Genetic clustering of depressed patients and normal controls based on single-nucleotide variant proportion

Yu, Chenglong; Baune, Bernhard T.; Fu, Ke-Ang; Wong, Ma Li; Licinio, Júlio

doi:10.1016/j.jad.2017.11.023

Cited by 8 publications

(4 citation statements)

References 21 publications

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“…Since we evaluated the SNPs using Japanese, we should be cautious for interpretation of the results. Third, new additional genes associated with MDD42–49 were not examined. Fourth, the result was a chance finding because, sample number was small.…”

Section: Discussionmentioning

confidence: 99%

<p>A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder</p>

Katsuki¹,

Kakeda²,

Watanabe³

et al. 2019

NDT

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ObjectiveRecently, a genome-wide association study successfully identified genetic variants associated with major depressive disorder (MDD). The study identified 17 independent single-nucleotide polymorphisms (SNPs) significantly associated with diagnosis of MDD. These SNPs were predicted to be enriched in genes that are expressed in the central nervous system and function in transcriptional regulation associated with neurodevelopment. The study aimed to investigate associations between 17 SNPs and brain morphometry using magnetic resonance imaging (MRI) in drug-naïve patients with MDD and healthy controls (HCs).MethodsForty-seven patients with MDD and 42 HCs were included. All participants underwent T1-weighted structural MRI and genotyping. The genotype–diagnosis interactions associated with regional cortical thicknesses were evaluated using voxel-based morphometry for the 17 SNPs.ResultsRegarding rs301806, an SNP in the RERE genomic regions, we found a significant difference in a genotype effect in the right-lateral orbitofrontal and postcentral lobes between diagnosis groups. After testing every possible diagnostic comparison, the genotype–diagnosis interaction in these areas revealed that the cortical thickness reductions in the MDD group relative to those in the HC group were significantly larger in T/T individuals than in C-carrier ones. For the other SNPs, no brain area was noted where a genotype effect significantly differed between the two groups.ConclusionsWe found that a RERE gene SNP was associated with cortical thickness reductions in the right-lateral orbitofrontal and postcentral lobes in drug-naïve patients with MDD. The effects of RERE gene polymorphism and gene–environment interactions may exist in brain structures of patients with MDD.

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Section: Discussionmentioning

confidence: 99%

<p>A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder</p>

Katsuki¹,

Kakeda²,

Watanabe³

et al. 2019

NDT

View full text Add to dashboard Cite

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“…Subsequent work may include to develop ML predictive models that can classify new individuals to such derived groups (Roman, 2019). Interestingly, the combination unsupervised ML techniques may lead to the identification of individuals exhibiting differential clinical profiles (i.e., extreme phenotypes; Acosta et al, 2011;Arcos-Burgos et al, 2019;Elia et al, 2009;Pérez-Gracia et al, 2010;Vidal et al, 2020;Yu et al, 2017;Yu et al, 2018), hence contributing to the development of personalized interventions, treatments, and follow-up strategies. The combination of supervised and unsupervised ML techniques as well as the automation of the data analysis process could allow the development of data-driven Intelligent Systems supporting psychologists to make more accurate and timely decisions (de Mello & de Souza, 2019;Luxton, 2016).…”

Section: Psychology: Predictive Models Clustering and Intelligent Systemsmentioning

confidence: 99%

Machine Learning based Psychology: Advocating for A Data-Driven Approach

Vélez

2021

Int. j. psychol. res.

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Since its beginnings, Psychology has been prone to both data generation and understanding of human behavior through data analysis. Back in 1879, Dr. Wilheim Wundt opened the first experimental psychology lab at the University of Leipzig to study reaction times. To many, this is considered the start of Psychology as a separate scientific discipline and the use of data analysis for data-driven decision making in the field (Flis, 2019; Tweney, 2003). In this Editorial, we briefly discuss how Psychology students, clinicians, and researchers may take part of the data revolution and help transforming Psychology, as we know it, into Machine Learning Psychology.

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“…A Bayesian nonparametric clustering approach was applied to divide patients having cancer into sub-groups to measure their anxiety and depression scores before psychotherapy [17]. Further, in a study authors proposed a hierarchical clustering algorithm incorporating genetic concept for partitioning the patients with or without depression [18]. Furthermore, many intelligent nature-inspired algorithms have also been proposed for data clustering.…”

Section: Literature Reviewmentioning

confidence: 99%

An Optimized Framework for Surgical Team Selection

Petwal¹,

Rani²

2021

Computers, Materials &Amp; Continua

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In the healthcare system, a surgical team is a unit of experienced personnel who provide medical care to surgical patients during surgery. Selecting a surgical team is challenging for a multispecialty hospital as the performance of its members affects the efficiency and reliability of the hospital's patient care. The effectiveness of a surgical team depends not only on its individual members but also on the coordination among them. In this paper, we addressed the challenges of surgical team selection faced by a multispecialty hospital and proposed a decision-making framework for selecting the optimal list of surgical teams for a given patient. The proposed framework focused on improving the existing surgical history management system by arranging surgery-bound patients into optimal subgroups based on similar characteristics and selecting an optimal list of surgical teams for a new surgical patient based on the patient's subgroups. For this end, two population-based meta-heuristic algorithms for clustering of mixed datasets and multi-objective optimization were proposed. The proposed algorithms were tested using different datasets and benchmark functions. Furthermore, the proposed framework was validated through a case study of a real postoperative surgical dataset obtained from the orthopedic surgery department of a multispecialty hospital in India. The results revealed that the proposed framework was efficient in arranging patients in optimal groups as well as selecting optimal surgical teams for a given patient.

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Genetic clustering of depressed patients and normal controls based on single-nucleotide variant proportion

Abstract: High quality sequencing costs limited our ability to obtain larger datasets.

Cited by 8 publications

References 21 publications

<p>A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder</p>

<p>A single-nucleotide polymorphism influences brain morphology in drug-naïve patients with major depressive disorder</p>

Machine Learning based Psychology: Advocating for A Data-Driven Approach

An Optimized Framework for Surgical Team Selection

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