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"abstract": "Shared understandings are important for software development as they guide effective individual contributions and coordination of the software development process. However, it is not clear if such understandings can be developed in highly distributed groups that do not regularly meet face-to-face. In this paper, we present the results of a preliminary analysis of shared mental models within a Free/Libre Open Source Software (FLOSS) development team. We analyzed mental models using cognitive mapping and process analysis and compared the models of four developers from the Apache Lucene Java project. Our analysis suggests that there is a high level of sharing among core developers but the sharing is not complete, with some differences related to tenure and role in the project. Finally, we suggest directions for further research on shared mental models in FLOSS teams.",
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"abstract": "The need to understand and track files (and inherently, data) in cloud computing systems is in high demand. Over the past years, the use of logs and data representation using graphs have become the main method for tracking and relating information to the cloud users. While being used, tracking related information with 'data provenance' (i.e. series of chronicles and the derivation history of data on metadata) is the new trend for cloud users. However, there is still much room for improving data activity representation in cloud systems for end-users. We propose \"User-centric Visualization of data provenance with Gestalt (UVisP)\", a novel user-centric visualization technique for data provenance. This technique aims to facilitate the missing link between data movements in cloud computing environments and the end-users uncertain queries over their files security and life cycle within cloud systems. The proof of concept for the UVisP technique integrates an open-source visualization API with Gestalt's theory of perception to provide a range of user-centric provenance visualizations. UVisP allows users to transform and visualize provenance (logs) with implicit prior knowledge of 'Gestalt's theory of perception.' We presented the initial development of the UVisP technique and our results show that the integration of Gestalt and 'perceptual key(s)' in provenance visualization allows end-users to enhance their visualizing capabilities, to extract useful knowledge and understand the visualizations better.",
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"abstract": "In wireless sensor networks (WSNs), provenance is vital for assessing data's trustworthiness, detecting the misbehaviors conducted by adversaries or troubleshooting communication failures. The provenance can be encoded through fingerprinting the node IDs along a packet path where the packets are generated, forwarded and/or aggregated. Because WSNs are resource-tightened networks, most of the known provenance schemes applied in WSNs address the issues on how to reduce the provenance size with various compression techniques only. However, reducing the provenance size at a sensor node also costs too much energy. In addition, such schemes did not take the secure and persistant provenance storage for consideration in a long term. To fill the gap, we propose a blockchainbased data provenance scheme (BCP) of compression free, where the provenances are stored distributively on the nodes along the packet path and the BS can retrieve the provenance on demand through a query process. An edge computing based monitor network consisting of high performance nodes (H-nodes) is deployed above or nearby the WSNs, which keeps the WSN's provenance data in a blockchain-based database. The security and authenticity of the provenances are then protected. What's more, the WSN is released from consuming much energy in handling provenance data, which is more superior to all the previous schemes. Both the simulation and experiment results show that our scheme BCP is more energy efficient and secure than those of the known distributed data provenances.",
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"content": "In wireless sensor networks (WSNs), provenance is vital for assessing data's trustworthiness, detecting the misbehaviors conducted by adversaries or troubleshooting communication failures. The provenance can be encoded through fingerprinting the node IDs along a packet path where the packets are generated, forwarded and/or aggregated. Because WSNs are resource-tightened networks, most of the known provenance schemes applied in WSNs address the issues on how to reduce the provenance size with various compression techniques only. However, reducing the provenance size at a sensor node also costs too much energy. In addition, such schemes did not take the secure and persistant provenance storage for consideration in a long term. To fill the gap, we propose a blockchainbased data provenance scheme (BCP) of compression free, where the provenances are stored distributively on the nodes along the packet path and the BS can retrieve the provenance on demand through a query process. An edge computing based monitor network consisting of high performance nodes (H-nodes) is deployed above or nearby the WSNs, which keeps the WSN's provenance data in a blockchain-based database. The security and authenticity of the provenances are then protected. What's more, the WSN is released from consuming much energy in handling provenance data, which is more superior to all the previous schemes. Both the simulation and experiment results show that our scheme BCP is more energy efficient and secure than those of the known distributed data provenances.",
