The Experts below are selected from a list of 276681 Experts worldwide ranked by ideXlab platform
Anne E Carpenter - One of the best experts on this subject based on the ideXlab platform.
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cellprofiler analyst Interactive Data exploration analysis and classification of large biological image sets
Bioinformatics, 2016Co-Authors: David Dao, Adam Fraser, Jane Hung, Vebjorn Ljosa, Shantanu Singh, Anne E CarpenterAbstract:Summary: CellProfiler Analyst allows the exploration and visualization of image-based Data, together with the classification of complex biological phenotypes, via an Interactive user interface designed for biologists and Data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). Availability and Implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process that supports nightly updates and regular release cycles for the software. Contact: gro.etutitsnidaorb@enna Supplementary information: Supplementary Data are available at Bioinformatics online.
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cellprofiler analyst Interactive Data exploration analysis and classification of large biological image sets
bioRxiv, 2016Co-Authors: David Dao, Adam Fraser, Jane Hung, Vebjorn Ljosa, Shantanu Singh, Anne E CarpenterAbstract:Summary: CellProfiler Analyst allows the exploration and visualization of image-based Data, together with the classification of complex biological phenotypes, via an Interactive user interface designed for biologists and Data scientists. CellProfiler Analyst 2.0, completely rewritten in Python, builds on these features and adds enhanced supervised machine learning capabilities (in Classifier), as well as visualization tools to overview an experiment (Plate Viewer and Image Gallery). Availability and implementation: CellProfiler Analyst 2.0 is free and open source, available at http://www.cellprofiler.org and from GitHub (https://github.com/CellProfiler/CellProfiler-Analyst) under the BSD license. It is available as a packaged application for Mac OS X and Microsoft Windows and can be compiled for Linux. We implemented an automatic build process which supports nightly updates and regular release cycles for the software.
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cellprofiler analyst Data exploration and analysis software for complex image based screens
BMC Bioinformatics, 2008Co-Authors: Thouis R Jones, In Han Kang, Douglas B Wheeler, Robert A Lindquist, Adam Papallo, David M Sabatini, Polina Golland, Anne E CarpenterAbstract:Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment. Here, we describe CellProfiler Analyst, open-source software for the Interactive exploration and analysis of multidimensional Data, particularly Data from high-throughput, image-based experiments. The system enables Interactive Data exploration for image-based screens and automated scoring of complex phenotypes that require combinations of multiple measured features per cell.
Satoru Miyano - One of the best experts on this subject based on the ideXlab platform.
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sensitivity analysis of agent based simulation utilizing massively parallel computation and Interactive Data visualization
PLOS ONE, 2019Co-Authors: Atsushi Niida, Takanori Hasegawa, Satoru MiyanoAbstract:An essential step in the analysis of agent-based simulation is sensitivity analysis, which namely examines the dependency of parameter values on simulation results. Although a number of approaches have been proposed for sensitivity analysis, they still have limitations in exhaustivity and interpretability. In this study, we propose a novel methodology for sensitivity analysis of agent-based simulation, MASSIVE (Massively parallel Agent-based Simulations and Subsequent Interactive Visualization-based Exploration). MASSIVE takes a unique paradigm, which is completely different from those of sensitivity analysis methods developed so far, By combining massively parallel computation and Interactive Data visualization, MASSIVE enables us to inspect a broad parameter space intuitively. We demonstrated the utility of MASSIVE by its application to cancer evolution simulation, which successfully identified conditions that generate heterogeneous tumors. We believe that our approach would be a de facto standard for sensitivity analysis of agent-based simulation in an era of evergrowing computational technology. All the results form our MASSIVE analysis are available at https://www.hgc.jp/~niiyan/massive.
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sensitivity analysis of agent based simulation utilizing massively parallel computation and Interactive Data visualization
bioRxiv, 2019Co-Authors: Atsushi Niida, Takanori Hasegawa, Satoru MiyanoAbstract:Abstract An essential step in the analysis of agent-based simulation is sensitivity analysis, which namely examines the dependency of parameter values on simulation results. Although a number of approaches have been proposed for sensitivity analysis, they still have limitations in exhaustivity and interpretability. In this study, we propose a novel methodology for sensitivity analysis of agent-based simulation, MASSIVE (Massively parallel Agent-based Simulations and Subsequent Interactive Visualization-based Exploration). MASSIVE takes a unique paradigm, which is completely different from those of sensitivity analysis methods developed so far, By combining massively parallel computation and Interactive Data visualization, MASSIVE enables us to inspect a broad parameter space intuitively. We demonstrated the utility of MASSIVE by its application to cancer evolution simulation, which successfully identified conditions that generate heterogeneous tumors. We believe that our approach would be a de facto standard for sensitivity analysis of agent-based simulation in an era of ever-growing computational technology. All the result form our MASSIVE analysis is available at https://www.hgc.jp/~niiyan/massive.
Yanlei Diao - One of the best experts on this subject based on the ideXlab platform.
