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Ensemble approach to predict specificity determinants: benchmarking and validation.

ABSTRACT: It is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results.It was shown that the analysis of structural characteristics of predicted specificity determining sites might provide the means to validate their prediction accuracy. For example, we found that for smaller distances it holds true that the more reliable the prediction method is, the closer predicted specificity determining sites are to each other and to the ligand.We observed certain similarities of structural features between predicted and actual subsites which might point to their functional relevance. We speculate that majority of the identified potential specificity determining sites might be indirectly involved in specific interactions and could be ideal target for mutagenesis experiments.

SUBMITTER: Chakrabarti S

PROVIDER: S-EPMC2716344 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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Ensemble approach to predict specificity determinants: benchmarking and validation.

Chakrabarti Saikat S Panchenko Anna R AR

BMC bioinformatics 20090702

<h4>Background</h4>It is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement o ...[more]

PMID: 19573245

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Project description:Investigate success rates of cannulating a 'virgin' papilla during endoscopic retrograde cholangiopancreatography (ERCP) at a tertiary referral centre; determine reasons for failure and propose learnings for consideration in future revision of success benchmarking.Review of all ERCPs recorded on Endosoft database from 2006 to 2012 (n=1862). Specifically, 'virgin' papillae, defined as those with no evidence of prior surgical intervention, stents in situ or sphincterotomy (n=947). Virgin papillae present the most challenging target for endoscopists.Gastroenterology department, St Thomas' Hospital, London.All patients who underwent an ERCP recorded on Endosoft from 2006 to 2012 (n=1134). A proportion of these patients underwent repeat procedures, all considered virgin provided the aforementioned criteria were met.None, retrospective audit and benchmarking exercise.Determine criteria for successful cannulation of a virgin papilla.Overall success of cannulation of a virgin papilla at ERCP was 79.5%, 753 out of a total of 947 virgin papillae cases. Per patient with a virgin papilla, the success rate was 79.7%, 693 out of 869. Eliminating cases with features complicating cannulation increased success rates to 86% and 87%, respectively. Chronic pancreatitis was the single Indication associated with a failed cannulation (OR=3.9, CI 2.1 to 7.1), while biliary stones were significantly associated with a successful cannulation (OR=0.3, CI 0.2 to 0.4). Reasons for failure included patient agitation (OR=27.1, CI 7.9 to 92.7), duodenal stricturing (OR=12.5, CI 5.5 to 28.5), previous anatomy-changing surgery (OR=12.2, CI 3.3 to 45.4), tumour impingement (OR=9.5, CI 4.1 to 22.3) and equipment failure (OR=7.9, CI 1.4=43.5).The Joint Advisory Group's 80% success rate for completion of therapeutic intent must be viewed in light of published difficulty rating scales, if fair comparisons and standards are to be met. This highlights the need for standardised success criterion for ERCP training and accreditation.

Project description:BackgroundQuestionnaires have been used in the past 2 decades to predict the diagnosis of vertigo and assist clinical decision-making. A questionnaire-based machine learning model is expected to improve the efficiency of diagnosis of vestibular disorders.ObjectiveThis study aims to develop and validate a questionnaire-based machine learning model that predicts the diagnosis of vertigo.MethodsIn this multicenter prospective study, patients presenting with vertigo entered a consecutive cohort at their first visit to the ENT and vertigo clinics of 7 tertiary referral centers from August 2019 to March 2021, with a follow-up period of 2 months. All participants completed a diagnostic questionnaire after eligibility screening. Patients who received only 1 final diagnosis by their treating specialists for their primary complaint were included in model development and validation. The data of patients enrolled before February 1, 2021 were used for modeling and cross-validation, while patients enrolled afterward entered external validation.ResultsA total of 1693 patients were enrolled, with a response rate of 96.2% (1693/1760). The median age was 51 (IQR 38-61) years, with 991 (58.5%) females; 1041 (61.5%) patients received the final diagnosis during the study period. Among them, 928 (54.8%) patients were included in model development and validation, and 113 (6.7%) patients who enrolled later were used as a test set for external validation. They were classified into 5 diagnostic categories. We compared 9 candidate machine learning methods, and the recalibrated model of light gradient boosting machine achieved the best performance, with an area under the curve of 0.937 (95% CI 0.917-0.962) in cross-validation and 0.954 (95% CI 0.944-0.967) in external validation.ConclusionsThe questionnaire-based light gradient boosting machine was able to predict common vestibular disorders and assist decision-making in ENT and vertigo clinics. Further studies with a larger sample size and the participation of neurologists will help assess the generalization and robustness of this machine learning method.

Dataset Information

Ensemble approach to predict specificity determinants: benchmarking and validation.

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Ensemble approach to predict specificity determinants: benchmarking and validation.

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