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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/文摘

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.