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"abstract": "Many complex networks commonly exhibit community structure in their underlying organizations, i.e., They contain multiple groups of nodes having more connections inside a group and less interactions among groups. This special structure not only offers key insights into understanding the network organization principles but also plays a vital role in maintaining the normal function of the whole system. As a result, any significant change to the network communities, due to element-wise failures, can potentially redefine their organizational structures and consequently lead to the malfunction or undesirable corruption of the entire system. Therefore, identifying network elements that are essential to its community structure is a fundamental and important problem. However, to the best of our knowledge, this research direction has not received much been attention in the literature. In this paper, we study the structural vulnerability of overlapping complex network communities to identify nodes that are important in maintaining the complex structure organization. Specifically, given a network and a budget of k nodes, we want to identify k critical nodes whose their exclusions transforms the current network community structure. To effectively analyze this vulnerability on overlapping communities, we propose the concept of generating edges and provide an optimal algorithm for detecting the Minimal Generating Edge Set (MGES) in a network community. We suggest genEdge, an effective solution based on this MGES. Empirical results on both synthesized networks with known community structures, and real data including Reality cellular data, Foursquare and Facebook social traces confirm the efficacy of our approach.",
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{
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"content": "Many complex networks commonly exhibit community structure in their underlying organizations, i.e., They contain multiple groups of nodes having more connections inside a group and less interactions among groups. This special structure not only offers key insights into understanding the network organization principles but also plays a vital role in maintaining the normal function of the whole system. As a result, any significant change to the network communities, due to element-wise failures, can potentially redefine their organizational structures and consequently lead to the malfunction or undesirable corruption of the entire system. Therefore, identifying network elements that are essential to its community structure is a fundamental and important problem. However, to the best of our knowledge, this research direction has not received much been attention in the literature. In this paper, we study the structural vulnerability of overlapping complex network communities to identify nodes that are important in maintaining the complex structure organization. Specifically, given a network and a budget of k nodes, we want to identify k critical nodes whose their exclusions transforms the current network community structure. To effectively analyze this vulnerability on overlapping communities, we propose the concept of generating edges and provide an optimal algorithm for detecting the Minimal Generating Edge Set (MGES) in a network community. We suggest genEdge, an effective solution based on this MGES. Empirical results on both synthesized networks with known community structures, and real data including Reality cellular data, Foursquare and Facebook social traces confirm the efficacy of our approach.",
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"abstract": "One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. Identifying such overlapping communities is crucial for the understanding the structure as well as the function of real-world networks. Even though community structure in networks has been widely studied in the past, practically all research makes an implicit assumption that overlaps between communities are less densely connected than the non-overlapping parts themselves. Here we validate this assumption on 6 large scale social, collaboration and information networks where nodes explicitly state their community memberships. By examining such ground-truth communities we find that the community overlaps are more densely connected than the non-overlapping parts, which is in sharp contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Practically all existing community detection methods fail to detect communities with dense overlaps. We propose Community-Affiliation Graph Model, a model-based community detection method that builds on bipartite node-community affiliation networks. Our method successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.",
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"content": "One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Communities in networks often overlap as nodes can belong to multiple communities at once. Identifying such overlapping communities is crucial for the understanding the structure as well as the function of real-world networks. Even though community structure in networks has been widely studied in the past, practically all research makes an implicit assumption that overlaps between communities are less densely connected than the non-overlapping parts themselves. Here we validate this assumption on 6 large scale social, collaboration and information networks where nodes explicitly state their community memberships. By examining such ground-truth communities we find that the community overlaps are more densely connected than the non-overlapping parts, which is in sharp contrast to the conventional wisdom that community overlaps are more sparsely connected than the communities themselves. Practically all existing community detection methods fail to detect communities with dense overlaps. We propose Community-Affiliation Graph Model, a model-based community detection method that builds on bipartite node-community affiliation networks. Our method successfully captures overlapping, non-overlapping as well as hierarchically nested communities, and identifies relevant communities more accurately than the state-of-the-art methods in networks ranging from biological to social and information networks.",
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"proceeding": {
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"title": "2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)",
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"doi": "10.1109/ASONAM.2014.6921582",
"title": "Detecting highly overlapping community structure based on Maximal Clique Networks",
