权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

BOND: Benchmarking based on heterogeneous biOmedical Network and Deep learning novel drug-target associations

BOND：基于异构生物医学网络和深度学习新型药物靶标关联的基准测试

基本信息

批准号：
10227201
负责人：
NANSU ZONG
金额：
$ 0.83万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2021-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10227201
关键词：
Address Algorithms Area Base Ratios Benchmarking Big Data Clinic Computational Biology Computer Assisted Data Set Data Sources Databases Development Dimensions Disease Drug Evaluation Drug Targeting Electronic Health Record Employment Evaluation Foundations Generations Genetic Goals Graph Investigation Learning Learning Module Link Machine Learning Mentors Methodology Methods Mining Modeling Molecular Network-based Pharmaceutical Preparations Pharmacology Phase Play Positioning Attribute Research Research Personnel Role Sampling Silver Source Standardization Structure Systems Biology Technology Testing Training Ursidae Family Validation base biomedical informatics cancer genomics computer based Semantic Analysis cost data integration data management data tools deep learning deep neural network design drug development drug repurposing flexibility heterogenous data improved in silico knowledge base large scale data learning algorithm learning strategy machine learning algorithm new therapeutic target novel precision medicine predictive modeling predictive test programs repository screening skills tool

项目摘要

Project Summary/Abstract The applicant’s goals are to develop the necessary skills to become an independent translational biomedical informatics researcher in the area of computational drug repurposing. Exploring novel drug-target interactions (DTI) plays a crucial role in drug development. In order to lower the overall costs and uncover more potential screening targets, computational (in silico) methods have become popular and are commonly applied to poly-pharmacology and drug repurposing. Although machine learning-based strategies have been studied for years, there is no standardized benchmark that provides large-scale training datasets as well as diverse evaluation tasks to test different methods. Furthermore, the existing methods suffer from remarkable limitations, where 1) results are often biased due to a lack of negative samples, 2) novel drug-target associations with new (or isolated) drugs/targets cannot be explored, and 3) the comprehensive topological structure cannot be captured by feature learning methods . Therefore, in the era of big data, the applicant proposes a study to tackle the challenges by achieving two aims. • Aim 1 (K99 Phase): Develop a large scale benchmark for evaluating drug-target prediction based on the generation of a multipartite network from heterogeneous biomedical datasets. • Aim 2 (R00 Phase): Adapt a deep learning model to build an accurate predictive model based on a novel feature learning algorithm that mines the multi-dimensional biomedical network (multipartite network). In the mentored phase, the applicant will integrate heterogeneous biomedical datasets and build a benchmark for evaluation of the drug-target prediction based on well-designed strategies. The applicant will receive training in standardization tools for data integration, tools, and skills for data management, evaluation methods for drug-target predictions, and state-of-the-art machine learning/deep learning methods in computer-aided pharmacology. Complementary didactic, intellectual, and professional training will help prepare the applicant for the R00 phase where he will develop a deep learning-based predictive model and multi-dimensional graph embedding methods for feature learning. Together, these novel studies will advance the current computational drug repurposing by providing 1) comprehensive benchmarking for testing and evaluation, and 2) a scalable and accurate predictive model based on a biomedical multi-partite network. The applicant will be mentored by senior, established investigators with substantial expertise in Semantic Web, computational biology, cancer genomics, drug development, and machine learning/deep learning. Importantly, this project will provide a foundation for the applicant to establish independent research programs in 1) computational drug repurposing in real cases, 2) investigation of the diverse hidden associations in system biology (e.g., associations between drugs, genetics, and diseases), and 3) precision medicine aimed applications leveraging biomedical knowledgebases and electronic health records.

项目总结/摘要申请人的目标是发展必要的技能，成为独立的翻译生物医学计算机药物再利用领域的信息学研究员。探索新型药物-靶标相互作用 (DTI)在药物开发中起着至关重要的作用。为了降低总成本，发掘更多潜力，筛选目标，计算（计算机模拟）方法已变得流行，并通常应用于多药理学和药物再利用。虽然基于机器学习的策略已经被研究了多年来，没有一个标准化的基准可以提供大规模的训练数据集以及多样化的评估任务，以测试不同的方法。此外，现有的方法具有显著的局限性，其中，1）由于缺乏阴性样本，结果往往存在偏倚，2）新的药物靶点与新的 (or分离的）药物/靶不能被探索， 3）综合拓扑结构不能通过特征学习方法捕获 .因此，在大数据时代，申请人提出了一项研究，通过实现两个目标来应对挑战。目标1（K99阶段）：开发一个大规模的基准，用于评估药物靶点预测，从异构生物医学数据集生成多部分网络。 ·目标2（R 00阶段）：调整深度学习模型，基于新的预测模型构建准确的预测模型。挖掘多维生物医学网络（多部网络）的特征学习算法。在辅导阶段，申请人将整合异构生物医学数据集并建立基准用于评估基于精心设计的策略的药物靶点预测。申请人将接受培训在数据集成的标准化工具、数据管理的工具和技能、药物靶点预测，以及计算机辅助的最先进的机器学习/深度学习方法药理学补充教学，智力和专业培训将有助于申请人准备在R 00阶段，他将开发基于深度学习的预测模型和多维图特征学习的嵌入方法。总之，这些新的研究将推动目前的计算通过提供1）全面的测试和评估基准，以及2）可扩展的和基于生物医学多体网络的精确预测模型。申请人将接受以下人员的指导：在语义网、计算生物学、癌症等领域拥有丰富专业知识的资深研究人员基因组学、药物开发和机器学习/深度学习。重要的是，该项目将提供一个申请人建立独立研究计划的基础， 1)计算药物再利用真实的案例，2）系统生物学中各种隐藏关联的调查（例如，之间的关联药物，遗传学和疾病），以及3）精准医学，旨在利用生物医学的应用知识库和电子健康记录。

项目成果

期刊论文数量（4）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Deep Denoising of Raw Biomedical Knowledge Graph From COVID-19 Literature, LitCovid, and Pubtator: Framework Development and Validation.

DOI：
10.2196/38584
发表时间：
2022-07-06
期刊：
JOURNAL OF MEDICAL INTERNET RESEARCH
影响因子：
7.4
作者：
Jiang, Chao;Ngo, Victoria;Chapman, Richard;Yu, Yue;Liu, Hongfang;Jiang, Guoqian;Zong, Nansu
通讯作者：
Zong, Nansu

Computational drug repurposing based on electronic health records: a scoping review.