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

项目摘要/摘要 申请者的目标是发展成为一名独立的翻译生物医学所需的技能 计算机药物再利用领域的信息学研究员。探索新的药物-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，挖掘更多潜力 筛选目标，计算方法(在电子计算机中)已变得流行，并通常应用于 多元药理学与药物再利用。尽管基于机器学习的策略已经研究了 多年来，没有一个标准化的基准来提供大规模和多样化的培训数据集 评估任务测试不同的方法。此外，现有的方法存在显著的局限性， 其中1)结果通常由于缺乏阴性样本而有偏差，2)新的药物靶标与新的 (或孤立的)药物/靶点不能被探索， 3)综合的拓扑结构不能 通过特征学习方法捕获 。因此，在大数据时代，申请人提出研究要解决 通过实现两个目标来应对挑战。 ·目标1(K99阶段)：制定评估药物靶标预测的大规模基准 从异质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，根据新的 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，申请者将整合不同种类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靶点预测。申请者将接受培训 在数据集成的标准化工具、数据管理的工具和技能中，评估方法 计算机辅助的药物靶点预测和最新的机器学习/深度学习方法 药理学。辅助性的教学、智力和专业培训将帮助申请人做好准备 R00阶段，他将在该阶段开发基于深度学习的预测模型和多维图表 用于特征学习的嵌入方法。总而言之，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 以及基于生物医学多方网络的精确预测模型。 申请者将由以下人员指导 资深的资深研究人员，在语义网、计算生物学、癌症方面拥有丰富的专业知识 基因组学、药物开发和机器学习/深度学习。 重要的是，这个项目将提供一个 申请人设立独立研究项目的基金会 1)计算药物再利用 真实案例，2)系统生物学中各种隐藏的关联的调查(例如， 药物、遗传学和疾病)，以及3)针对利用生物医学的应用的精确医学 知识库和电子健康记录。

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.