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Definition and classification of evaluation units for CASP10.

ABSTRACT: For the 10th experiment on Critical Assessment of the techniques of protein Structure Prediction (CASP), the prediction target proteins were broken into independent evaluation units (EUs), which were then classified into template-based modeling (TBM) or free modeling (FM) categories. We describe here how the EUs were defined and classified, what issues arose in the process, and how we resolved them. EUs are frequently not the whole target proteins but the constituting structural domains. However, the assessors from CASP7 on combined more than one domain into 1 EU for some targets, which implied that the assessment also included evaluation of the prediction of the relative position and orientation of these domains. In CASP10, we followed and expanded this notion by defining multidomain EUs for a number of targets. These included 3 EUs, each made of two domains of familiar fold but arranged in a novel manner and for which the focus of evaluation was the interdomain arrangement. An EU was classified to the TBM category if a template could be found by sequence similarity searches and to FM if a structural template could not be found by structural similarity searches. The EUs that did not fall cleanly in either of these cases were classified case-by-case, often including consideration of the overall quality and characteristics of the predictions.

SUBMITTER: Taylor TJ

PROVIDER: S-EPMC4133092 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

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Definition and classification of evaluation units for CASP10.

Taylor Todd J TJ Tai Chin-Hsien CH Huang Yuanpeng J YJ Block Jeremy J Bai Hongjun H Kryshtafovych Andriy A Montelione Gaetano T GT Lee Byungkook B

Proteins 20131122

For the 10th experiment on Critical Assessment of the techniques of protein Structure Prediction (CASP), the prediction target proteins were broken into independent evaluation units (EUs), which were then classified into template-based modeling (TBM) or free modeling (FM) categories. We describe here how the EUs were defined and classified, what issues arose in the process, and how we resolved them. EUs are frequently not the whole target proteins but the constituting structural domains. However ...[more]

PMID: 24123179

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Project description:The article presents an assessment of the ability of the thirty-seven model quality assessment (MQA) methods participating in CASP10 to provide an a priori estimation of the quality of structural models, and of the 67 tertiary structure prediction groups to provide confidence estimates for their predicted coordinates. The assessment of MQA predictors is based on the methods used in previous CASPs, such as correlation between the predicted and observed quality of the models (both at the global and local levels), accuracy of methods in distinguishing between good and bad models as well as good and bad regions within them, and ability to identify the best models in the decoy sets. Several numerical evaluations were used in our analysis for the first time, such as comparison of global and local quality predictors with reference (baseline) predictors and a ROC analysis of the predictors' ability to differentiate between the well and poorly modeled regions. For the evaluation of the reliability of self-assessment of the coordinate errors, we used the correlation between the predicted and observed deviations of the coordinates and a ROC analysis of correctly identified errors in the models. A modified two-stage procedure for testing MQA methods in CASP10 whereby a small number of models spanning the whole range of model accuracy was released first followed by the release of a larger number of models of more uniform quality, allowed a more thorough analysis of abilities and inabilities of different types of methods. Clustering methods were shown to have an advantage over the single- and quasi-single- model methods on the larger datasets. At the same time, the evaluation revealed that the size of the dataset has smaller influence on the global quality assessment scores (for both clustering and nonclustering methods), than its diversity. Narrowing the quality range of the assessed models caused significant decrease in accuracy of ranking for global quality predictors but essentially did not change the results for local predictors. Self-assessment error estimates submitted by the majority of groups were poor overall, with two research groups showing significantly better results than the remaining ones.

Project description:ObjectivesThe objectives of this study are to construct the high definition phenotype (HDP), a novel time-series data structure composed of both primary and derived parameters, using heterogeneous clinical sources and to determine whether different predictive models can utilize the HDP in the neonatal intensive care unit (NICU) to improve neonatal mortality prediction in clinical settings.Materials and methodsA total of 49 primary data parameters were collected from July 2018 to May 2020 from eight level-III NICUs. From a total of 1546 patients, 757 patients were found to contain sufficient fixed, intermittent, and continuous data to create HDPs. Two different predictive models utilizing the HDP, one a logistic regression model (LRM) and the other a deep learning long-short-term memory (LSTM) model, were constructed to predict neonatal mortality at multiple time points during the patient hospitalization. The results were compared with previous illness severity scores, including SNAPPE, SNAPPE-II, CRIB, and CRIB-II.ResultsA HDP matrix, including 12 221 536 minutes of patient stay in NICU, was constructed. The LRM model and the LSTM model performed better than existing neonatal illness severity scores in predicting mortality using the area under the receiver operating characteristic curve (AUC) metric. An ablation study showed that utilizing continuous parameters alone results in an AUC score of >80% for both LRM and LSTM, but combining fixed, intermittent, and continuous parameters in the HDP results in scores >85%. The probability of mortality predictive score has recall and precision of 0.88 and 0.77 for the LRM and 0.97 and 0.85 for the LSTM.Conclusions and relevanceThe HDP data structure supports multiple analytic techniques, including the statistical LRM approach and the machine learning LSTM approach used in this study. LRM and LSTM predictive models of neonatal mortality utilizing the HDP performed better than existing neonatal illness severity scores. Further research is necessary to create HDP-based clinical decision tools to detect the early onset of neonatal morbidities.

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Definition and classification of evaluation units for CASP10.

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Definition and classification of evaluation units for CASP10.

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