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

• 批准号: 10054989
• 负责人: NANSU ZONG
• 金额: $ 10万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10054989
• 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; 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

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.

项目概要/摘要 申请人的目标是培养成为独立转化生物医学人员所需的技能 计算药物再利用领域的信息学研究员。探索新型药物-靶点相互作用 （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挖掘更多潜力 筛选目标时，计算（计算机）方法已变得流行并通常应用于 多药理学和药物再利用。尽管基于机器学习的策略已经被研究 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数据集 评估任务来测试不同的方法。此外，现有方法存在明显的局限性， 其中 1) 由于缺乏阴性样本，结果常常存在偏差，2) 新的药物靶点与新药物的关联 （或孤立的）药物/靶点无法探索， 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，申请人提出一项研究来解决 通过实现两个目标来应对挑战。 • 目标 1（K99 阶段）：开发一个大规模基准，用于基于 从异构生物医学数据集生成多方网络。 • 目标 2（R00 阶段）：采用深度学习模型，基于新颖的模型构建准确的预测模型 挖掘多维生物医学网络（多部分网络）的特征学习算法。 在指导阶段，申请人将整合异构生物医学数据集并建立基准 基于精心设计的策略评估药物靶点预测。申请人将接受培训 数据集成的标准化工具、数据管理的工具和技能、评估方法 药物靶标预测以及计算机辅助中最先进的机器学习/深度学习方法 药理。补充性的教学、智力和专业培训将帮助申请人做好准备 R00阶段，他将开发基于深度学习的预测模型和多维图 用于特征学习的嵌入方法。总之，这些新颖的研究将推动当前的计算 通过提供 1) 全面的测试和评估基准，以及 2) 可扩展的药物再利用 基于生物医学多方网络的准确预测模型。 申请人将接受以下人士的指导 在语义网、计算生物学、癌症方面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 为申请人建立独立研究项目奠定基础 1）计算药物再利用 真实案例，2）系统生物学中各种隐藏关联的调查（例如， 药物、遗传学和疾病），以及3）利用生物医学的精准医学目标应用 知识库和电子健康记录。