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"abstract": "Most of overlapping community detection algorithms cannot be applied to networks with highly overlapping community such as online social networks where individuals belong to many communities. One important reason is that many algorithms detect communities based on the explicit borders where nodes have more connections inside the communities, however, when the vertices' membership number gets large, the explicit borders between communities will fade away. To overcome this disadvantage, a new algorithm named MCNLPA is proposed by expanding the traditional Label Propagation Algorithm (LPA) based on the Maximal Clique Network for highly overlapping community detection. By finding all maximal cliques in networks and defining reasonable edges between them, the maximal clique network is established. Then the updated rule of classic LPA is modified to apply to the maximal network. Experiments show that MCNLPA has a relatively good performance in highly overlapping community detection and overlapping nodes identification.",
"abstracts": [
{
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"content": "Most of overlapping community detection algorithms cannot be applied to networks with highly overlapping community such as online social networks where individuals belong to many communities. One important reason is that many algorithms detect communities based on the explicit borders where nodes have more connections inside the communities, however, when the vertices' membership number gets large, the explicit borders between communities will fade away. To overcome this disadvantage, a new algorithm named MCNLPA is proposed by expanding the traditional Label Propagation Algorithm (LPA) based on the Maximal Clique Network for highly overlapping community detection. By finding all maximal cliques in networks and defining reasonable edges between them, the maximal clique network is established. Then the updated rule of classic LPA is modified to apply to the maximal network. Experiments show that MCNLPA has a relatively good performance in highly overlapping community detection and overlapping nodes identification.",
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"normalizedAbstract": "Most of overlapping community detection algorithms cannot be applied to networks with highly overlapping community such as online social networks where individuals belong to many communities. One important reason is that many algorithms detect communities based on the explicit borders where nodes have more connections inside the communities, however, when the vertices' membership number gets large, the explicit borders between communities will fade away. To overcome this disadvantage, a new algorithm named MCNLPA is proposed by expanding the traditional Label Propagation Algorithm (LPA) based on the Maximal Clique Network for highly overlapping community detection. By finding all maximal cliques in networks and defining reasonable edges between them, the maximal clique network is established. Then the updated rule of classic LPA is modified to apply to the maximal network. Experiments show that MCNLPA has a relatively good performance in highly overlapping community detection and overlapping nodes identification.",
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"affiliation": "School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China 200240",
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"affiliation": "School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, China 200240",
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"title": "2015 IEEE Pacific Visualization Symposium (PacificVis)",
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"doi": "10.1109/PACIFICVIS.2015.7156355",
"title": "Interactive visual summary of major communities in a large network",
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"abstract": "In this paper, we introduce a novel visualization method which allows people to explore, compare and refine the major communities in a large network. We first detect major communities in a network using data mining and community analysis methods. Then, the statistics attributes of each community, the relational strength between communities, and the boundary nodes connecting those communities are computed and stored. We propose a novel method based on Voronoi treemap to encode each community with a polygon and the relative positions of polygons encode their relational strengths. Different community attributes can be encoded by polygon shapes, sizes and colors. A corner-cutting method is further introduced to adjust the smoothness of polygons based on certain community attribute. To accommodate the boundary nodes, the gaps between the polygons are widened by a polygon-shrinking algorithm such that the boundary nodes can be conveniently embedded into the newly created spaces. The method is very efficient, enabling users to test different community detection algorithms, fine tune the results, and explore the fuzzy relations between communities interactively. The case studies with two real data sets demonstrate that our approach can provide a visual summary of major communities in a large network, and help people better understand the characteristics of each community and inspect various relational patterns between communities.",
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{
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"content": "In this paper, we introduce a novel visualization method which allows people to explore, compare and refine the major communities in a large network. We first detect major communities in a network using data mining and community analysis methods. Then, the statistics attributes of each community, the relational strength between communities, and the boundary nodes connecting those communities are computed and stored. We propose a novel method based on Voronoi treemap to encode each community with a polygon and the relative positions of polygons encode their relational strengths. Different community attributes can be encoded by polygon shapes, sizes and colors. A corner-cutting method is further introduced to adjust the smoothness of polygons based on certain community attribute. To accommodate the boundary nodes, the gaps between the polygons are widened by a polygon-shrinking algorithm such that the boundary nodes can be conveniently embedded into the newly created spaces. The method is very efficient, enabling users to test different community detection algorithms, fine tune the results, and explore the fuzzy relations between communities interactively. The case studies with two real data sets demonstrate that our approach can provide a visual summary of major communities in a large network, and help people better understand the characteristics of each community and inspect various relational patterns between communities.",
