ArticleScientific reports2021
An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Gut-brain axis and neuroplasticity in health and disease: a systematic review.La Radiologia medica · 2025Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- The Brain-Gut Health Initiative (BIGHI): A Prospective Cohort on Psychiatric Disorders in China.Research (Washington, D.C.) · 2026Review
- Multi-kingdom microbial changes and their associations with the clinical characteristics in schizophrenia patients.Translational psychiatry · 2025Article
- Association between redox dysregulation and vulnerability to cognitive deficits induced by maternal immune activation.Translational psychiatry · 2025Article
- Investigating the gut microbiome in schizophrenia cases versus controls: South Africa's version.Neurogenetics · 2025Article
- Diagnosing schizophrenia with routine blood tests: a comparative analysis of machine learning algorithms.Frontiers in psychiatry · 2025Article
- A novel blood-based epigenetic biosignature in first-episode schizophrenia patients through automated machine learning.Translational psychiatry · 2024Article
- Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Building Predictive Models for Schizophrenia Diagnosis with Peripheral Inflammatory Biomarkers.Biomedicines · 2023Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Article
- Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran.International journal of environmental research and public health · 2023Article
- Adjustment ofFrontiers in genetics · 2023Article
- Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review).Sovremennye tekhnologii v meditsine · 2022Review
- Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study.Frontiers in neuroscience · 2021Article
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14 authors.
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Abstract
Finding effective and objective biomarkers to inform the diagnosis of schizophrenia is of great importance yet remains challenging. Relatively little work has been conducted on multi-biological data for the diagnosis of schizophrenia. In this cross-sectional study, we extracted multiple features from three types of biological data, including gut microbiota data, blood data, and electroencephalogram data. Then, an integrated framework of machine learning consisting of five classifiers, three feature selection algorithms, and four cross validation methods was used to discriminate patients with schizophrenia from healthy controls. Our results show that the support vector machine classifier without feature selection using the input features of multi-biological data achieved the best performance, with an accuracy of 91.7% and an AUC of 96.5% (p < 0.05). These results indicate that multi-biological data showed better discriminative capacity for patients with schizophrenia than single biological data. The top 5% discriminative features selected from the optimal model include the gut microbiota features (Lactobacillus, Haemophilus, and Prevotella), the blood features (superoxide dismutase level, monocyte-lymphocyte ratio, and neutrophil count), and the electroencephalogram features (nodal local efficiency, nodal efficiency, and nodal shortest path length in the temporal and frontal-parietal brain areas). The proposed integrated framework may be helpful for understanding the pathophysiology of schizophrenia and developing biomarkers for schizophrenia using multi-biological data.
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