Computational genomics and psychiatry
in Biomedical Informatics Lab


主持或参加科研项目（课题）：

在研
1) 合作主持，上海交通大学，医工交叉重点项目，“早发型卵巢功能不全（POI）预测模型建立及精准诊疗转化应用”，35万，2023.01-2025.12，在研（项目编号23X010302269） 英文标识为“Medical-Engineering Cross Foundation of Shanghai Jiao Tong University“
2) 项目骨干，上海交通大学，STAR计划项目，“帕金森病平衡障碍的智能康复策略及相关机制研究”，40万，2023.01-2025.12 （项目编号20220103）
3) 项目骨干，科技创新2030—“脑科学与类脑研究”重大项目，“儿童青少年情绪问题分级与干预”，118万，2022.05-2027.05. 中文标识为“科技创新2030（项目编号2022ZD0209101）”， 英文标识为“STI 2030—Major Projects（No：2022ZD0209101）”，在研
4) 合作主持，上海交通大学，医工交叉重点项目，“基于多组学特征的青少年神经性厌食症的精准识别和早期分型研究”， 30万，2022.01-2024.12，在研（项目编号YG2022ZD026）， 英文标识为“Medical-Engineering Cross Foundation of Shanghai Jiao Tong University“
5) 主持，国家自然科学基金外国学者（优秀青年）研究基金，“双相障碍早期诊断的多组学深度学习预测模型构建研究”(No: 82150610506)，2022.01-2023.12，在研
6) 主持，上海市自然科学基金，面上项目，“精神病高危综合征临床转归的深度学习预测模型构建研究”(No: 21ZR1428600)，2021.07-2024.06，在研
7) 主持，国家自然科学基金，面上项目，“基于多态-多元-时空网络对强迫症遗传模式的集成研究”(No: 81971292)，2020.01-2023.12，在研
8) 主持，上海市精神卫生中心技术开发课题，“精神疾病药物疗效智能预测及评估云平台建设”(No: 21H010100316)，2021.01-2022.12，在研

已结题
1) 主持，国家自然科学基金，面上项目，“基于脑基因异构体网络对精神分裂症突变的功能研究”(No: 81671328)，2017.01-2020.12，已结题
2) 主持，科技部国家重点研发计划，精准医学研究专项子课题，“精神分裂症和双相障碍分子-脑网络多模态生物标志物特征谱及诊疗规范研究”(No: 017YFC0909202)，2017.07-2019.12，已结题
3) 合作主持，上海交通大学，医工交叉青年项目，“非编码RNA网络调控ROBO1泛素化降解在视网膜新生血管性疾病中的作用”(No: YG2019QNA59)， 2020.01-2022.12，已结题
4) 主持，上海市重性精神病重点实验室，开放课题，“强迫症染色体拷贝数变异的时空特异性表达及其致病机制分析”（No: 20Z111240001），2019.07-2021.12，已结题
5) 主持，上海交通大学生物医学工程学院基础研究创新培育计划（上海市高峰高原学科建设专项支持），“基于强迫症核心家系全基因组的致病研究”(No: ZXWF082101/033)，2018.01-2019.12，已结题
6) 主持，上海市瑞金医院技术开发课题，“膝关节软骨组织基因数据处理分析”(No: 21H010101387)，2021.05-2022.12，已结题
7) 主持，上海市第九人民医院技术开发课题，“人工智能辅助人工髋关节置换手术临床数据库建模与分析服务”(No: 20H30000036)，2019.01-2020.12，已结题