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"fno": "07156355",
"keywords": [
"Communities",
"Layout",
"Visualization",
"Image Color Analysis",
"Clustering Algorithms",
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"authors": [
{
"affiliation": "Hong Kong University of Science Technology, Huawei Technologies Co., Ltd., China",
"fullName": "Yanhong Wu",
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"affiliation": "Hong Kong University of Science Technology, Huawei Technologies Co., Ltd., China",
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"affiliation": "Hong Kong University of Science Technology, Huawei Technologies Co., Ltd., China",
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"affiliation": "Hong Kong University of Science Technology, Huawei Technologies Co., Ltd., China",
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"affiliation": "Hong Kong University of Science Technology, Huawei Technologies Co., Ltd., China",
"fullName": "Huamin Qu",
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"abstract": "Many well-formalized program transformations rely on techniques derived from the linear algebra theory. In such transformations, program entities are represented using polyhedra, which are then transformed using linear or affine functions. However, reasoning within this abstract framework is made extremely difficult by high dimensionality of spaces used to represent complex program transformations and various entities in the resulting programs: data, sets, iteration domains, access functions etc. This difficulty can be alleviated, at least partly, by providing tools for interactive visualization and manipulation of polyhedra and integrating such tools into a programming environment. In this paper we explore the issues involved in designing an interactive visualization tool for high-dimensionality polyhedra, and discuss the possible research directions arising from our current experience.",
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"abstract": "Overlapping set visualization is a well-known problem in information visualization. This problem considers elements and sets containing all or part of the elements, a given element possibly belonging to more than one set. A typical example is the properties of the 20 amino-acids. A more complex application is the visual comparison of the contraindications or the adverse effects of several similar drugs. The knowledge involved is voluminous, each drug has many contraindications and adverse effects, some of them are shared with other drugs. In this paper, we present rainbow boxes, a novel technique for visualizing overlapping sets, and its application to the properties of amino-acids and to the comparison of drug properties. We also describe a user study comparing rainbow boxes to tables and showing that the former allowed physicians to find information significantly faster. We finally discuss the limits and the perspectives of rainbow boxes.",
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"content": "Overlapping set visualization is a well-known problem in information visualization. This problem considers elements and sets containing all or part of the elements, a given element possibly belonging to more than one set. A typical example is the properties of the 20 amino-acids. A more complex application is the visual comparison of the contraindications or the adverse effects of several similar drugs. The knowledge involved is voluminous, each drug has many contraindications and adverse effects, some of them are shared with other drugs. In this paper, we present rainbow boxes, a novel technique for visualizing overlapping sets, and its application to the properties of amino-acids and to the comparison of drug properties. We also describe a user study comparing rainbow boxes to tables and showing that the former allowed physicians to find information significantly faster. We finally discuss the limits and the perspectives of rainbow boxes.",
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"abstract": "The discrete nature of categorical data makes it a particular challenge for visualization. Methods that work very well for continuous data are often hardly usable with categorical dimensions. Only few methods deal properly with such data, mostly because of the discrete nature of categorical data, which does not translate well into the continuous domains of space and color. Parallel sets is a new visualization method that adopts the layout of parallel coordinates, but substitutes the individual data points by a frequency based representation. This abstracted view, combined with a set of carefully designed interactions, supports visual data analysis of large and complex data sets. The technique allows efficient work with meta data, which is particularly important when dealing with categorical datasets. By creating new dimensions from existing ones, for example, the user can filter the data according to his or her current needs. We also present the results from an interactive analysis of CRM data using parallel sets. We demonstrate how the flexible layout eases the process of knowledge crystallization, especially when combined with a sophisticated interaction scheme.",
