The Experts below are selected from a list of 173892 Experts worldwide ranked by ideXlab platform
Lise Getoor - One of the best experts on this subject based on the ideXlab platform.
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A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Angelika Kimmig, Alex Memory, Ren´ee J. Miller, Lise GetoorAbstract:We propose a Probabilistic Approach to the problem of schema mapping. Our Approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and Probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art Probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise.
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A Collective, Probabilistic Approach to Schema Mapping
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Angelika Kimmig, Alex Memory, Ren´ee J. Miller, Lise GetoorAbstract:We propose a Probabilistic Approach to the problem of schema mapping. Our Approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and Probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using stateof- the-art Probabilistic reasoning techniques, which allows for inconsistencies and incompleteness. Using hundreds of realistic integration scenarios, we demonstrate that the accuracy of CMD is more than 33% above that of metadata-only Approaches already for small data examples, and that CMD routinely finds perfect mappings even if a quarter of the data is inconsistent.
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a Probabilistic Approach for collective similarity based drug drug interaction prediction
Bioinformatics, 2016Co-Authors: Dhanya Sridhar, Shobeir Fakhraei, Lise GetoorAbstract:MOTIVATION As concurrent use of multiple medications becomes ubiquitous among patients, it is crucial to characterize both adverse and synergistic interactions between drugs. Statistical methods for prediction of putative drug-drug interactions (DDIs) can guide in vitro testing and cut down significant cost and effort. With the abundance of experimental data characterizing drugs and their associated targets, such methods must effectively fuse multiple sources of information and perform inference over the network of drugs. RESULTS We propose a Probabilistic Approach for jointly inferring unknown DDIs from a network of multiple drug-based similarities and known interactions. We use the highly scalable and easily extensible Probabilistic programming framework Probabilistic Soft Logic We compare against two methods including a state-of-the-art DDI prediction system across three experiments and show best performing improvements of more than 50% in AUPR over both baselines. We find five novel interactions validated by external sources among the top-ranked predictions of our model. AVAILABILITY AND IMPLEMENTATION Final versions of all datasets and implementations will be made publicly available. CONTACT dsridhar@ucsc.edu.
Haileyesus B Endeshaw - One of the best experts on this subject based on the ideXlab platform.
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parametric Probabilistic Approach for cumulative fatigue damage using double linear damage rule considering limited data
International Journal of Fatigue, 2019Co-Authors: Joao Paulo Dias, Stephen Ekwaroosire, Americo Cunha, Shweta Dabetwar, Abraham Nispel, Fisseha M Alemayehu, Haileyesus B EndeshawAbstract:Abstract This work proposes a parametric Probabilistic Approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A Probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed Approach is validated using fatigue life experiments available in the literature.
Angelika Kimmig - One of the best experts on this subject based on the ideXlab platform.
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A Collective, Probabilistic Approach to Schema Mapping Using Diverse Noisy Evidence
IEEE Transactions on Knowledge and Data Engineering, 2019Co-Authors: Angelika Kimmig, Alex Memory, Ren´ee J. Miller, Lise GetoorAbstract:We propose a Probabilistic Approach to the problem of schema mapping. Our Approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and Probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art Probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise.
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A Collective, Probabilistic Approach to Schema Mapping
2017 IEEE 33rd International Conference on Data Engineering (ICDE), 2017Co-Authors: Angelika Kimmig, Alex Memory, Ren´ee J. Miller, Lise GetoorAbstract:We propose a Probabilistic Approach to the problem of schema mapping. Our Approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and Probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using stateof- the-art Probabilistic reasoning techniques, which allows for inconsistencies and incompleteness. Using hundreds of realistic integration scenarios, we demonstrate that the accuracy of CMD is more than 33% above that of metadata-only Approaches already for small data examples, and that CMD routinely finds perfect mappings even if a quarter of the data is inconsistent.
Michal Linial - One of the best experts on this subject based on the ideXlab platform.