Funding Supports

1) PI, Research Fund for International Excellent Young Scientists（No:82150610506）, 2022-2023
1) PI, General Program of Shanghai Natural Science Foundation, “A deep learning model for symptom evaluation and prognosis prediction of clinical high-risk psychosis” (No: 21ZR1428600), ¥200,000, 2021.07-2024.06, in progress
2) PI, General Program of National Natural Science Foundation of China (NSFC), “Integrative spatio-temporal network study on the genetic architecture of Obsessive-Compulsive Disorder” (No: 81971292), ¥ 550,000, 2020.01-2023.12, in progress
3) PI, General Program of National Natural Science Foundation of China (NSFC), “Evaluating functional effects of de novo mutations on isoform networks in Schizophrenia” (No: 81671328), ¥ 570,000, 2017.01-2020.12, completed
4) PI, National Key R & D Program of China, Precision Medicine Research, "Characterizing multimodal molecular biomarkers for diagnosis and treatment of Schizophrenia and Bipolar Disorder" (No: 2017YFC0909202), ¥ 3,600,000, 2017.07-2019.12, completed
5) Co-PI, grant from the Interdisciplinary Program of Shanghai Jiao Tong University, “The role of non-coding RNA network in regulating ROBO1 ubiquitination degradation in neovascular retinal disease” (No: YG2019QNA59), ¥ 200,000, 2020.01-2022.12, in progress
6) PI, Shanghai Key Laboratory of Psychotic Disorders Open Grant, " Characterizing the spatial and temporal expression patterns and potential pathogenic mechanisms of copy number variations in Obsessive-Compulsive Disorder" (No: 13dz2260500), ¥ 50,000, 2019.07-2021.12, in progress
7) PI, Grant from School of Biomedical Engineering Basic Research Innovation Cultivation Program, supported by Innovation Research Plan of Shanghai Municipal Education Commission, “Genome-wide de novo mutation study on trio-based families in Obsessive-Compulsive Disorder” (No: ZXWF082101), ¥ 300,000, 2018.01-2019.12, completed
8) PI, Grant from Shanghai Ruijin Hospital, "Pipeline development of processing and analyzing genetic data of knee cartilage tissues" (No: 21H010101387), ¥ 98,000, 2021.05-2022.12, in progress
9) PI, Grant from Shanghai Mental Health Center, "Construction of a cloud-based platform for prediction and evaluation of the efficacy of neuropsychiatric drugs" (No: 21H010100316), ¥ 255,000, 2021.01-2022.12, in progress
10) PI, Grant from Shanghai Ninth People’s Hospital, "Modeling and analysis of clinical data from AI-assisted artificial hip replacement surgery " (No: 20H30000036), ¥ 60,000, 2019.01-2020.12, completed

基于强迫症核心家系全基因组的致病突变研究:

在这个项目里，我们对一群患有早期强迫症的患者和他们无强迫症症状的父母 （样本由医学院附属精神卫生中心采集）进行核心家系Trio全基因组测序。 对所捕获的突变大数据组进行有效生信统计，对比分析检测新生突变，再结合多组学多维度数据， 利用机器学习方法去构建一个预测强迫症致病因子的计算模型。 我们将通过与患者的临床表型关联，对预测出的高风险因子分析其和脑功能通路的关联以及它们的致病原理。 我们希望通过本项目的研究能识别出一组影响强迫症患者脑功能的风险基因和致病性突变.

WGS study on the genetics of Obsessive-Compulsive Disorder (OCD):

In this project, we are performing the whole-genome sequencing (WGS) of a cohort of patients diagnosed with early-onset Obsessive-Compulsive Disorder (OCD) together with their unaffected parents (collected by Shanghai Mental Health Center), where we hope to identify de novo mutations and risk genes that involve in the etiology of the disease. We hope that research in this area will eventually help to identify the pathogenic mutations and disease pathways, and to further refine the categorization of disease pathology, so that therapies can be more targeted and effective.


综合基因网络对精神分裂症(SCZ)和双向情感障碍(BP)的共病研究:

精神分裂症主要为精神障碍；躁郁症主要为的情绪障碍，但也可能涉及精神病。但是，由于两者有着一些类似的临床症状，区分两者有时会很困难。 而事实上，确实存在中度分裂情感性精神障碍，所以此研究重在研究两者的共病机制。而常见的多效机制有可能就是SCZ和BP共病的基础。 我们将根据构建共病功能通路和基因网络来确定SCZ和BP之间的“遗传重叠”的程度。 可帮助深入了解这两种在基因上相互相关，临床表型有重叠，但仍然是不同的神经发育障碍的内在病理机制。我们将使用生物信息学的方法来 构建综合遗传网络，包括新生突变数据，高风险CNV，microRNA数据，动物模型和基因表达图谱。我们旨在预测与SCZ 和BP遗传相关的神经发育障碍的遗传通路和遗传网络，并辨别常见的多效性遗传可作为药物靶标的风险变体。

