The Experts below are selected from a list of 126072 Experts worldwide ranked by ideXlab platform
Mark M Davis - One of the best experts on this subject based on the ideXlab platform.
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SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses.
Journal of Immunology, 2019Co-Authors: Adriana Tomic, Ivan Tomic, Yael Rosenberg-hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M DavisAbstract:: Machine Learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. In this study, we developed Sequential Iterative Modeling "OverNight" (SIMON), an automated Machine Learning System that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust Ab response to influenza Ags. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.
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SIMON, an automated Machine Learning System reveals immune signatures of influenza vaccine responses
bioRxiv, 2019Co-Authors: Adriana Tomic, Ivan Tomic, Yael Rosenberg-hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M DavisAbstract:Machine Learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. Here, we developed Sequential Iterative Modelling "OverNight" or SIMON, an automated Machine Learning System that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust antibody response to influenza antigens. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.
Guodong Zhou - One of the best experts on this subject based on the ideXlab platform.
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multilingual dependency Learning a huge feature engineering method to semantic dependency parsing
Conference on Computational Natural Language Learning, 2009Co-Authors: Hai Zhao, Chunyu Kity, Wenliang Chen, Guodong ZhouAbstract:This paper describes our System about multilingual semantic dependency parsing (SR-Lonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into a word-pair classification problem and implemented as a single-stage Machine Learning System. For each input corpus, a large scale feature engineering is conducted to select the best fit feature template set incorporated with a proper argument pruning strategy. The System achieved the top average score in the closed challenge: 80.47% semantic labeled F1 for the average score.
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CoNLL Shared Task - Multilingual Dependency Learning: A Huge Feature Engineering Method to Semantic Dependency Parsing
Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task - CoNLL '09, 2009Co-Authors: Hai Zhao, Chunyu Kity, Wenliang Chen, Guodong ZhouAbstract:This paper describes our System about multilingual semantic dependency parsing (SR-Lonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into a word-pair classification problem and implemented as a single-stage Machine Learning System. For each input corpus, a large scale feature engineering is conducted to select the best fit feature template set incorporated with a proper argument pruning strategy. The System achieved the top average score in the closed challenge: 80.47% semantic labeled F1 for the average score.
Adriana Tomic - One of the best experts on this subject based on the ideXlab platform.
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SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses.
Journal of Immunology, 2019Co-Authors: Adriana Tomic, Ivan Tomic, Yael Rosenberg-hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M DavisAbstract:: Machine Learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. In this study, we developed Sequential Iterative Modeling "OverNight" (SIMON), an automated Machine Learning System that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust Ab response to influenza Ags. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.
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SIMON, an automated Machine Learning System reveals immune signatures of influenza vaccine responses
bioRxiv, 2019Co-Authors: Adriana Tomic, Ivan Tomic, Yael Rosenberg-hasson, Cornelia L. Dekker, Holden T. Maecker, Mark M DavisAbstract:Machine Learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. Here, we developed Sequential Iterative Modelling "OverNight" or SIMON, an automated Machine Learning System that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust antibody response to influenza antigens. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.
Hai Zhao - One of the best experts on this subject based on the ideXlab platform.
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multilingual dependency Learning a huge feature engineering method to semantic dependency parsing
Conference on Computational Natural Language Learning, 2009Co-Authors: Hai Zhao, Chunyu Kity, Wenliang Chen, Guodong ZhouAbstract:This paper describes our System about multilingual semantic dependency parsing (SR-Lonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into a word-pair classification problem and implemented as a single-stage Machine Learning System. For each input corpus, a large scale feature engineering is conducted to select the best fit feature template set incorporated with a proper argument pruning strategy. The System achieved the top average score in the closed challenge: 80.47% semantic labeled F1 for the average score.
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CoNLL Shared Task - Multilingual Dependency Learning: A Huge Feature Engineering Method to Semantic Dependency Parsing
Proceedings of the Thirteenth Conference on Computational Natural Language Learning Shared Task - CoNLL '09, 2009Co-Authors: Hai Zhao, Chunyu Kity, Wenliang Chen, Guodong ZhouAbstract:This paper describes our System about multilingual semantic dependency parsing (SR-Lonly) for our participation in the shared task of CoNLL-2009. We illustrate that semantic dependency parsing can be transformed into a word-pair classification problem and implemented as a single-stage Machine Learning System. For each input corpus, a large scale feature engineering is conducted to select the best fit feature template set incorporated with a proper argument pruning strategy. The System achieved the top average score in the closed challenge: 80.47% semantic labeled F1 for the average score.
Ngoctri Ngo - One of the best experts on this subject based on the ideXlab platform.
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time series analytics using sliding window metaheuristic optimization based Machine Learning System for identifying building energy consumption patterns
Applied Energy, 2016Co-Authors: Juisheng Chou, Ngoctri NgoAbstract:Smart grids are a promising solution to the rapidly growing power demand because they can considerably increase building energy efficiency. This study developed a novel time-series sliding window metaheuristic optimization-based Machine Learning System for predicting real-time building energy consumption data collected by a smart grid. The proposed System integrates a seasonal autoregressive integrated moving average (SARIMA) model and metaheuristic firefly algorithm-based least squares support vector regression (MetaFA-LSSVR) model. Specifically, the proposed System fits the SARIMA model to linear data components in the first stage, and the MetaFA-LSSVR model captures nonlinear data components in the second stage. Real-time data retrieved from an experimental smart grid installed in a building were used to evaluate the efficacy and effectiveness of the proposed System. A k-week sliding window approach is proposed for employing historical data as input for the novel time-series forecasting System. The prediction System yielded high and reliable accuracy rates in 1-day-ahead predictions of building energy consumption, with a total error rate of 1.181% and mean absolute error of 0.026kWh. Notably, the System demonstrates an improved accuracy rate in the range of 36.8–113.2% relative to those of the linear forecasting model (i.e., SARIMA) and nonlinear forecasting models (i.e., LSSVR and MetaFA-LSSVR). Therefore, end users can further apply the forecasted information to enhance efficiency of energy usage in their buildings, especially during peak times. In particular, the System can potentially be scaled up for using big data framework to predict building energy consumption.