The Experts below are selected from a list of 167025 Experts worldwide ranked by ideXlab platform
Yang Bingru - One of the best experts on this subject based on the ideXlab platform.
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Protein Secondary Structure Prediction Algorithm Based on Mixed-SVM Method
Computer Science, 2011Co-Authors: Yang BingruAbstract:Protein secondary structure Prediction is one of the most important problems in bioinformatics.The protein secondary structure Prediction accuracy plays an important role in the field of protein structure research.In this paper,using a Knowledge Discovery Theory based on the Inner Cognitive Mechanism(KDTICM),an efficient protein secondary structure Prediction Algorithm based on mixed-SVM(support vector machine) approach was proposed.The Algorithm makes full use of the evolutionary information contained in the physicochemical properties of each amino acid and a position-specific scoring matrix generated by a PSI-SEARCH multiple sequence alignment,secondary structure can be predicted at significantly increased accuracy.At last,the experiments were used to show the superior accuracy and generality of the new Algorithm than other classical Algorithm.
Yaniv Kerem - One of the best experts on this subject based on the ideXlab platform.
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multicenter validation of a sepsis Prediction Algorithm using only vital sign data in the emergency department general ward and icu
bioRxiv, 2018Co-Authors: Qingqing Mao, Melissa Jay, Jana Hoffman, Jacob Calvert, Christopher Barton, David Shimabukuro, Lisa Shieh, Uli K Chettipally, Grant S Fletcher, Yaniv KeremAbstract:Objectives: We validate a machine learning-based sepsis Prediction Algorithm (InSight) for detection and Prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings. Design: A machine learning Algorithm with gradient tree boosting. Features for Prediction were created from combinations of only six vital sign measurements and their changes over time. Setting: A mixed-ward retrospective data set from the University of California, San Francisco (UCSF) Medical Center (San Francisco, CA) as the primary source, an intensive care unit data set from the Beth Israel Deaconess Medical Center (Boston, MA) as a transfer learning source, and four additional institutions9 datasets to evaluate generalizability. Participants: 684,443 total encounters, with 90,353 encounters from June 2011 to March 2016 at UCSF. Interventions: none Primary and secondary outcome measures: Area under the receiver operating characteristic curve (AUROC) for detection and Prediction of sepsis, severe sepsis, and septic shock. Results: For detection of sepsis and severe sepsis, InSight achieves an area under the receiver operating characteristic (AUROC) curve of 0.92 (95% CI 0.90 - 0.93) and 0.87 (95% CI 0.86 - 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 - 0.98), and severe sepsis with an AUROC of 0.85 (95% CI 0.79 - 0.91). Conclusions: InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions.
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multicentre validation of a sepsis Prediction Algorithm using only vital sign data in the emergency department general ward and icu
BMJ Open, 2018Co-Authors: Qingqing Mao, Melissa Jay, Jana Hoffman, Jacob Calvert, Christopher Barton, David Shimabukuro, Lisa Shieh, Uli K Chettipally, Grant S Fletcher, Yaniv KeremAbstract:Objectives We validate a machine learning-based sepsis-Prediction Algorithm (InSight) for the detection and Prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings. Design A machine-learning Algorithm with gradient tree boosting. Features for Prediction were created from combinations of six vital sign measurements and their changes over time. Setting A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions’ datasets to evaluate generalisability. Participants 684 443 total encounters, with 90 353 encounters from June 2011 to March 2016 at UCSF. Interventions None. Primary and secondary outcome measures Area under the receiver operating characteristic (AUROC) curve for detection and Prediction of sepsis, severe sepsis and septic shock. Results For detection of sepsis and severe sepsis, InSight achieves an AUROC curve of 0.92 (95% CI 0.90 to 0.93) and 0.87 (95% CI 0.86 to 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 to 0.98) and severe sepsis with an AUROC of 0.85 (95% CI 0.79 to 0.91). Conclusions InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.
Qiang Zhu - One of the best experts on this subject based on the ideXlab platform.
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new developments in evolutionary structure Prediction Algorithm uspex
Computer Physics Communications, 2013Co-Authors: Andriy O Lyakhov, Artem R. Oganov, Harold T Stokes, Qiang ZhuAbstract:a b s t r a c t We present new developments of the evolutionary Algorithm USPEX for crystal structure Prediction and its adaptation to cluster structure Prediction. We show how to generate randomly symmetric structures, and how to introduce 'smart' variation operators, learning about preferable local environments. These and other developments substantially improve the efficiency of the Algorithm and allow reliable Prediction of structures with up to ∼200 atoms in the unit cell. We show that an advanced version of the Particle Swarm Optimization (PSO) can be created on the basis of our method, but PSO is strongly outperformed by USPEX. We also show how ideas from metadynamics can be used in the context of evolutionary structure Prediction for escaping from local minima. Our cluster structure Prediction Algorithm, using the ideas initially developed for crystals, also shows excellent performance and outperforms other state-of-the-art Algorithms.
