The Experts below are selected from a list of 131100 Experts worldwide ranked by ideXlab platform
Kang Ning - One of the best experts on this subject based on the ideXlab platform.
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microbial community Pattern Detection in human body habitats via ensemble clustering framework
arXiv: Quantitative Methods, 2014Co-Authors: Peng Yang, Le Ouyang, Hon Nian Chua, Kang NingAbstract:The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial Patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community Pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering Pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural Patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.
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microbial community Pattern Detection in human body habitats via ensemble clustering framework
BMC Systems Biology, 2014Co-Authors: Peng Yang, Le Ouyang, Hon Nian Chua, Kang NingAbstract:Background: The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural Patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial Patterns effectively. Results: To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community Pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering Pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural Pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. Conclusions: In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.
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microbial community Pattern Detection in human body habitats via ensemble clustering framework
BMC Systems Biology, 2014Co-Authors: Peng Yang, Le Ouyang, Hon Nian Chua, Kang NingAbstract:The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural Patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial Patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community Pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering Pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural Pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.
Marco Zanoni - One of the best experts on this subject based on the ideXlab platform.
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Pattern Detection for conceptual schema recovery in data intensive systems
Journal of Software: Evolution and Process, 2014Co-Authors: Marco Zanoni, Fabrizio Perin, Francesca Arcelli Fontana, Gianluigi ViscusiAbstract:In this paper, an approach for information systems reverse engineering is proposed and applied. The aim is to support a unified perspective to the reverse engineering process of both data and software. At the state of the art, indeed, many methods, techniques, and tools for software reverse engineering have been proposed to support program comprehension, software maintenance, and software evolution. Other approaches and tools have been proposed for data reverse engineering, with the aim, for example, to provide complete and up-to-date documentation of legacy databases. However, the two engineering communities often worked independently, and very few approaches addressed the reverse engineering of both data and software as information system's constituencies. Hence, a higher integration is needed to support a better co-evolution of databases and programs, in an environment often characterized by high availability of data and volatility of information flows. Accordingly, the approach we propose leverages the Detection of object-relational mapping design Patterns to build a conceptual schema of the software under analysis. Then, the conceptual schema is mapped to the domain model of the system, to support the design of the evolution of the information system itself. The approach is evaluated on two large-scale open-source enterprise applications. Copyright © 2014 John Wiley & Sons, Ltd.
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a tool for design Pattern Detection and software architecture reconstruction
Information Sciences, 2011Co-Authors: Francesca Arcelli Fontana, Marco ZanoniAbstract:It is well known that software maintenance and evolution are expensive activities, both in terms of invested time and money. Reverse engineering activities support the obtainment of abstractions and views from a target system that should help the engineers to maintain, evolve and eventually re-engineer it. Two important tasks pursued by reverse engineering are design Pattern Detection and software architecture reconstruction, whose main objectives are the identification of the design Patterns that have been used in the implementation of a system as well as the generation of views placed at different levels of abstractions, which let the practitioners focus on the overall architecture of the system without worrying about the programming details it has been implemented with. In this context we propose an Eclipse plug-in called MARPLE (Metrics and Architecture Reconstruction Plug-in for Eclipse), which supports both the Detection of design Patterns and software architecture reconstruction activities through the use of basic elements and metrics that are mechanically extracted from the source code. The development of this platform is mainly based on the exploitation of the Eclipse framework and plug-ins as well as of different Java libraries for data access and graph management and visualization. In this paper we focus our attention on the design Pattern Detection process.
Peng Yang - One of the best experts on this subject based on the ideXlab platform.
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microbial community Pattern Detection in human body habitats via ensemble clustering framework
arXiv: Quantitative Methods, 2014Co-Authors: Peng Yang, Le Ouyang, Hon Nian Chua, Kang NingAbstract:The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial Patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community Pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering Pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural Patterns. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.
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microbial community Pattern Detection in human body habitats via ensemble clustering framework
BMC Systems Biology, 2014Co-Authors: Peng Yang, Le Ouyang, Hon Nian Chua, Kang NingAbstract:Background: The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural Patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial Patterns effectively. Results: To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community Pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering Pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural Pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. Conclusions: In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.
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microbial community Pattern Detection in human body habitats via ensemble clustering framework
BMC Systems Biology, 2014Co-Authors: Peng Yang, Le Ouyang, Hon Nian Chua, Kang NingAbstract:The human habitat is a host where microbial species evolve, function, and continue to evolve. Elucidating how microbial communities respond to human habitats is a fundamental and critical task, as establishing baselines of human microbiome is essential in understanding its role in human disease and health. Recent studies on healthy human microbiome focus on particular body habitats, assuming that microbiome develop similar structural Patterns to perform similar ecosystem function under same environmental conditions. However, current studies usually overlook a complex and interconnected landscape of human microbiome and limit the ability in particular body habitats with learning models of specific criterion. Therefore, these methods could not capture the real-world underlying microbial Patterns effectively. To obtain a comprehensive view, we propose a novel ensemble clustering framework to mine the structure of microbial community Pattern on large-scale metagenomic data. Particularly, we first build a microbial similarity network via integrating 1920 metagenomic samples from three body habitats of healthy adults. Then a novel symmetric Nonnegative Matrix Factorization (NMF) based ensemble model is proposed and applied onto the network to detect clustering Pattern. Extensive experiments are conducted to evaluate the effectiveness of our model on deriving microbial community with respect to body habitat and host gender. From clustering results, we observed that body habitat exhibits a strong bound but non-unique microbial structural Pattern. Meanwhile, human microbiome reveals different degree of structural variations over body habitat and host gender. In summary, our ensemble clustering framework could efficiently explore integrated clustering results to accurately identify microbial communities, and provide a comprehensive view for a set of microbial communities. The clustering results indicate that structure of human microbiome is varied systematically across body habitats and host genders. Such trends depict an integrated biography of microbial communities, which offer a new insight towards uncovering pathogenic model of human microbiome.
