progetti finalizzati per la ricerca di base-firb 2002 (Olon Ricerca Bioscience)
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Olon Ricerca Bioscience
progetti finalizzati per la ricerca di base-firb 2002
Progetti Finalizzati Per La Ricerca Di Base Firb 2002, supplied by Olon Ricerca Bioscience, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/firb/progetti+finalizzati+per+la+ricerca+di+base+firb+2002/pmc10815985-78-27-28
Average 90 stars, based on 1 article reviews
Progetti Finalizzati Per La Ricerca Di Base Firb 2002, supplied by Olon Ricerca Bioscience, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/firb/progetti+finalizzati+per+la+ricerca+di+base+firb+2002/pmc10815985-78-27-28
Average 90 stars, based on 1 article reviews
progetti finalizzati per la ricerca di base-firb 2002 - by Bioz Stars,
2026-10
90/100 stars
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High Performance Liquid Chromatography:Article Title: Does joint capsule and synovial fluid contain proteases capable of degrading aggrecan? Article Snippet: This work was supported by Article Title: Proangiogenic Cytokines as Hypoxia-Dependent Factors Stimulating Migration of Human Hepatic Stellate Cells Article Snippet: Supported by the Italian Ministero dell’Università e della Ricerca Fondo per Article Title: The approach of power priors for ability estimation in IRT models Article Snippet: Acknowledgments This research has been partially funded by the Italian Ministry of Education with the Article Title: Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals Article Snippet: This work was supported by Article Title: Mind the Gap! A Journey towards Computational Toxicology. Article Snippet: Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale.. However, the predictive potential of these models presents strengths and weaknesses.. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. Article Title: Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data. Article Snippet: 2015 Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.. Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency.. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Virus:Article Title: Does joint capsule and synovial fluid contain proteases capable of degrading aggrecan? Article Snippet: This work was supported by Article Title: Proangiogenic Cytokines as Hypoxia-Dependent Factors Stimulating Migration of Human Hepatic Stellate Cells Article Snippet: Supported by the Italian Ministero dell’Università e della Ricerca Fondo per Article Title: The approach of power priors for ability estimation in IRT models Article Snippet: Acknowledgments This research has been partially funded by the Italian Ministry of Education with the Article Title: Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals Article Snippet: This work was supported by Article Title: Mind the Gap! A Journey towards Computational Toxicology. Article Snippet: Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale.. However, the predictive potential of these models presents strengths and weaknesses.. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. Article Title: Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data. Article Snippet: 2015 Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.. Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency.. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Multiplex Assay:Article Title: Does joint capsule and synovial fluid contain proteases capable of degrading aggrecan? Article Snippet: This work was supported by Article Title: Proangiogenic Cytokines as Hypoxia-Dependent Factors Stimulating Migration of Human Hepatic Stellate Cells Article Snippet: Supported by the Italian Ministero dell’Università e della Ricerca Fondo per Article Title: The approach of power priors for ability estimation in IRT models Article Snippet: Acknowledgments This research has been partially funded by the Italian Ministry of Education with the Article Title: Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals Article Snippet: This work was supported by Article Title: Mind the Gap! A Journey towards Computational Toxicology. Article Snippet: Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale.. However, the predictive potential of these models presents strengths and weaknesses.. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. Article Title: Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data. Article Snippet: 2015 Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.. Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency.. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Ligation:Article Title: Does joint capsule and synovial fluid contain proteases capable of degrading aggrecan? Article Snippet: This work was supported by Article Title: Proangiogenic Cytokines as Hypoxia-Dependent Factors Stimulating Migration of Human Hepatic Stellate Cells Article Snippet: Supported by the Italian Ministero dell’Università e della Ricerca Fondo per Article Title: The approach of power priors for ability estimation in IRT models Article Snippet: Acknowledgments This research has been partially funded by the Italian Ministry of Education with the Article Title: Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals Article Snippet: This work was supported by Article Title: Mind the Gap! A Journey towards Computational Toxicology. Article Snippet: Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale.. However, the predictive potential of these models presents strengths and weaknesses.. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. Article Title: Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data. Article Snippet: 2015 Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.. Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency.. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Amplification:Article Title: Does joint capsule and synovial fluid contain proteases capable of degrading aggrecan? Article Snippet: This work was supported by Article Title: Proangiogenic Cytokines as Hypoxia-Dependent Factors Stimulating Migration of Human Hepatic Stellate Cells Article Snippet: Supported by the Italian Ministero dell’Università e della Ricerca Fondo per Article Title: The approach of power priors for ability estimation in IRT models Article Snippet: Acknowledgments This research has been partially funded by the Italian Ministry of Education with the Article Title: Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals Article Snippet: This work was supported by Article Title: Mind the Gap! A Journey towards Computational Toxicology. Article Snippet: Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale.. However, the predictive potential of these models presents strengths and weaknesses.. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. Article Title: Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data. Article Snippet: 2015 Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.. Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency.. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Whole Genome Amplification:Article Title: Does joint capsule and synovial fluid contain proteases capable of degrading aggrecan? Article Snippet: This work was supported by Article Title: Proangiogenic Cytokines as Hypoxia-Dependent Factors Stimulating Migration of Human Hepatic Stellate Cells Article Snippet: Supported by the Italian Ministero dell’Università e della Ricerca Fondo per Article Title: The approach of power priors for ability estimation in IRT models Article Snippet: Acknowledgments This research has been partially funded by the Italian Ministry of Education with the Article Title: Predictive Structure-Based Toxicology Approaches To Assess the Androgenic Potential of Chemicals Article Snippet: This work was supported by Article Title: Mind the Gap! A Journey towards Computational Toxicology. Article Snippet: Computational methods have advanced toxicology towards the development of target-specific models based on a clear cause-effect rationale.. However, the predictive potential of these models presents strengths and weaknesses.. On the good side, in silico models are valuable cheap alternatives to in vitro and in vivo experiments. Article Title: Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data. Article Snippet: 2015 Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals.. Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency.. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. |