ArticleScientific reports2017
Building a genetic risk model for bipolar disorder from genome-wide association data with random forest algorithm.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations.CPT: pharmacometrics & systems pharmacology · 2017Trial
- Exploring Random Forest in Genetic Risk Score Construction.Genetic epidemiology · 2025Article
- Functional variants identify sex-specific genes and pathways in Alzheimer's Disease.Nature communications · 2023Article
- The application of machine learning to predict genetic relatedness using human mtDNA hypervariable region I sequences.PloS one · 2022Article
- Machine learning algorithm performance evaluation in structural magnetic resonance imaging-based classification of pediatric bipolar disorders type I patients.Frontiers in computational neuroscience · 2022Article
- New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients.Frontiers in endocrinology · 2022Observational
- The Role of Machine Learning in Diagnosing Bipolar Disorder: Scoping Review.Journal of medical Internet research · 2021Article
- The roles of antimicrobial resistance, phage diversity, isolation source and selection in shaping the genomic architecture ofMicrobial genomics · 2021Article
- The Role of the UPR Pathway in the Pathophysiology and Treatment of Bipolar Disorder.Frontiers in cellular neuroscience · 2021Review
- Validation of a clinical-genetics score to predict hemorrhagic transformations after rtPA.Neurology · 2019Article
- Multivariate Pattern Analysis of Genotype-Phenotype Relationships in Schizophrenia.Schizophrenia bulletin · 2018Article
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2 authors.
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Abstract
A genetic risk score could be beneficial in assisting clinical diagnosis for complex diseases with high heritability. With large-scale genome-wide association (GWA) data, the current study constructed a genetic risk model with a machine learning approach for bipolar disorder (BPD). The GWA dataset of BPD from the Genetic Association Information Network was used as the training data for model construction, and the Systematic Treatment Enhancement Program (STEP) GWA data were used as the validation dataset. A random forest algorithm was applied for pre-filtered markers, and variable importance indices were assessed. 289 candidate markers were selected by random forest procedures with good discriminability; the area under the receiver operating characteristic curve was 0.944 (0.935-0.953) in the training set and 0.702 (0.681-0.723) in the STEP dataset. Using a score with the cutoff of 184, the sensitivity and specificity for BPD was 0.777 and 0.854, respectively. Pathway analyses revealed important biological pathways for identified genes. In conclusion, the present study identified informative genetic markers to differentiate BPD from healthy controls with acceptable discriminability in the validation dataset. In the future, diagnosis classification can be further improved by assessing more comprehensive clinical risk factors and jointly analysing them with genetic data in large samples.
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