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"abstract": "Interactive rendering of large data sets requires efficient algorithms and accelerated graphics hardware. Both efficiency and speed can be improved, but none of these alone guarantees interactive response time. If a scene is too complex, performance decreases, and neither faster algorithms nor speeding up hardware can ensure interactive performance. This means that certain timing characteristics need to be incorporated in order to obtain interactive behavior.In this paper we propose a new approach to interactive rendering by introducing a rendering pipeline with special timing predicates. We use this technique for applications in the field of medical imaging. Large data sets derived from CT or MRI scans and CAD designs must be rendered in real-time. The system should provide immediate feedback to the user. Interactive behavior enables the user to manipulate and adjust the image straight on demand.",
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"abstract": "The automatic detection of meaningful phases in a soccer game depends on the accurate localization of players and the ball at each moment. However, the automatic analysis of soccer sequences is a challenging task due to the presence of fast moving multiple objects. For this purpose, we present a multi-camera analysis system that yields the position of the ball and players on a common ground plane. The detection in each camera is based on a code-book algorithm and different features are used to classify the detected blobs. The detection results of each camera are transformed using homography to a virtual top-view of the playing field. Within this virtual top-view we merge trajectory information of the different cameras allowing to refine the found positions. In this paper, we evaluate the system on a public SOCCER dataset and end with a discussion of possible improvements of the dataset.",
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"abstract": "The analysis of large sums of soccer statistics can be extremely difficult if the data is not presented graphically. As a result, we developed an application, titled Soccer Scoop, which provides two separate visualizations that can aid a soccer team manager. With our application, a team manager can compare two players on different teams, analyze a particular player before signing them to a contract, measure the performance of a particular player at different positions, generate practice exercises, and determine if a particular player plays better on the road or at home. The visualizations used in our application apply information visualization techniques, such as glyphs, modified star plots, details on demand, color, and gestalt principles.",
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"abstract": "The software ecosystem paradigm is a means for companies to obtain benefits by joining a network of partners that supply infrastructure, software and services for a shared market. In this paper, we analyse the relationships among partners participating in software ecosystems by means of established power theories. We report the findings of a multiple case study of two software ecosystems formed by small-to-medium enterprises. Our main contribution is to investigate the role of power in software ecosystems by describing how companies exercise coercive, expert, legitimate, referent and reward power. In addition, we explore how power-balancing operations can lead these partnerships to thrive and sustain the evolution of software ecosystems.",
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"title": "Aligning Platform Ecosystems Through Product Roadmapping: Systematic Mapping Study and Research Agenda",
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"abstract": "Providing a digital infrastructure, platform technologies foster interfirm collaboration between loosely coupled companies, enabling the formation of ecosystems and building the organizational structure for value co-creation. Despite the known potential, the development of platform ecosystems creates new sources of complexity and uncertainty due to the involvement of various independent actors. For a platform ecosystem to succeed, it is essential that the platform ecosystem participants are aligned, coordinated, and given a common direction. Traditionally, product roadmaps have served these purposes during product development. A systematic mapping study was conducted to better understand how product roadmapping could be used in the dynamic environment of platform ecosystems. One result of the study is that there are hardly any concrete approaches for product roadmapping in platform ecosystems so far. However, many challenges on the topic are described in the literature from different perspectives. Based on the results of the systematic mapping study, a research agenda for product roadmapping in platform ecosystems is derived and presented.",
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"affiliation": "Reutlingen University,Department of Computer Science,Reutlingen,Germany",
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"abstract": "Biological interpretation and understanding of machine learning based predictive models are highly desirable in healthcare analytics. Predicting Adverse Drug Reactions (ADRs) is extremely important for safe and precision medicine. There are various machine learning based approaches to predict adverse reactions for drugs. These models, though effective, lack biological interpretation and are treated as black-boxes. We propose DrugPathSeeker, a novel interactive user interface that integrates the machine learning model, database query API, statistical analysis, and visualization for exploring and understanding of the association between drugs and ADRs. The proposed UI can take a query drug, and provide a visual interface designed to support exploration of the predictions from the machine learning model for further understanding and interpretation. DrugPathSeeker uses a machine learning model, Small Molecular Risk Profiler, to make ADR predictions for a given drug. The visualization uses Sankey type flow diagrams for highlighting the relation between the drugs and ADRs. The main goal of Drug-PathSeeker is to mine the gene-pathways from public databases and analyze them in a visual manner to generate a biological hypothesis. DrugPathSeeker's effectiveness is demonstrated with two use cases: mechanisms of action for carbamazepine-induced dystonia, and fluorometholone-induced diabetes mellitus.",
