ArticleTranslational psychiatry2019
Multimodel inference for biomarker development: an application to schizophrenia.
Article in Translational psychiatry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05257720 (Evaluation of the Role of Neurodegeneration in Treatment Resistant Schizophrenia With GFAP and S100B), which is not on this map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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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.
Evaluation of the Role of Neurodegeneration in Treatment Resistant Schizophrenia With GFAP and S100B
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Implementing Precision Psychiatry: A Systematic Review of Individualized Prediction Models for Clinical Practice.Schizophrenia bulletin · 2021Pooled it
- Association of central adiposity indices with cognitive impairment in elderly populations: Development and validation of a risk prediction nomogram using NHANES and CHARLS cohorts.Journal of Alzheimer's disease : JAD · 2026Article
- Blood Plasma Lipid Alterations Differentiating Psychotic and Affective Disorder Patients.Biomolecules · 2025Article
- Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.Frontiers in veterinary science · 2025Article
- Precision Medicine Approaches to Mental Health Care.Physiology (Bethesda, Md.) · 2023Review
- Development and Validation of Predictive Model for a Diagnosis of First Episode Psychosis Using the Multinational EU-GEI Case-control Study and Modern Statistical Learning Methods.Schizophrenia bulletin open · 2023Article
- Diagnostic model development for schizophrenia based on peripheral blood mononuclear cell subtype-specific expression of metabolic markers.Translational psychiatry · 2022Article
- Learnings from user feedback of a novel digital mental health assessment.Frontiers in psychiatry · 2022Article
- Screening of key biomarkers and immune infiltration in Pulmonary Arterial Hypertension via integrated bioinformatics analysis.Bioengineered · 2021Article
- A machine learning algorithm to differentiate bipolar disorder from major depressive disorder using an online mental health questionnaire and blood biomarker data.Translational psychiatry · 2021Article
- A Combined Digital and Biomarker Diagnostic Aid for Mood Disorders (the Delta Trial): Protocol for an Observational Study.JMIR research protocols · 2020Article
- Bacterial DNA induces the formation of heat-resistant disease-associated proteins in human plasma.Scientific reports · 2019Observational
- Integrating proteomic, sociodemographic and clinical data to predict future depression diagnosis in subthreshold symptomatic individuals.Translational psychiatry · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the present study, to improve the predictive performance of a model and its reproducibility when applied to an independent data set, we investigated the use of multimodel inference to predict the probability of having a complex psychiatric disorder. We formed training and test sets using proteomic data (147 peptides from 77 proteins) from two-independent collections of first-onset drug-naive schizophrenia patients and controls. A set of prediction models was produced by applying lasso regression with repeated tenfold cross-validation to the training set. We used feature extraction and model averaging across the set of models to form two prediction models. The resulting models clearly demonstrated the utility of a multimodel based approach to make good (training set AUC > 0.80) and reproducible predictions (test set AUC > 0.80) for the probability of having schizophrenia. Moreover, we identified four proteins (five peptides) whose effect on the probability of having schizophrenia was modified by sex, one of which was a novel potential biomarker of schizophrenia, foetal haemoglobin. The evidence of effect modification suggests that future schizophrenia studies should be conducted in males and females separately. Future biomarker studies should consider adopting a multimodel approach and going beyond the main effects of features.
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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.