The Experts below are selected from a list of 136005 Experts worldwide ranked by ideXlab platform
Xia Ning - One of the best experts on this subject based on the ideXlab platform.
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Pattern Discovery from High-Order Drug-Drug Interaction Relations
Journal of Healthcare Informatics Research, 2018Co-Authors: Wen-hao Chiang, Li Shen, Titus Schleyer, Xia NingAbstract:Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based Algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd’s) and directional DDI relations (DDI-d’s), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its Stochastic Algorithm SD 2 ID 2 S $\text {SD}^{2}\text {ID}^{2}\text {S}$ to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.
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pattern discovery from directional high order drug drug interaction relations
IEEE International Conference on Healthcare Informatics, 2017Co-Authors: Xia Ning, Titus Schleyer, Li ShenAbstract:Drug-Drug Interactions (DDIs) and associated Adverse Drug Reactions (ADRs) represent a significant public health problem in the United States. The research presented in this paper tackles the problems of representing, discovering, quantifying and visualizing patterns from high-order DDIs in a purely data-driven fashion. We formulate the problems based on a notion of directional DDI relations and correspondingly developed weighted hyper-graphlets for their representation. We also develop a convolutional scheme and its Stochastic Algorithm SD3ID2S to learn the directional DDI based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns from high-order DDIs.
Li Shen - One of the best experts on this subject based on the ideXlab platform.
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Pattern Discovery from High-Order Drug-Drug Interaction Relations
Journal of Healthcare Informatics Research, 2018Co-Authors: Wen-hao Chiang, Li Shen, Titus Schleyer, Xia NingAbstract:Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based Algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd’s) and directional DDI relations (DDI-d’s), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its Stochastic Algorithm SD 2 ID 2 S $\text {SD}^{2}\text {ID}^{2}\text {S}$ to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.
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pattern discovery from directional high order drug drug interaction relations
IEEE International Conference on Healthcare Informatics, 2017Co-Authors: Xia Ning, Titus Schleyer, Li ShenAbstract:Drug-Drug Interactions (DDIs) and associated Adverse Drug Reactions (ADRs) represent a significant public health problem in the United States. The research presented in this paper tackles the problems of representing, discovering, quantifying and visualizing patterns from high-order DDIs in a purely data-driven fashion. We formulate the problems based on a notion of directional DDI relations and correspondingly developed weighted hyper-graphlets for their representation. We also develop a convolutional scheme and its Stochastic Algorithm SD3ID2S to learn the directional DDI based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns from high-order DDIs.
Wen-hao Chiang - One of the best experts on this subject based on the ideXlab platform.
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Pattern Discovery from High-Order Drug-Drug Interaction Relations
Journal of Healthcare Informatics Research, 2018Co-Authors: Wen-hao Chiang, Li Shen, Titus Schleyer, Xia NingAbstract:Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based Algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd’s) and directional DDI relations (DDI-d’s), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its Stochastic Algorithm SD 2 ID 2 S $\text {SD}^{2}\text {ID}^{2}\text {S}$ to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.
Titus Schleyer - One of the best experts on this subject based on the ideXlab platform.
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Pattern Discovery from High-Order Drug-Drug Interaction Relations
Journal of Healthcare Informatics Research, 2018Co-Authors: Wen-hao Chiang, Li Shen, Titus Schleyer, Xia NingAbstract:Drug-drug interactions (DDIs) and associated adverse drug reactions (ADRs) represent a significant public health problem in the USA. The research presented in this manuscript tackles the problems of representing, quantifying, discovering, and visualizing patterns from high-order DDIs in a purely data-driven fashion within a unified graph-based framework and via unified convolution-based Algorithms. We formulate the problem based on the notions of nondirectional DDI relations (DDI-nd’s) and directional DDI relations (DDI-d’s), and correspondingly developed weighted complete graphs and hyper-graphlets for their representation, respectively. We also develop a convolutional scheme and its Stochastic Algorithm SD 2 ID 2 S $\text {SD}^{2}\text {ID}^{2}\text {S}$ to discover DDI-based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns of high-order DDIs.
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pattern discovery from directional high order drug drug interaction relations
IEEE International Conference on Healthcare Informatics, 2017Co-Authors: Xia Ning, Titus Schleyer, Li ShenAbstract:Drug-Drug Interactions (DDIs) and associated Adverse Drug Reactions (ADRs) represent a significant public health problem in the United States. The research presented in this paper tackles the problems of representing, discovering, quantifying and visualizing patterns from high-order DDIs in a purely data-driven fashion. We formulate the problems based on a notion of directional DDI relations and correspondingly developed weighted hyper-graphlets for their representation. We also develop a convolutional scheme and its Stochastic Algorithm SD3ID2S to learn the directional DDI based drug-drug similarities. Our experimental results demonstrate that such approaches can well capture the patterns from high-order DDIs.
Stephane Menozzi - One of the best experts on this subject based on the ideXlab platform.
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a forward backward Stochastic Algorithm for quasi linear pdes
arXiv: Probability, 2006Co-Authors: Francois Delarue, Stephane MenozziAbstract:We propose a time-space discretization scheme for quasi-linear parabolic PDEs. The Algorithm relies on the theory of fully coupled forward--backward SDEs, which provides an efficient probabilistic representation of this type of equation. The derivated Algorithm holds for strong solutions defined on any interval of arbitrary length. As a bypass product, we obtain a discretization procedure for the underlying FBSDE. In particular, our work provides an alternative to the method described in [Douglas, Ma and Protter (1996) Ann. Appl. Probab. 6 940--968] and weakens the regularity assumptions required in this reference.
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a forward backward Stochastic Algorithm for quasi linear pdes
Annals of Applied Probability, 2006Co-Authors: Francois Delarue, Stephane MenozziAbstract:We propose a time-space discretization scheme for quasi-linear PDEs. The Algorithm relies on the theory of fully coupled Forward-Backward SDEs, which provides an efficient probabilistic representation of this type of equations. The derivated Algorithm holds for strong solutions defined on any interval of arbitrary length. As a bypass product, we obtain a discretization procedure for the underlying FBSDE.
