The Experts below are selected from a list of 15573 Experts worldwide ranked by ideXlab platform
Jonathan A Javitch - One of the best experts on this subject based on the ideXlab platform.
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Detecting G protein-coupled receptor complexes in postmortem human brain with proximity ligation assay and a Bayesian Classifier
Biotechniques, 2020Co-Authors: Ying Zhu, Pierre Trifilieff, József Mészáros, Roman Walle, Rongxi Fan, Ziyi Sun, Andrew Dwork, Jonathan A JavitchAbstract:Despite the controversy regarding the existence and physiological relevance of class A G protein-coupled receptor dimerization, there is substantial evidence for functional interactions between the dopamine D2 receptor (D2R) and the adenosine A2A receptor (A2AR). A2AR-D2R complexes have been detected in rodent brains by proximity ligation assay; however, their existence in the human brain has not been demonstrated. In this study, we used Brightfield proximity ligation assay, combined with a systematic sampling and a parameter-free naive Bayesian Classifier, and demonstrated proximity between the D2R and the A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. These methods are applicable to the relative quantification of proximity of two proteins, as well as the expression levels of individual proteins. METHOD SUMMARY : Brightfield proximity ligation assay was used to assess the expression of G protein-coupled receptors and their proximity in postmortem adult human brains. A novel automated machine learning method (Bayesian optimized PLA signal sorting) was developed to automatically quantify Brightfield proximity ligation assay data.
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detecting gpcr complexes in postmortem human brain with proximity ligation assay and a Bayesian Classifier
bioRxiv, 2019Co-Authors: Ying Zhu, Pierre Trifilieff, József Mészáros, Roman Walle, Rongxi Fan, Ziyi Sun, Andrew Dwork, Jonathan A JavitchAbstract:Abstract Despite the general controversy regarding the existence and physiological relevance of Class A GPCR dimerization, there is substantial evidence for functional interactions between dopamine D2 receptor (D2R) and adenosine A2A receptor (A2AR). A2AR-D2R complexes have been detected in rodent brains by proximity ligation assay (PLA), but their existence in the human brain is yet to be demonstrated. In this study, we used brightfield PLA, combined with a systematic sampling and a parameter-free naive Bayesian Classifier, and demonstrated proximity between D2R and A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. These methods are applicable to the quantitative analysis of proximity of two proteins and the expression of individual proteins. Method Summary Brightfield proximity ligation assay (PLA) was used to assess the expression of G protein-coupled receptors and their proximity in postmortem adult human brains. A novel automated machine learning method (Bayesian Optimized PLA Signal Sorting) was developed to automatically quantify brightfield PLA data.
Anup Basu - One of the best experts on this subject based on the ideXlab platform.
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fully automatic brain tumor segmentation using a normalized gaussian Bayesian Classifier and 3d fluid vector flow
arXiv: Image and Video Processing, 2019Co-Authors: T Wang, I Cheng, Anup BasuAbstract:Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain tissues. Gaussian Bayesian Classifier is exploited to acquire a Gaussian Bayesian Brain Map (GBBM) from the test brain MR images. GBBM is further processed to initialize the 3D FVF algorithm, which segments the brain tumor. This algorithm has two major contributions. First, we present a NGMM to model healthy brains. Second, we extend our 2D FVF algorithm to 3D space and use it for brain tumor segmentation. The proposed method is validated on a publicly available dataset.
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Fully automatic brain tumor segmentation using a normalized Gaussian Bayesian Classifier and 3D fluid vector flow
Proceedings - International Conference on Image Processing ICIP, 2010Co-Authors: T Wang, I Cheng, Anup BasuAbstract:Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an important task to measure tumor responses to treatments. However, automatic segmentation is very challenging. This paper presents an automatic brain tumor segmentation method based on a Normalized Gaussian Bayesian classification and a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain tissues. Gaussian Bayesian Classifier is exploited to acquire a Gaussian Bayesian Brain Map (GBBM) from the test brain MRIs. GBBM is further processed to initialize the 3D FVF algorithm, which segments the brain tumor. This algorithm has two major contributions. First, we present a NGMM to model healthy brains. Second, we extend our 2D FVF algorithm to 3D space and use it for brain tumor segmentation. The proposed method is validated on a publicly available dataset.
Ying Zhu - One of the best experts on this subject based on the ideXlab platform.