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a cell based Probabilistic Approach unveils the concerted action of mirnas
PLOS Computational Biology, 2019Co-Authors: Shelly Mahlabaviv, Nathan Linial, Michal LinialAbstract:Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate the underlying principles of miRNA regulation in living cells. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under a paradigm of a transcriptional arrest. Based on an exhaustive matrix of mRNA-miRNA binding probabilities, and parameters extracted from our experiments, we developed a computational framework that captures the cooperative action of miRNAs in living cells. The framework, called COMICS, simulates the stochastic binding events between miRNAs and mRNAs in cells. The input of COMICS is cell-specific profiles of mRNAs and miRNAs, and the outcome is the retention level of each mRNA at the end of 100,000 iterations. The results of COMICS from thousands of miRNA manipulations reveal gene sets that exhibit coordinated behavior with respect to all miRNAs (total of 248 families). We identified a small set of genes that are highly responsive to changes in the expression of almost any of the miRNAs. In contrast, about 20% of the tested genes remain insensitive to a broad range of miRNA manipulations. The set of insensitive genes is strongly enriched with genes that belong to the translation machinery. These trends are shared by different cell types. We conclude that the stochastic nature of miRNAs reveals unexpected robustness of gene expression in living cells. By applying a systematic Probabilistic Approach some key design principles of cell states are revealed, emphasizing in particular, the immunity of the translational machinery vis-a-vis miRNA manipulations across cell types. We propose COMICS as a valuable platform for assessing the outcome of miRNA regulation of cells in health and disease.
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a cell based Probabilistic Approach unveils the concerted action of mirnas
bioRxiv, 2018Co-Authors: Shelly Mahlabaviv, Nathan Linial, Michal LinialAbstract:Summary Mature microRNAs (miRNAs) regulate most human genes through direct base-pairing with mRNAs. We investigate some underlying principles of such regulation. To this end, we overexpressed miRNAs in different cell types and measured the mRNA decay rate under transcriptional arrest. Parameters extracted from these experiments were incorporated into a computational stochastic framework which was developed to simulate the cooperative action of miRNAs in living cells. We identified gene sets that exhibit coordinated behavior with respect to all major miRNAs, over a broad range of overexpression levels. While a small set of genes is highly sensitive to miRNA manipulations, about 180 genes are insensitive to miRNA manipulations as measured by their degree of mRNA retention. The insensitive genes are associated with the translation machinery. We conclude that the stochastic nature of miRNAs reveals an unexpected robustness of gene expression in living cells. Moreover, the use of a systematic Probabilistic Approach exposes design principles of cells’ states and in particular, the translational machinery. Highlights A Probabilistic-based simulator assesses the cellular response to thousands of miRNA overexpression manipulations The translational machinery displays an exceptional resistance to manipulations of miRNAs. The insensitivity of the translation machinery to miRNA manipulations is shared by different cell types The composition of the most abundant miRNAs dominates cell identity
Joao Paulo Dias - One of the best experts on this subject based on the ideXlab platform.
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parametric Probabilistic Approach for cumulative fatigue damage using double linear damage rule considering limited data
International Journal of Fatigue, 2019Co-Authors: Joao Paulo Dias, Stephen Ekwaroosire, Americo Cunha, Shweta Dabetwar, Abraham Nispel, Fisseha M Alemayehu, Haileyesus B EndeshawAbstract:Abstract This work proposes a parametric Probabilistic Approach to model damage accumulation using the double linear damage rule (DLDR) considering the existence of limited experimental fatigue data. A Probabilistic version of DLDR is developed in which the joint distribution of the knee-point coordinates is obtained as a function of the joint distribution of the DLDR model input parameters. Considering information extracted from experiments containing a limited number of data points, an uncertainty quantification framework based on the Maximum Entropy Principle and Monte Carlo simulations is proposed to determine the distribution of fatigue life. The proposed Approach is validated using fatigue life experiments available in the literature.
