ArticleSchizophrenia (Heidelberg, Germany)2025
Interpretable machine learning to evaluate relationships between DAO/DAOA (pLG72) protein data and features in clinical assessments, functional outcome, and cognitive function in schizophrenia patients.
Article in Schizophrenia (Heidelberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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Who cites it
3 citing papers in PubMed.
- Role of emotional intelligence in schizophrenia: ability-based vs. self-reported assessment.European archives of psychiatry and clinical neuroscience · 2026Article
- D-serine: A Multitalented Neuromodulator in Brain Function, Systemic Homeostasis, and Disease.Cellular and molecular neurobiology · 2026Review
- Impact of D-Amino Acids in Schizophrenia.Biomolecules · 2025Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Machine learning has been proposed to utilize D-amino acid oxidase (DAO) and DAO activator (DAOA [or pLG72]) protein levels to ascertain disease status in schizophrenia. However, it remains unclear whether machine learning can effectively evaluate clinical features in relation to DAO and DAOA in schizophrenia patients. We employed an interpretable machine learning (IML) framework including linear regression, least absolute shrinkage and selection operator (Lasso) models, and generalized additive models (GAMs) to analyze DAO/DAOA levels using 380 Taiwanese schizophrenia patients. Additionally, we incorporated 27 parameters encompassing demographic variables, clinical assessments, functional outcomes, and cognitive function as features. The IML framework facilitated linear and non-linear relationships between features and DAO/DAOA. DAO levels demonstrated significant associations with the 17-item Hamilton Depression Rating Scale (HAMD17) based on linear regression. The Lasso model identified four features-HAMD17, age, working memory, and overall cognitive function (OCF)-and highlighted HAMD17 as the most significant feature, using DAO from chronically stable patients. Utilizing DAOA from acutely exacerbated patients, the Lasso model also identified four features-OCF, Scale for the Assessment of Negative Symptoms 20-item, quality of life scale (QLS), and category fluency-and emphasized OCF as the most significant feature. Furthermore, GAMs revealed a non-linear relationship between category fluency and DAO in chronically stable patients, as well as between QLS and DAOA in acutely exacerbated patients. The study suggests that an IML framework holds promise for assessing linear and non-linear relationships between DAO/DAOA and various features in clinical assessments, functional outcomes, and cognitive function in patients with schizophrenia.
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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.