Integrated genetic network study of comorbidity between Schizophrenia(SCZ) and Bipolar(BP)

Common pleiotropic mechanisms may underlie shared repetitive symptoms across SCZ and BP. In this project, we will determine the extent of "genetic overlap" in terms of shared underlying gene pathways and gene networks between SCZ and BP, to provide further insights into how aberrant processes underlie these genetically related, clinically overlapping but still distinct neurodevelopmental disorders. We will use a combination of bioinformatics and literature approaches to construct integrated genetic networks in-corroborating genetic evidence including de novo mutation data and recurrent high risk ‘genome-wide’ CNVs, microRNA data, animal models and gene expression studies. We aim to Detect and integrate common genetic pathways and genetic networks for neurodevelopmental disorders that are genetically related to SCZ and/or BP, and to identify common pleiotropic genetic risk variants as possible druggable targets.

建立中国人群SCZ、BP分子-影像生物标志物图谱数据库，搭建多组学、脑影像精准分析云平台:

在这个项目里，我们将搭建一个中央化的中国人群SCZ、BP分子-影像生物标志物大数据存储和备份平台，以及大数据分析平台。 实现对海量数据的统一存储、分析与共享。这个云平台将在科研和临床上实现以下两大目标：在科研上提供中国人群SCZ和BP图谱数据库的共享。 科研用户可以用简单的网络方式登录并访问我们课题整合或新发现的SCZ、BP的影像学、生物标志物等图谱数据库，并提供原始数据的下载； 在临床上，根据病人多模态信息提供一个精准诊疗方案。临床终端用户可以用简单的网络方式登录并上传我们所认可的病人的多模态图谱数据， 经过不依赖终端用户的远程云端数据处理，返回一个精准诊疗方案。

Build a -OMIC knowledge base of SCZ and BP Chinese cohort

In this National Key Research and Development Program of China supported project, we are building a cloud knowledge-base of -omic data and brain MRI images of SCZ and BP patient cohorts. It will be a centralized big data analysis platform and big data storage and backup platform as the unified storage, analysis and sharing of massive data.


条件性敲除自闭症风险基因小鼠模型的研究:

虽然在识别自闭症和其他神经发育障碍的遗传原因，以及这些疾病潜在分子机制认识方面，目前已有了很大的进展。 然而，我们在被遗传突变所破坏的神经分子通路，以及能否有个性化的精准治疗上，认识仍然相对有限。 因此携带着人类基因突变的小鼠模型是作为能提高我们对这些神经分子通路认知的重要工具。在这些项目中，我们将通过构建条件性敲自闭症风险基因的小鼠模型 来研究疾病病因并试图了解其脑功能分子通路径。我们将利用这些小鼠模型来研究基因突变对神经细胞，不同脑区的转录和翻译水平的影响。 我们的目标是揭开与疾病表型相关的细胞和分子机制。

Autism genes conditional knockout mouse model studies

The progress in identifying genetic causes of autism and other neurodevelopmental disorders has opened new possibilities for improving our understanding of the molecular mechanisms underlying these diseases. However, the knowledge of the molecular pathways that are disrupted by genetic mutations, and of those that could be targeted therapeutically, remains quite limited. Mouse models that carry human genetic mutations serve as important tools for improving the knowledge about these pathways. In these projects, we are making conditional autism gene knockout mouse models to study the disease etiology and try to understand the underlying molecular pathways.We are investigating the impact of these mutations at the cellular, transcriptional and translational levels using developing mouse brain. Our goal is to unravel detailed cellular and molecular mechanisms responsible for the disease phenotypes.


其它科研项目还包括基因组学方面的数据挖掘（机器学习）的转化医学研究，我们不间断的和校内外合作者开拓新的合作项目。

Other areas of interest include machine learning in the context of genomics and translational genetics. We are constantly forming on and off-campus collaborations to further these and other areas of interest.