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Detecting G protein-coupled receptor complexes in postmortem human brain with proximity ligation assay and a Bayesian Classifier
Biotechniques, 2020Co-Authors: Ying Zhu, Pierre Trifilieff, József Mészáros, Roman Walle, Rongxi Fan, Ziyi Sun, Andrew Dwork, Jonathan A JavitchAbstract:Despite the controversy regarding the existence and physiological relevance of class A G protein-coupled receptor dimerization, there is substantial evidence for functional interactions between the dopamine D2 receptor (D2R) and the adenosine A2A receptor (A2AR). A2AR-D2R complexes have been detected in rodent brains by proximity ligation assay; however, their existence in the human brain has not been demonstrated. In this study, we used Brightfield proximity ligation assay, combined with a systematic sampling and a parameter-free naive Bayesian Classifier, and demonstrated proximity between the D2R and the A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. These methods are applicable to the relative quantification of proximity of two proteins, as well as the expression levels of individual proteins. METHOD SUMMARY : Brightfield proximity ligation assay was used to assess the expression of G protein-coupled receptors and their proximity in postmortem adult human brains. A novel automated machine learning method (Bayesian optimized PLA signal sorting) was developed to automatically quantify Brightfield proximity ligation assay data.
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detecting gpcr complexes in postmortem human brain with proximity ligation assay and a Bayesian Classifier
bioRxiv, 2019Co-Authors: Ying Zhu, Pierre Trifilieff, József Mészáros, Roman Walle, Rongxi Fan, Ziyi Sun, Andrew Dwork, Jonathan A JavitchAbstract:Abstract Despite the general controversy regarding the existence and physiological relevance of Class A GPCR dimerization, there is substantial evidence for functional interactions between dopamine D2 receptor (D2R) and adenosine A2A receptor (A2AR). A2AR-D2R complexes have been detected in rodent brains by proximity ligation assay (PLA), but their existence in the human brain is yet to be demonstrated. In this study, we used brightfield PLA, combined with a systematic sampling and a parameter-free naive Bayesian Classifier, and demonstrated proximity between D2R and A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. These methods are applicable to the quantitative analysis of proximity of two proteins and the expression of individual proteins. Method Summary Brightfield proximity ligation assay (PLA) was used to assess the expression of G protein-coupled receptors and their proximity in postmortem adult human brains. A novel automated machine learning method (Bayesian Optimized PLA Signal Sorting) was developed to automatically quantify brightfield PLA data.
Aida Krichene - One of the best experts on this subject based on the ideXlab platform.
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using a naive Bayesian Classifier methodology for loan risk assessment evidence from a tunisian commercial bank
Social Science Research Network, 2017Co-Authors: Aida KricheneAbstract:Purpose – Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank. Design/methodology/approach – The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian Classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators. Findings – The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent. Originality/value – The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers.
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using a naive Bayesian Classifier methodology for loan risk assessment evidence from a tunisian commercial bank
Journal of Economics Finance and Administrative Science, 2017Co-Authors: Aida KricheneAbstract:Purpose – Loan default risk or credit risk evaluation is important to financial institutions which provide loans to businesses and individuals. Loans carry the risk of being defaulted. To understand the risk levels of credit users (corporations and individuals), credit providers (bankers) normally collect vast amounts of information on borrowers. Statistical predictive analytic techniques can be used to analyse or to determine the risk levels involved in loans. This paper aims to address the question of default prediction of short-term loans for a Tunisian commercial bank. Design/methodology/approach – The authors have used a database of 924 files of credits granted to industrial Tunisian companies by a commercial bank in the years 2003, 2004, 2005 and 2006. The naive Bayesian Classifier algorithm was used, and the results show that the good classification rate is of the order of 63.85 per cent. The default probability is explained by the variables measuring working capital, leverage, solvency, profitability and cash flow indicators. Findings – The results of the validation test show that the good classification rate is of the order of 58.66 per cent; nevertheless, the error types I and II remain relatively high at 42.42 and 40.47 per cent, respectively. A receiver operating characteristic curve is plotted to evaluate the performance of the model. The result shows that the