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aide an active learning based approach for Interactive Data exploration
IEEE Transactions on Knowledge and Data Engineering, 2016Co-Authors: Kyriaki Dimitriadou, Olga Papaemmanouil, Yanlei DiaoAbstract:In this paper, we argue that Database systems be augmented with an automated Data exploration service that methodically steers users through the Data in a meaningful way. Such an automated system is crucial for deriving insights from complex Datasets found in many big Data applications such as scientific and healthcare applications as well as for reducing the human effort of Data exploration. Towards this end, we present AIDE, an Automatic Interactive Data Exploration framework that assists users in discovering new interesting Data patterns and eliminate expensive ad-hoc exploratory queries. AIDE relies on a seamless integration of classification algorithms and Data management optimization techniques that collectively strive to accurately learn the user interests based on his relevance feedback on strategically collected samples. We present a number of exploration techniques as well as optimizations that minimize the number of samples presented to the user while offering Interactive performance. AIDE can deliver highly accurate query predictions for very common conjunctive queries with small user effort while, given a reasonable number of samples, it can predict with high accuracy complex disjunctive queries. It provides Interactive performance as it limits the user wait time per iteration of exploration to less than a few seconds.
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explore by example an automatic query steering framework for Interactive Data exploration
International Conference on Management of Data, 2014Co-Authors: Kyriaki Dimitriadou, Olga Papaemmanouil, Yanlei DiaoAbstract:Interactive Data Exploration (IDE) is a key ingredient of a diverse set of discovery-oriented applications, including ones from scientific computing and evidence-based medicine. In these applications, Data discovery is a highly ad hoc Interactive process where users execute numerous exploration queries using varying predicates aiming to balance the trade-off between collecting all relevant information and reducing the size of returned Data. Therefore, there is a strong need to support these human-in-the-loop applications by assisting their navigation in the Data to find interesting objects. In this paper, we introduce AIDE, an Automatic Interactive Data Exploration framework, that iteratively steers the user towards interesting Data areas and predicts a query that retrieves his objects of interest. Our approach leverages relevance feedback on Database samples to model user interests and strategically collects more samples to refine the model while minimizing the user effort. AIDE integrates machine learning and Data management techniques to provide effective Data exploration results (matching the user's interests with high accuracy) as well as high Interactive performance. It delivers highly accurate query predictions for very common conjunctive queries with very small user effort while, given a reasonable number of samples, it can predict with high accuracy complex conjunctive queries. Furthermore, it provides Interactive performance by limiting the user wait time per iteration to less than a few seconds in average. Our user study indicates that AIDE is a practical exploration framework as it significantly reduces the user effort and the total exploration time compared with the current state-of-the-art approach of manual exploration.
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Interactive Data exploration based on user relevance feedback
International Conference on Data Engineering, 2014Co-Authors: Kyriaki Dimitriadou, Olga Papaemmanouil, Yanlei DiaoAbstract:Interactive Data Exploration (IDE) applications typically involve users that aim to discover interesting objects by it-eratively executing numerous ad-hoc exploration queries. Therefore, IDE can easily become an extremely labor and resource intensive process. To support these applications, we introduce a framework that assists users by automatically navigating them through the Data set and allows them to identify relevant objects without formulating Data retrieval queries. Our approach relies on user relevance feedback on Data samples to model user interests and strategically collects more samples to refine the model while minimizing the user effort. The system leverages decision tree classifiers to generate an effective user model that balances the trade-off between identifying all relevant objects and reducing the size of final returned (relevant and irrelevant) objects. Our preliminary experimental results demonstrate that we can predict linear patterns of user interests (i.e., range queries) with high accuracy while achieving Interactive performance.
Aditya Parameswaran - One of the best experts on this subject based on the ideXlab platform.
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Interactive Data exploration with smart drill-down
2016 IEEE 32nd International Conference on Data Engineering (ICDE), 2016Co-Authors: Manas Joglekar, Hector Garcia-molina, Aditya ParameswaranAbstract:We present smart drill-down, an operator for Interactively exploring a relational table to discover and summarize “interesting” groups of tuples. Each group of tuples is described by a rule. For instance, the rule (a, b, *, 1000) tells us that there are a thousand tuples with value a in the first column and b in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can Interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are NP-HARD, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real Datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms.
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Interactive Data exploration with smart drill down
arXiv: Databases, 2014Co-Authors: Manas Joglekar, Hector Garciamolina, Aditya ParameswaranAbstract:We present {\em smart drill-down}, an operator for Interactively exploring a relational table to discover and summarize "interesting" groups of tuples. Each group of tuples is described by a {\em rule}. For instance, the rule $(a, b, \star, 1000)$ tells us that there are a thousand tuples with value $a$ in the first column and $b$ in the second column (and any value in the third column). Smart drill-down presents an analyst with a list of rules that together describe interesting aspects of the table. The analyst can tailor the definition of interesting, and can Interactively apply smart drill-down on an existing rule to explore that part of the table. We demonstrate that the underlying optimization problems are {\sc NP-Hard}, and describe an algorithm for finding the approximately optimal list of rules to display when the user uses a smart drill-down, and a dynamic sampling scheme for efficiently interacting with large tables. Finally, we perform experiments on real Datasets on our experimental prototype to demonstrate the usefulness of smart drill-down and study the performance of our algorithms.
Thouis R Jones - One of the best experts on this subject based on the ideXlab platform.
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cellprofiler analyst Data exploration and analysis software for complex image based screens
BMC Bioinformatics, 2008Co-Authors: Thouis R Jones, In Han Kang, Douglas B Wheeler, Robert A Lindquist, Adam Papallo, David M Sabatini, Polina Golland, Anne E CarpenterAbstract:Image-based screens can produce hundreds of measured features for each of hundreds of millions of individual cells in a single experiment. Here, we describe CellProfiler Analyst, open-source software for the Interactive exploration and analysis of multidimensional Data, particularly Data from high-throughput, image-based experiments. The system enables Interactive Data exploration for image-based screens and automated scoring of complex phenotypes that require combinations of multiple measured features per cell.
