دورية أكاديمية

A decision tree to improve identification of pathogenic mutations in clinical practice.

التفاصيل البيبلوغرافية
العنوان: A decision tree to improve identification of pathogenic mutations in clinical practice.
المؤلفون: do Nascimento PM; Bioinformatics Postgraduate Program, Metrópole Digital Institute, Federal University of Rio Grande do Norte, Natal, Brazil., Medeiros IG; Bioinformatics Postgraduate Program, Metrópole Digital Institute, Federal University of Rio Grande do Norte, Natal, Brazil., Falcão RM; Bioinformatics Postgraduate Program, Metrópole Digital Institute, Federal University of Rio Grande do Norte, Natal, Brazil., Stransky B; Biomedical Engineering Department, Center of Technology, Federal University of Rio Grande do Norte, Natal, Brazil.; Bioinformatics Multidisciplinary Environment (BioME), Metrópole Digital Institute, Federal University of Rio Grande do Norte, Natal, Brazil., de Souza JES; Bioinformatics Postgraduate Program, Metrópole Digital Institute, Federal University of Rio Grande do Norte, Natal, Brazil. jorge@imd.ufrn.br.; Bioinformatics Multidisciplinary Environment (BioME), Metrópole Digital Institute, Federal University of Rio Grande do Norte, Natal, Brazil. jorge@imd.ufrn.br.
المصدر: BMC medical informatics and decision making [BMC Med Inform Decis Mak] 2020 Mar 10; Vol. 20 (1), pp. 52. Date of Electronic Publication: 2020 Mar 10.
نوع المنشور: Journal Article; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: BioMed Central Country of Publication: England NLM ID: 101088682 Publication Model: Electronic Cited Medium: Internet ISSN: 1472-6947 (Electronic) Linking ISSN: 14726947 NLM ISO Abbreviation: BMC Med Inform Decis Mak Subsets: MEDLINE
أسماء مطبوعة: Original Publication: London : BioMed Central, [2001-
مواضيع طبية MeSH: Algorithms* , Decision Trees* , Mutation*, Humans ; Precision Medicine/methods ; Supervised Machine Learning ; Virulence/genetics
مستخلص: Background: A variant of unknown significance (VUS) is a variant form of a gene that has been identified through genetic testing, but whose significance to the organism function is not known. An actual challenge in precision medicine is to precisely identify which detected mutations from a sequencing process have a suitable role in the treatment or diagnosis of a disease. The average accuracy of pathogenicity predictors is 85%. However, there is a significant discordance about the identification of mutational impact and pathogenicity among them. Therefore, manual verification is necessary for confirming the real effect of a mutation in its casuistic.
Methods: In this work, we use variables categorization and selection for building a decision tree model, and later we measure and compare its accuracy with four known mutation predictors and seventeen supervised machine-learning (ML) algorithms.
Results: The results showed that the proposed tree reached the highest precision among all tested variables: 91% for True Neutrals, 8% for False Neutrals, 9% for False Pathogenic, and 92% for True Pathogenic.
Conclusions: The decision tree exceptionally demonstrated high classification precision with cancer data, producing consistently relevant forecasts for the sample tests with an accuracy close to the best ones achieved from supervised ML algorithms. Besides, the decision tree algorithm is easier to apply in clinical practice by non-IT experts. From the cancer research community perspective, this approach can be successfully applied as an alternative for the determination of potential pathogenicity of VOUS.
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فهرسة مساهمة: Keywords: Decision tree; Mutation; Pathogenicity; Precision medicine; Predictor; VOUS
تواريخ الأحداث: Date Created: 20200311 Date Completed: 20201005 Latest Revision: 20240328
رمز التحديث: 20240329
مُعرف محوري في PubMed: PMC7063785
DOI: 10.1186/s12911-020-1060-0
PMID: 32151256
قاعدة البيانات: MEDLINE
الوصف
تدمد:1472-6947
DOI:10.1186/s12911-020-1060-0