area under the curve criterion is of the order of 69 per cent. Originality/value – The paper highlights the fact that the Tunisian central bank obliged all commercial banks to conduct a survey study to collect qualitative data for better credit notation of the borrowers. Proposito – El riesgo de incumplimiento de prestamos o la evaluacion del riesgo de credito es importante para las instituciones financieras que otorgan prestamos a empresas e individuos. Existe el riesgo de que el pago de prestamos no se cumpla. Para entender los niveles de riesgo de los usuarios de credito (corporaciones e individuos), los proveedores de credito (banqueros) normalmente recogen gran cantidad de informacion sobre los prestatarios. Las tecnicas analiticas predictivas estadisticas pueden utilizarse para analizar o determinar los niveles de riesgo involucrados en los prestamos. En este articulo abordamos la cuestion de la prediccion por defecto de los prestamos a corto plazo para un banco comercial tunecino. Diseno/metodologia/enfoque – Utilizamos una base de datos de 924 archivos de creditos concedidos a empresas industriales tunecinas por un banco comercial en 2003, 2004, 2005 y 2006. El algoritmo Bayesiano de clasificadores se llevo a cabo y los resultados muestran que la tasa de clasificacion buena es del orden del 63.85%. La probabilidad de incumplimiento se explica por las variables que miden el capital de trabajo, el apalancamiento, la solvencia, la rentabilidad y los indicadores de flujo de efectivo. Hallazgos – Los resultados de la prueba de validacion muestran que la buena tasa de clasificacion es del orden de 58.66% ; sin embargo, los errores tipo I y II permanecen relativamente altos, siendo de 42.42% y 40.47%, respectivamente. Se traza una curva ROC para evaluar el rendimiento del modelo. El resultado muestra que el criterio de area bajo curva (AUC, por sus siglas en ingles) es del orden del 69%. Originalidad/valor – El documento destaca el hecho de que el Banco Central tunecino obligo a todas las entidades del sector llevar a cabo un estudio de encuesta para recopilar datos cualitativos para un mejor registro de credito de los prestatarios.
Pierre Trifilieff - One of the best experts on this subject based on the ideXlab platform.
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Detecting G protein-coupled receptor complexes in postmortem human brain with proximity ligation assay and a Bayesian Classifier
Biotechniques, 2020Co-Authors: Ying Zhu, Pierre Trifilieff, József Mészáros, Roman Walle, Rongxi Fan, Ziyi Sun, Andrew Dwork, Jonathan A JavitchAbstract:Despite the controversy regarding the existence and physiological relevance of class A G protein-coupled receptor dimerization, there is substantial evidence for functional interactions between the dopamine D2 receptor (D2R) and the adenosine A2A receptor (A2AR). A2AR-D2R complexes have been detected in rodent brains by proximity ligation assay; however, their existence in the human brain has not been demonstrated. In this study, we used Brightfield proximity ligation assay, combined with a systematic sampling and a parameter-free naive Bayesian Classifier, and demonstrated proximity between the D2R and the A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. These methods are applicable to the relative quantification of proximity of two proteins, as well as the expression levels of individual proteins. METHOD SUMMARY : Brightfield proximity ligation assay was used to assess the expression of G protein-coupled receptors and their proximity in postmortem adult human brains. A novel automated machine learning method (Bayesian optimized PLA signal sorting) was developed to automatically quantify Brightfield proximity ligation assay data.
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detecting gpcr complexes in postmortem human brain with proximity ligation assay and a Bayesian Classifier
bioRxiv, 2019Co-Authors: Ying Zhu, Pierre Trifilieff, József Mészáros, Roman Walle, Rongxi Fan, Ziyi Sun, Andrew Dwork, Jonathan A JavitchAbstract:Abstract Despite the general controversy regarding the existence and physiological relevance of Class A GPCR dimerization, there is substantial evidence for functional interactions between dopamine D2 receptor (D2R) and adenosine A2A receptor (A2AR). A2AR-D2R complexes have been detected in rodent brains by proximity ligation assay (PLA), but their existence in the human brain is yet to be demonstrated. In this study, we used brightfield PLA, combined with a systematic sampling and a parameter-free naive Bayesian Classifier, and demonstrated proximity between D2R and A2AR in the adult human ventral striatum, consistent with their colocalization within complexes and the possible existence of D2R-A2AR heteromers. These methods are applicable to the quantitative analysis of proximity of two proteins and the expression of individual proteins. Method Summary Brightfield proximity ligation assay (PLA) was used to assess the expression of G protein-coupled receptors and their proximity in postmortem adult human brains. A novel automated machine learning method (Bayesian Optimized PLA Signal Sorting) was developed to automatically quantify brightfield PLA data.