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"content": "Biological interpretation and understanding of machine learning based predictive models are highly desirable in healthcare analytics. Predicting Adverse Drug Reactions (ADRs) is extremely important for safe and precision medicine. There are various machine learning based approaches to predict adverse reactions for drugs. These models, though effective, lack biological interpretation and are treated as black-boxes. We propose DrugPathSeeker, a novel interactive user interface that integrates the machine learning model, database query API, statistical analysis, and visualization for exploring and understanding of the association between drugs and ADRs. The proposed UI can take a query drug, and provide a visual interface designed to support exploration of the predictions from the machine learning model for further understanding and interpretation. DrugPathSeeker uses a machine learning model, Small Molecular Risk Profiler, to make ADR predictions for a given drug. The visualization uses Sankey type flow diagrams for highlighting the relation between the drugs and ADRs. The main goal of Drug-PathSeeker is to mine the gene-pathways from public databases and analyze them in a visual manner to generate a biological hypothesis. DrugPathSeeker's effectiveness is demonstrated with two use cases: mechanisms of action for carbamazepine-induced dystonia, and fluorometholone-induced diabetes mellitus.",
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"normalizedAbstract": "Biological interpretation and understanding of machine learning based predictive models are highly desirable in healthcare analytics. Predicting Adverse Drug Reactions (ADRs) is extremely important for safe and precision medicine. There are various machine learning based approaches to predict adverse reactions for drugs. These models, though effective, lack biological interpretation and are treated as black-boxes. We propose DrugPathSeeker, a novel interactive user interface that integrates the machine learning model, database query API, statistical analysis, and visualization for exploring and understanding of the association between drugs and ADRs. The proposed UI can take a query drug, and provide a visual interface designed to support exploration of the predictions from the machine learning model for further understanding and interpretation. DrugPathSeeker uses a machine learning model, Small Molecular Risk Profiler, to make ADR predictions for a given drug. The visualization uses Sankey type flow diagrams for highlighting the relation between the drugs and ADRs. The main goal of Drug-PathSeeker is to mine the gene-pathways from public databases and analyze them in a visual manner to generate a biological hypothesis. DrugPathSeeker's effectiveness is demonstrated with two use cases: mechanisms of action for carbamazepine-induced dystonia, and fluorometholone-induced diabetes mellitus.",
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"abstract": "Drugs and biological experiments are designed to affect a particular target gene or pathway. However, they might inadvertently activate other pathways and cause side effects.Because of the existence of complex cellular mechanisms responding to stimuli,it is difficult to detect the presence of such side effects.Therefore, identification of pathways that function together under identical conditions would greatly help in anticipating these side effects before conducting these experiments. We develop a novel method to enumerate ``cooperative pathways'' defined as pathways that functiontogether under identical conditionsby combining pathway networks withcomprehensive gene expression profiles.For finding cooperative pathways from whole pathways,we propose an efficient algorithm, CoopeRativE Pathway Enumerator (CREPE),which enumerates connected subpathways having common activate conditionsand selects combinations of the subpathways sharing the conditions.%We apply CREPE to a yeast stress dataset combined with the KEGG pathways.We observe that the starch and sucrose metabolism pathwaycooperates with the pyruvate metabolism under heat shock stresses. It cooperates with the tricarboxylic acid (TCA) cycle under the stationary phases.",
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"content": "Drugs and biological experiments are designed to affect a particular target gene or pathway. However, they might inadvertently activate other pathways and cause side effects.Because of the existence of complex cellular mechanisms responding to stimuli,it is difficult to detect the presence of such side effects.Therefore, identification of pathways that function together under identical conditions would greatly help in anticipating these side effects before conducting these experiments. We develop a novel method to enumerate ``cooperative pathways'' defined as pathways that functiontogether under identical conditionsby combining pathway networks withcomprehensive gene expression profiles.For finding cooperative pathways from whole pathways,we propose an efficient algorithm, CoopeRativE Pathway Enumerator (CREPE),which enumerates connected subpathways having common activate conditionsand selects combinations of the subpathways sharing the conditions.%We apply CREPE to a yeast stress dataset combined with the KEGG pathways.We observe that the starch and sucrose metabolism pathwaycooperates with the pyruvate metabolism under heat shock stresses. It cooperates with the tricarboxylic acid (TCA) cycle under the stationary phases.",
