ArticleBMC medical informatics and decision making2020
A decision tree to improve identification of pathogenic mutations in clinical practice.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of Artificial Intelligence for Personalized Medicine in Neoplasms: A Systematic Review.BioMed research international · 2022Pooled it
- CaRinDB: an integrated database of common cancer mutations and residue interaction network parameters.Bioinformatics advances · 2026Article
- Leveraging artificial intelligence for risk stratification of inherited cardiomyopathies in under-resourced settings.Heart rhythm O2 · 2025Article
- DTreePred: an online viewer based on machine learning for pathogenicity prediction of genomic variants.BMC bioinformatics · 2025Article
- A biomarker discovery of acute myocardial infarction using feature selection and machine learning.Medical & biological engineering & computing · 2023Article
- Brazilian XP-E siblings carrying a novel DDB2 variant developed early-onset melanoma: a case report.BMC medical genomics · 2023Article
- Diagnosis of Epstein-Barr and cytomegalovirus infections using decision trees: an effective way to avoid antibiotic overuse in paediatric tonsillopharyngitis.BMC pediatrics · 2023Article
- A Retrospective Cohort Study: Predicting 90-Day Mortality for ICU Trauma Patients with a Machine Learning Algorithm Using XGBoost Using MIMIC-III Database.Journal of multidisciplinary healthcare · 2023Article
- Genomic, transcriptomic and RNA editing analysis of human MM1 and VV2 sporadic Creutzfeldt-Jakob disease.Acta neuropathologica communications · 2022Article
- Article
- A machine learning approach based on ACMG/AMP guidelines for genomic variant classification and prioritization.Scientific reports · 2022Article
- Identification of recurrent genetic patterns from targeted sequencing panels with advanced data science: a case-study on sporadic and genetic neurodegenerative diseases.BMC medical genomics · 2022Article
- Computational approaches for predicting variant impact: An overview from resources, principles to applications.Frontiers in genetics · 2022Review
- Application of machine learning algorithms in predicting HIV infection among men who have sex with men: Model development and validation.Frontiers in public health · 2022Article
- VPMBench: a test bench for variant prioritization methods.BMC bioinformatics · 2021Article
- Bone morphological feature extraction for customized bone plate design.Scientific reports · 2021Article
- Germline Compound Heterozygous Variants Identified in theFrontiers in pediatrics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundA 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.
methodsIn 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.
resultsThe 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.
conclusionsThe 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.