Transcriptomics and machine learning to advance schizophrenia genetics: A case-control study using post-mortem brain data

Qi, Bill; Boscenco, Sonia; Ramamurthy, Janani; Trakadis, Yannis

doi:10.1016/j.cmpb.2021.106590

Cited by 3 publications

(2 citation statements)

References 47 publications

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“…The AutoML technique was implemented for applying various classifiers to determine the laterality of seizure onset in FLE using HRV parameters as input features (He et al, 2021 ; Qi et al, 2022 ). Four frequency domain (i.e., VLF, LF, HF, and LF/HF ratio) and six time domain HRV parameters (i.e., mean NN interval, SDNN, RMSSD, SDSD, pNN20, and pNN50) were utilized as input features.…”

Section: Methodsmentioning

confidence: 99%

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy

Lee,

Kim,

Kim

et al. 2024

Front. Neurosci.

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Determining the laterality of the seizure onset zone is challenging in frontal lobe epilepsy (FLE) due to the rapid propagation of epileptic discharges to the contralateral hemisphere. There is hemispheric lateralization of autonomic control, and heart rate is modulated by interactions between the sympathetic and parasympathetic nervous systems. Based on this notion, the laterality of seizure foci in FLE might be determined using heart rate variability (HRV) parameters. We explored preictal markers for differentiating the laterality of seizure foci in FLE using HRV parameters. Twelve patients with FLE (6 right FLE and 6 left FLE) were included in the analyzes. A total of 551 (460 left FLE and 91 right FLE) 1-min epoch electrocardiography data were used for HRV analysis. We found that most HRV parameters differed between the left and right FLE groups. Among the machine learning algorithms applied in this study, the light gradient boosting machine was the most accurate, with an AUC value of 0.983 and a classification accuracy of 0.961. Our findings suggest that HRV parameter-based laterality determination models can be convenient and effective tools in clinical settings. Considering that heart rate can be easily measured in real time with a wearable device, our proposed method can be applied to a closed-loop device as a real-time monitoring tool for determining the side of stimulation.

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Section: Methodsmentioning

confidence: 99%

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy

Lee,

Kim,

Kim

et al. 2024

Front. Neurosci.

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“…Using data from the iPSYCH2012 case cohort, another study integrated genetics and registry data with a deep learning approach to stratify 19,636 patients with schizophrenia and/or major depressive disorder into clinically distinct subgroups, characterized by unique disorder severities and comorbidity signatures, with predictive models achieving AUCs of 0.55 to 0.97, and therefore emphasized the importance of data-driven stratification for improving psychiatric diagnosis and prognosis [40]. Similarly, Qi and colleagues analyzed gene expression datasets from untreated schizophrenia patients and controls, identified 14 key gene probes, and used artificial NN to achieve diagnostic accuracy of 91.2% in training and 87.9% in testing and highlighted the potential of machine learning in identifying clinically useful biomarkers for schizophrenia [41]. Another study introduced a sparse deep NN approach for identifying interpretable features for schizophrenia casecontrol classification using gray matter volume and single nucleotide polymorphism data, demonstrating slightly improved performance over traditional methods, and highlighting key brain regions related to the schizophrenia [42].…”

Section: Predicting Schizophreniamentioning

confidence: 99%

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: A Scoping Review (Preprint)

Hudon,

Beaudoin,

Phraxayavong

et al. 2024

Preprint

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BACKGROUND An increasing body of literature highlights the integration of machine learning with genomic data in psychiatry, particularly for complex mental health disorders such as schizophrenia. These advanced techniques offer promising potential for uncovering various facets of these disorders. A comprehensive review of the current applications of machine learning in conjunction with genomic data within this context can significantly enhance our understanding of the current state of research and its future directions. OBJECTIVE The objective of this study is to conduct a systematic scoping review of the use of machine learning algorithms with genomic data in the field of schizophrenia. METHODS A systematic, scoping review, search was performed in the electronic databases of Medline, Web of Science, PsycNet (PsycINFO), and Google Scholar from 2013 to 2024. Studies at the intersection of schizophrenia, genomic data, and machine learning were evaluated. RESULTS The literature search identified 2437 eligible articles after removing duplicates. Following abstract screening, 143 full-text articles were assessed, and 121 were subsequently excluded. Therefore, 21 studies were thoroughly assessed. Various machine learning algorithms were used in the identified studies, with support vector machines being the most common. The studies notably used genomic data to predict schizophrenia, identifying schizophrenia features, discovering drugs, classifying schizophrenia amongst other mental health disorders, and predicting the quality-of-life of patients. CONCLUSIONS Several high-quality studies were identified. Yet, the application of machine learning with genomic data in the context of schizophrenia remains limited. Future research is essential to further evaluate the portability of these models and to explore their potential clinical applications.

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Automatic recognition of schizophrenia from facial videos using 3D convolutional neural network

Huang¹,

Zhao²,

Qu³

et al. 2022

Asian Journal of Psychiatry

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Transcriptomics and machine learning to advance schizophrenia genetics: A case-control study using post-mortem brain data

Cited by 3 publications

References 47 publications

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy

Heart rate variability as a preictal marker for determining the laterality of seizure onset zone in frontal lobe epilepsy

Exploring the Intersection of Schizophrenia, Machine Learning, and Genomics: A Scoping Review (Preprint)

Automatic recognition of schizophrenia from facial videos using 3D convolutional neural network

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