Francesca Arcelli Fontana - One of the best experts on this subject based on the ideXlab platform.
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Pattern Detection for conceptual schema recovery in data intensive systems
Journal of Software: Evolution and Process, 2014Co-Authors: Marco Zanoni, Fabrizio Perin, Francesca Arcelli Fontana, Gianluigi ViscusiAbstract:In this paper, an approach for information systems reverse engineering is proposed and applied. The aim is to support a unified perspective to the reverse engineering process of both data and software. At the state of the art, indeed, many methods, techniques, and tools for software reverse engineering have been proposed to support program comprehension, software maintenance, and software evolution. Other approaches and tools have been proposed for data reverse engineering, with the aim, for example, to provide complete and up-to-date documentation of legacy databases. However, the two engineering communities often worked independently, and very few approaches addressed the reverse engineering of both data and software as information system's constituencies. Hence, a higher integration is needed to support a better co-evolution of databases and programs, in an environment often characterized by high availability of data and volatility of information flows. Accordingly, the approach we propose leverages the Detection of object-relational mapping design Patterns to build a conceptual schema of the software under analysis. Then, the conceptual schema is mapped to the domain model of the system, to support the design of the evolution of the information system itself. The approach is evaluated on two large-scale open-source enterprise applications. Copyright © 2014 John Wiley & Sons, Ltd.
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a tool for design Pattern Detection and software architecture reconstruction
Information Sciences, 2011Co-Authors: Francesca Arcelli Fontana, Marco ZanoniAbstract:It is well known that software maintenance and evolution are expensive activities, both in terms of invested time and money. Reverse engineering activities support the obtainment of abstractions and views from a target system that should help the engineers to maintain, evolve and eventually re-engineer it. Two important tasks pursued by reverse engineering are design Pattern Detection and software architecture reconstruction, whose main objectives are the identification of the design Patterns that have been used in the implementation of a system as well as the generation of views placed at different levels of abstractions, which let the practitioners focus on the overall architecture of the system without worrying about the programming details it has been implemented with. In this context we propose an Eclipse plug-in called MARPLE (Metrics and Architecture Reconstruction Plug-in for Eclipse), which supports both the Detection of design Patterns and software architecture reconstruction activities through the use of basic elements and metrics that are mechanically extracted from the source code. The development of this platform is mainly based on the exploitation of the Eclipse framework and plug-ins as well as of different Java libraries for data access and graph management and visualization. In this paper we focus our attention on the design Pattern Detection process.
Matthew G Crowson - One of the best experts on this subject based on the ideXlab platform.
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machine learning for Pattern Detection in cochlear implant fda adverse event reports
Cochlear Implants International, 2020Co-Authors: Matthew G Crowson, Amr Hamour, Vincent Lin, Joseph M Chen, Timothy C Y ChanAbstract:Importance: Medical device performance and safety databases can be analyzed for Patterns and novel opportunities for improving patient safety and/or device design. Objective: The objective of this ...
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machine learning for Pattern Detection in cochlear implant fda adverse event reports
medRxiv, 2020Co-Authors: Matthew G Crowson, Amr Hamour, Vincent Lin, Joseph M Chen, Timothy C Y ChanAbstract:ABSTRACT Importance The United States Food & Drug Administration (FDA) passively monitors medical device performance and safety through submitted medical device reports (MDRs) in the Manufacturer and User Facility Device Experience (MAUDE) database. These databases can be analyzed for Patterns and novel opportunities for improving patient safety and/or device design. Objectives The objective of this analysis was to use supervised machine learning to explore Patterns in reported adverse events involving cochlear implants. Design The MDRs for the top three CI manufacturers by volume from January 1st 2009 to August 30th 2019 were retained for the analysis. Natural language processing was used to measure the importance of specific words. Four supervised machine learning algorithms were used to predict which adverse event narrative description Pattern corresponded with a specific cochlear implant manufacturer and adverse event type - injury, malfunction, or death. Setting U.S. government public database. Participants Adult and pediatric cochlear patients. Exposure Surgical placement of a cochlear implant. Main Outcome Measure Machine learning model classification prediction accuracy (% correct predictions). Results 27,511 adverse events related to cochlear implant devices were submitted to the MAUDE database during the study period. Most adverse events involved patient injury (n = 16,736), followed by device malfunction (n = 10,760), and death (n = 16). Submissions to the database were dominated by Cochlear Corporation (n = 13,897), followed by MedEL (n = 7,125), and Advanced Bionics (n = 6,489). The random forest, linear SVC, naive Bayes and logistic algorithms were able to predict the specific CI manufacturer based on the adverse event narrative with an average accuracy of 74.8%, 86.0%, 88.5% and 88.6%, respectively. Conclusions & Relevance Using supervised machine learning algorithms, our classification models were able to predict the CI manufacturer and event type with high accuracy based on Patterns in adverse event text descriptions. Level of evidence 3
