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精神分裂症研究在团队科学和大数据时代。

Schizophrenia research in the era of Team Science and big data.

机构信息

National Institute of Mental Health, United States of America.

National Institute of Mental Health, United States of America.

出版信息

Schizophr Res. 2020 Mar;217:13-16. doi: 10.1016/j.schres.2019.07.008. Epub 2019 Jul 16.

DOI:10.1016/j.schres.2019.07.008
PMID:31324441
Abstract

The last decade has provided new insights into the genetic architecture of schizophrenia. For the first time researchers have identified genetic factors conferring risk that can be mapped to tissue and cell specific perturbations of the molecular machinery underlying disease processes. However, it has also become clear that attempts to gain mechanistic insights into disease processes that span multiple levels of biological complexity, from genes to cells to circuits to behaviors, are inherently difficult and will require interdisciplinary efforts. Here we discuss the opportunities and pitfalls of developing causal models of SCZ that will lead to novel treatments and prevention strategies. We make the case that integrated large-scale Team Science efforts will be necessary to achieve this goal and that a systems level approach that includes genetics and integrative modelling is needed.

摘要

过去十年为精神分裂症的遗传结构提供了新的见解。研究人员首次发现了能够将风险因素映射到疾病过程分子机制的组织和细胞特定扰动的遗传因素。然而,人们也清楚地认识到,试图深入了解跨越多个生物学复杂性层次的疾病过程(从基因到细胞到回路再到行为)具有内在的困难,并且需要跨学科的努力。在这里,我们讨论了开发精神分裂症因果模型的机会和陷阱,这些模型将导致新的治疗和预防策略。我们认为,必须开展大型团队科学综合研究,以实现这一目标,并且需要包括遗传学和综合建模的系统方法。

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Application of Support Vector Machine on fMRI Data as Biomarkers in Schizophrenia Diagnosis: A Systematic Review.支持向量机在功能磁共振成像数据作为精神分裂症诊断生物标志物中的应用:一项系统综述
Front Psychiatry. 2020 Jun 23;11:588. doi: 10.3389/fpsyt.2020.00588. eCollection 2020.