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.

项目摘要/摘要申请人的目标是发展必要的技能，成为独立翻译的生物医学计算药物重新利用领域的信息研究人员。探索新型的药品目标相互作用（DTI）在药物开发中起着至关重要的作用。为了降低整体成本并发现更多的潜力筛选目标，计算方法（在计算机中）已经流行，通常应用于多药理学和药物重新利用。尽管基于机器学习的策略已经研究了多年来，没有标准化的基准提供大规模培训数据集以及潜水员评估任务测试不同的方法。此外，现有方法遭受了显着的局限性，其中1）由于缺乏负样本而经常偏向结果，2）新型药物目标与新的药物关联（或孤立的）药物/靶标不能探索 3）全面的拓扑结构不能是通过特征学习方法捕获。因此，在大数据时代，适用的提案要解决实现两个目标来面临挑战。 •AIM 1（K99阶段）：开发一个大规模的基准，用于评估基于药物目标的预测从异质生物医学数据集生成多方网络。 •目标2（R00阶段）：调整深度学习模型以基于新颖的方式构建准确的预测模型特征学习算法，该算法挖掘了多维生物医学网络（多部分网络）。在修改阶段，申请人将整合异质的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目标预测。申请人将接受培训在数据集成，工具和数据管理技能的标准化工具中，评估方法药品目标预测以及最先进的机器学习/计算机AID中的深度学习方法药理。互补的教学，智力和专业培训将有助于申请人为 R00阶段他将开发一个基于深度学习的预测模型和多维图嵌入特征学习的方法。这些新颖的研究将共同推进当前的计算通过提供1）进行测试和评估的全面基准测试以及2）可扩展的药物重新定位以及基于生物医学多目标网络的准确预测模型。申请人将由高级，成熟的研究人员在语义网，计算生物学，癌症方面具有丰富的专业知识基因组学，药物开发和机器学习/深度学习。重要的是，该项目将提供申请人建立独立研究计划的基础 1）计算药物重新利用实际情况，2）研究潜水员在系统生物学中隐藏的关联（例如，药物，遗传学和疾病）和3）精确药物的瞄准应用利用生物医学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.