Qingqing Mao - One of the best experts on this subject based on the ideXlab platform.
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multicenter validation of a sepsis Prediction Algorithm using only vital sign data in the emergency department general ward and icu
bioRxiv, 2018Co-Authors: Qingqing Mao, Melissa Jay, Jana Hoffman, Jacob Calvert, Christopher Barton, David Shimabukuro, Lisa Shieh, Uli K Chettipally, Grant S Fletcher, Yaniv KeremAbstract:Objectives: We validate a machine learning-based sepsis Prediction Algorithm (InSight) for detection and Prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customization to site-specific data using transfer learning, and generalizability to new settings. Design: A machine learning Algorithm with gradient tree boosting. Features for Prediction were created from combinations of only six vital sign measurements and their changes over time. Setting: A mixed-ward retrospective data set from the University of California, San Francisco (UCSF) Medical Center (San Francisco, CA) as the primary source, an intensive care unit data set from the Beth Israel Deaconess Medical Center (Boston, MA) as a transfer learning source, and four additional institutions9 datasets to evaluate generalizability. Participants: 684,443 total encounters, with 90,353 encounters from June 2011 to March 2016 at UCSF. Interventions: none Primary and secondary outcome measures: Area under the receiver operating characteristic curve (AUROC) for detection and Prediction of sepsis, severe sepsis, and septic shock. Results: For detection of sepsis and severe sepsis, InSight achieves an area under the receiver operating characteristic (AUROC) curve of 0.92 (95% CI 0.90 - 0.93) and 0.87 (95% CI 0.86 - 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 - 0.98), and severe sepsis with an AUROC of 0.85 (95% CI 0.79 - 0.91). Conclusions: InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis, and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customized to novel hospital data using a small fraction of site data, and retained strong discrimination across all institutions.
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multicentre validation of a sepsis Prediction Algorithm using only vital sign data in the emergency department general ward and icu
BMJ Open, 2018Co-Authors: Qingqing Mao, Melissa Jay, Jana Hoffman, Jacob Calvert, Christopher Barton, David Shimabukuro, Lisa Shieh, Uli K Chettipally, Grant S Fletcher, Yaniv KeremAbstract:Objectives We validate a machine learning-based sepsis-Prediction Algorithm (InSight) for the detection and Prediction of three sepsis-related gold standards, using only six vital signs. We evaluate robustness to missing data, customisation to site-specific data using transfer learning and generalisability to new settings. Design A machine-learning Algorithm with gradient tree boosting. Features for Prediction were created from combinations of six vital sign measurements and their changes over time. Setting A mixed-ward retrospective dataset from the University of California, San Francisco (UCSF) Medical Center (San Francisco, California, USA) as the primary source, an intensive care unit dataset from the Beth Israel Deaconess Medical Center (Boston, Massachusetts, USA) as a transfer-learning source and four additional institutions’ datasets to evaluate generalisability. Participants 684 443 total encounters, with 90 353 encounters from June 2011 to March 2016 at UCSF. Interventions None. Primary and secondary outcome measures Area under the receiver operating characteristic (AUROC) curve for detection and Prediction of sepsis, severe sepsis and septic shock. Results For detection of sepsis and severe sepsis, InSight achieves an AUROC curve of 0.92 (95% CI 0.90 to 0.93) and 0.87 (95% CI 0.86 to 0.88), respectively. Four hours before onset, InSight predicts septic shock with an AUROC of 0.96 (95% CI 0.94 to 0.98) and severe sepsis with an AUROC of 0.85 (95% CI 0.79 to 0.91). Conclusions InSight outperforms existing sepsis scoring systems in identifying and predicting sepsis, severe sepsis and septic shock. This is the first sepsis screening system to exceed an AUROC of 0.90 using only vital sign inputs. InSight is robust to missing data, can be customised to novel hospital data using a small fraction of site data and retains strong discrimination across all institutions.
Andriy O Lyakhov - One of the best experts on this subject based on the ideXlab platform.
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new developments in evolutionary structure Prediction Algorithm uspex
Computer Physics Communications, 2013Co-Authors: Andriy O Lyakhov, Artem R. Oganov, Harold T Stokes, Qiang ZhuAbstract:a b s t r a c t We present new developments of the evolutionary Algorithm USPEX for crystal structure Prediction and its adaptation to cluster structure Prediction. We show how to generate randomly symmetric structures, and how to introduce 'smart' variation operators, learning about preferable local environments. These and other developments substantially improve the efficiency of the Algorithm and allow reliable Prediction of structures with up to ∼200 atoms in the unit cell. We show that an advanced version of the Particle Swarm Optimization (PSO) can be created on the basis of our method, but PSO is strongly outperformed by USPEX. We also show how ideas from metadynamics can be used in the context of evolutionary structure Prediction for escaping from local minima. Our cluster structure Prediction Algorithm, using the ideas initially developed for crystals, also shows excellent performance and outperforms other state-of-the-art Algorithms.