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"normalizedAbstract": "Drugs and biological experiments are designed to affect a particular target gene or pathway. However, they might inadvertently activate other pathways and cause side effects.Because of the existence of complex cellular mechanisms responding to stimuli,it is difficult to detect the presence of such side effects.Therefore, identification of pathways that function together under identical conditions would greatly help in anticipating these side effects before conducting these experiments. We develop a novel method to enumerate ``cooperative pathways'' defined as pathways that functiontogether under identical conditionsby combining pathway networks withcomprehensive gene expression profiles.For finding cooperative pathways from whole pathways,we propose an efficient algorithm, CoopeRativE Pathway Enumerator (CREPE),which enumerates connected subpathways having common activate conditionsand selects combinations of the subpathways sharing the conditions.%We apply CREPE to a yeast stress dataset combined with the KEGG pathways.We observe that the starch and sucrose metabolism pathwaycooperates with the pyruvate metabolism under heat shock stresses. It cooperates with the tricarboxylic acid (TCA) cycle under the stationary phases.",
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"title": "IsoKEGG: A logic based system for querying biological pathways in KEGG",
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"abstract": "Understanding the interaction patterns among a set of biological entities in a pathway is an important exercise because it potentially could reveal the role of the entities in biological systems. Although a considerable amount of effort has been directed to the detection and mining of patterns in biological pathways in contemporary research, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways that satisfy a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways based on a novel and generic subgraph isomorphism computation technique. We cast this technique into a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways. We demonstrate that IsoKEGG is flexible enough to allow querying based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It also allows editing KGML represented query pathways and returns all possible pathways in KEGG that satisfy a given query condition that the users are able to investigate further.",
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"content": "Understanding the interaction patterns among a set of biological entities in a pathway is an important exercise because it potentially could reveal the role of the entities in biological systems. Although a considerable amount of effort has been directed to the detection and mining of patterns in biological pathways in contemporary research, querying biological pathways remained relatively unexplored. Querying is principally different in which we retrieve pathways that satisfy a given property in terms of its topology, or constituents. One such property is subnetwork matching using various constituent parameters. In this paper, we introduce a logic based framework for querying biological pathways based on a novel and generic subgraph isomorphism computation technique. We cast this technique into a graphical interface called IsoKEGG to facilitate flexible querying of KEGG pathways. We demonstrate that IsoKEGG is flexible enough to allow querying based on isomorphic pathway topologies as well as matching any combination of node names, types, and edges. It also allows editing KGML represented query pathways and returns all possible pathways in KEGG that satisfy a given query condition that the users are able to investigate further.",
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"affiliation": "Department of Computer Science, Wayne State University, USA",
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"affiliation": "Department of Computer Science, Wayne State University, USA",
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"affiliation": "Department of Computer Science, Wayne State University, USA",
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"abstract": "Huntington's disease (HD), a rare autosomal dominant disease, affecting the medium spiny neurons of the CNS. Although HD is caused by a trinucleotide repeat in the HTT gene, it is a complex disease. Systems Bioinformatics which combines systems biology and bioinformatics, has the ability to reveal synergistic relationships between multiple entities. This approach is vital as it can shed light on the biological behavior and mechanisms of the cell rather than only trying to study and understand a part of the system. Metabolomics is the systematic study and measurement of metabolites within a biological sample. In this work, we employ two approaches to identify metabolites for HD-related pathways, which were previously identified from our previous work on multi-source data integration These include: i) creation of pathway-to-pathway networks based on the reference network of PathwayConnector where pathways are mapped based on connectivity on KEGG, and (ii) creation of pathway-to-pathway networks using a pairwise approach, where a connection between two pathways exists only if they share common metabolites.",
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"abstract": "With the increasing complexity of the Grid based application workflows, the workflow design process is also getting more and more complex. Many workflow design tools provide mechanism to ease the workflow design process and to make the life easier for a workflow designer. In this paper, we present a provenance based workflow design suggestion system for quick and easy creation of the error-free workflows. In our approach, the provenance system intercepts the users' actions, processes, stores these actions in the provenance store and provides suggestions about possible subsequent actions for the workflow design. These suggested actions are based on the current user actions and are calculated using the provenance information available in the provenance store. These design suggestions partially automate the design process providing ease of use, reliability and correctness during the workflow design process. Creating error-free workflows is of pivotal importance in distributed execution environments. Increasing complexity in the designing of these complex workflows is making the design process more error prone and tedious. Taking into account the significance of correctness of the Grid based workflow and realizing the importance of the design-time in the life of a workflow based application, we present a novel approach of using recorded provenance information.",
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