39 research outputs found

    LEGAL EDUCATION IN GERMANY

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    <div><p>Predicting the subcellular localization of proteins conquers the major drawbacks of high-throughput localization experiments that are costly and time-consuming. However, current subcellular localization predictors are limited in scope and accuracy. In particular, most predictors perform well on certain locations or with certain data sets while poorly on others. Here, we present PSI, a novel high accuracy web server for plant subcellular localization prediction. PSI derives the wisdom of multiple specialized predictors via a joint-approach of group decision making strategy and machine learning methods to give an integrated best result. The overall accuracy obtained (up to 93.4%) was higher than best individual (CELLO) by ∼10.7%. The precision of each predicable subcellular location (more than 80%) far exceeds that of the individual predictors. It can also deal with multi-localization proteins. PSI is expected to be a powerful tool in protein location engineering as well as in plant sciences, while the strategy employed could be applied to other integrative problems. A user-friendly web server, PSI, has been developed for free access at <a href="http://bis.zju.edu.cn/psi/" target="_blank">http://bis.zju.edu.cn/psi/</a>.</p></div

    The tidemark roughness at the six selected locations on the tibial plateau of the PTOA and control knees.

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    <p>The tidemark roughness in the control knees varies from area to area on the tibial plateau. In the PTOA knees, there are not regional differences in tidemark roughness. In addition, the generally reduced tidemark roughness in the PTOA knees is statistically significant at the central and medial areas of medial plateau, when compared with the controls. Note: * indicates p < 0.05. L = lateral plateau; M = medial plateau.</p

    Model for combination of group voting and neural network.

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    <p>Raw prediction results from 11 predictors first entered ranking system to give prediction for each subcellular location of elevated-accuracy. Then neural network took group-voting output as input for further calculation and adjustment. Final prediction results were given through neural network.</p

    Correlation between Mankin’s score and tidemark roughness.

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    <p>In the PTOA knees, Mankin’s score and tidemark roughness are reversely correlated (p < 0.05).</p

    A) Histology of PTOA knees.

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    <p>A, B and C are the peripheral, central and medial regions of the medial plateau in the control knees. D, E, and F are the peripheral, central and medial regions of the medial plateau of the PTOA knees, respectively. While the cartilage in the control knees is intact and uniformly stained for extracellular matrix, the cartilage in the PTOA joints shows clustering cells, decreased and uneven staining of proteoglycans, and surface fibrillation (Safranin-O/Fast green/Hematoxylin staining). B) Mankin’s scores of six selected locations on the tibial plateau of the PTOA and control knees. PTOA developed in all three locations on the medial plateau and the peripheral area of the lateral plateau after four weeks of ACL transection and meniscectomy. Note: * indicates p < 0.05; ** p < 0.001. L = lateral plateau; M = medial plateau; bar = 20μm.</p

    Determination of the model topological structure.

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    <p>(a) Performance under different model structures with full data as input. Blue lines denote the training set and red lines denote the test set. Peak for prediction was on 10 neurons, 1 hidden layer. (b) Stepwise-selection performance was evaluated by AUROC. Obvious enhancement took place in step 2, 3 and 4. Peak was in step 9, with best community consisting of cello, Wolf PSORT, MultiLoc, mPloc, YLoc and iPsort. (c) With selected community of predictors, model topological structure was determined using the same method in (a). Peak was on 10 neurons, 1 hidden layer. (d) Model structure evaluation for combination of group-voting and neural network. Results from group-voting were taken as input for neural network. Peak was on 10 neurons, 1 hidden layer. The best results are boxed in dash.</p

    A) Diagram of selected locations on tibial plateau for Mankin’s score and CZC measurements.

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    <p>Showing here is a lateral tibial plateau, where standardized areas are defined in the evenly divided three portions of the plateau: peripheral portion, near the edge of the plateau; the central portion, in the middle of the plateau; and the medial portion, close to the cruciate ligaments. Three areas are defined in the same way on the medial plateau (bar = 50μm). B) CZC measurements, using ImageJ. The tidemark was traced for true length (L, white arrows). The straight line (black arrows) was drawn, as L0, for calculation of tidemark roughness. The cement line between CZC and subchondral bone was also traced (open arrows) for measurements of CZC area between the tidemark and cement line (bar = 20μm).</p

    DataSheet5_Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction.CSV

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    Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.</p

    DataSheet8_Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction.CSV

    No full text
    Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.</p

    DataSheet4_Support vector machine deep mining of electronic medical records to predict the prognosis of severe acute myocardial infarction.CSV

    No full text
    Cardiovascular disease is currently one of the most important diseases causing death in China and the world, and acute myocardial infarction is a major cause of cardiovascular disease. This study provides an analytical technique for predicting the prognosis of patients with severe acute myocardial infarction using a support vector machine (SVM) technique based on information gleaned from electronic medical records in the Medical Information Marketplace for Intensive Care (MIMIC)-III database. The MIMIC-III database provided 4785 electronic medical records data for inclusion in the model development after screening 7070 electronic medical records of patients admitted to the intensive care unit for treatment of acute myocardial infarction. Adopting the APS-III score as the criterion for identifying anticipated risk, the dimensions of data information incorporated into the mathematical model design were found using correlation coefficient matrix heatmaps and ordered logistic analysis. An automated prognostic risk-prediction model was developed using SVM, and the fit was evaluated by 5× cross-validation. We used a grid search method to further optimize the parameters and improve the model fit. The excellent generalization ability of SVM was fully verified by calculating the 95% confidence interval of the area under the receiver operating characteristic curve (AUC) for six algorithms (linear discriminant, tree, Kernel Naive Bayes, RUSBoost, KNN, and SVM). Compared to the remaining five models, its confidence interval was the narrowest with higher fitting accuracy and better performance. The patient prognostic risk prediction model constructed using SVM had a relatively impressive accuracy (92.2%) and AUC value (0.98). In this study, a model was designed for fitting that can maximize the potential information to be gleaned in the electronic medical records data. It was demonstrated that SVM models based on electronic medical records data can offer an effective solution for clinical disease prognostic risk assessment and improved clinical outcomes and have great potential for clinical application in the clinical treatment of myocardial infarction.</p
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