completion times in IBM's distributed development project Jazz, which explicitly focuses on distributed collaboration.
Abstract: Nowadays, distributed development is common in software development. Besides many advantages, research in the last decade has consistently found that distribution has a negative impact on collaboration in general, and communication and task completion time in particular. Adapted processes, practices and tools are demanded to overcome these challenges. We report on an empirical study of communication structures and delay, as well as task completion times in IBM's distributed development project Jazz. The Jazz project explicitly focuses on distributed collaboration and has adapted processes and tools to overcome known challenges. We explored the effect of distance on communication and task completion time and use social network analysis to obtain insights about the collaboration in the Jazz project. We discuss our findings in the light of existing literature on distributed collaboration and delays.
TL;DR: 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 is presented.
TL;DR: It is argued that network analysis can be used, in general, to improve databases, to infer novel functions, to quantify positional importance and to support predictions in pathogenesis studies.
Abstract: In order to better understand several cellular processes, it is helpful to study how various components make up the system. This systems perspective is supported by several modelling tools including network analysis. Networks of protein ^ protein interactions (PPI networks) offer a way to depict, visualize and quantify the functioning and relative importance of particular proteins in cell function. The toolkit of network analysis ranges from the local indices describing individual proteins (as network nodes) to global indicators of system architecture, describing the total interaction system (as the whole network). We briefly introduce some of these network indices and present a case study where the connectedness and potential functional relationships between certain disease proteins are inferred. We argue that network analysis can be used, in general, to improve databases, to infer novel functions, to quantify positional importance and to support predictions in pathogenesis studies. The systems perspective and network analysis can be of particular importance in studying diseases with complex molecular processes.
TL;DR: It is suggested that studying central nodes in mediator networks may contribute to better understanding and quantifying pleiotropy.
Abstract: 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. 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. 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.
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.
01 Feb 2015
TL;DR: In this article, the authors describe the integrative analysis of 111 reference human epigenomes generated as part of the NIH Roadmap Epigenomics Consortium, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression.
Abstract: The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease.
01 Jan 2012
TL;DR: Why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease are detailed.
Abstract: Complex biological systems and cellular networks may underlie most genotype to phenotype relationships. Here, we review basic concepts in network biology, discussing different types of interactome networks and the insights that can come from analyzing them. We elaborate on why interactome networks are important to consider in biology, how they can be mapped and integrated with each other, what global properties are starting to emerge from interactome network models, and how these properties may relate to human disease.
TL;DR: It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates and an optimized protocol of network-aided drug development is suggested, and a list of systems-level hallmarks of drug quality is provided.