Repurpose open data to discover therapeutics for understudied diseases

重新利用开放数据来发现尚未研究的疾病的治疗方法

基本信息

  • 批准号:
    10231115
  • 负责人:
  • 金额:
    $ 41.63万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-09-01 至 2024-07-31
  • 项目状态:
    已结题

项目摘要

Project Summary/Abstract Many diseases are understudied because they are rare or of little public interest. The effect of each understudied disease may be limited, but the cumulative effects of all these diseases could be profound. One common research challenge for these diseases is that the resources allocated to each is often limited. For instance, large- scale screening of drugs is often challenging, if not possible, in small labs. The decreasing costs of next generation sequencing make possible the generation of gene expression profiles of understudied disease samples. Integrating these expression profiles with other open data provides tremendous opportunities to gain insights into disease mechanisms and identify new therapeutics for understudied diseases. We have utilized a systems-based approach that employs gene expression profiles of disease samples and drug-induced gene expression profiles from cancer cell lines to predict new therapeutic candidates for hepatocellular carcinoma, Ewing sarcoma and basal cell carcinoma. All these candidates were successfully validated in preclinical models. The success of this approach relies on multiscale procedures, such as quality control of disease samples, selection of appropriate reference tissues, evaluation of disease signatures, and weighting cell lines. There is a plethora of relevant datasets and analysis modules that are publicly available, yet are isolated in distinct silos, making it tedious to implement this approach in translational research. A centralized informatics system that allows prediction of therapeutics for further experimental validation is thus of great interest to researchers working on understudied diseases. Accordingly, we propose four specific aims: 1) developing novel deep learning methods to select precise reference normal tissues for disease signature creation, 2) developing computational methods to reuse drug profiles from other disease models for drug prediction, 3) integrating open efficacy data to identify new targets from the systems-based approach, and 4) developing a centralized platform and promoting the platform in the scientific community. This proposal will reuse several big open databases (e.g., TCGA, TARGET, GTEx, GEO, LINCS, CTRP, GDSC) and employ cutting-edge informatics methods (e.g., deep learning). To demonstrate the scalability of the system, we will investigate three representative understudied diseases: multiple organ dysfunction syndrome (Aim 1), diffuse intrinsic pontine glioma (Aim 2) and hepatocellular carcinoma (Aim 3). Successful implementation of the systems-based approach can be used as a model for using other large open omics (proteins, metabolites) to discover therapeutics for diseases with unmet needs. This proposal will bring together experts in informatics, statistics, computer science, and physicians from Michigan State University, Stanford University, UC Berkeley and Spectrum Health. All data and code will be released to the public for continuing development. The system will be deployed to our OCTAD portal (http://octad.org), an open workplace for therapeutic discovery.
项目总结/文摘

项目成果

期刊论文数量(0)
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会议论文数量(0)
专利数量(0)

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Bin Chen其他文献

Bin Chen的其他文献

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{{ truncateString('Bin Chen', 18)}}的其他基金

virtual compound screening using gene expression
使用基因表达进行虚拟化合物筛选
  • 批准号:
    10418186
  • 财政年份:
    2022
  • 资助金额:
    $ 41.63万
  • 项目类别:
virtual compound screening using gene expression
使用基因表达进行虚拟化合物筛选
  • 批准号:
    10673837
  • 财政年份:
    2022
  • 资助金额:
    $ 41.63万
  • 项目类别:
Equipment Purchases for R01GM145700
R01GM145700 的设备采购
  • 批准号:
    10795418
  • 财政年份:
    2022
  • 资助金额:
    $ 41.63万
  • 项目类别:
A postdoctoral training program for impactful careers in stem cell biology
干细胞生物学领域有影响力的职业博士后培训计划
  • 批准号:
    10592329
  • 财政年份:
    2022
  • 资助金额:
    $ 41.63万
  • 项目类别:
Drug biomarker resources for precise translational research
用于精准转化研究的药物生物标志物资源
  • 批准号:
    10056488
  • 财政年份:
    2020
  • 资助金额:
    $ 41.63万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10461787
  • 财政年份:
    2019
  • 资助金额:
    $ 41.63万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10704561
  • 财政年份:
    2019
  • 资助金额:
    $ 41.63万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10669357
  • 财政年份:
    2019
  • 资助金额:
    $ 41.63万
  • 项目类别:
Repurpose open data to discover therapeutics for understudied diseases
重新利用开放数据来发现尚未研究的疾病的治疗方法
  • 批准号:
    10713005
  • 财政年份:
    2019
  • 资助金额:
    $ 41.63万
  • 项目类别:
Integrating transcriptomic, proteomic and pharmacogenomic data to inform individualized therapy in cancers
整合转录组学、蛋白质组学和药物基因组学数据,为癌症个体化治疗提供信息
  • 批准号:
    9925076
  • 财政年份:
    2018
  • 资助金额:
    $ 41.63万
  • 项目类别:

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