Deep learning for prioritizing small molecules candidates for drug repositioning
深度学习优先考虑小分子候选药物的重新定位
基本信息
- 批准号:543968-2019
- 负责人:
- 金额:$ 1.82万
- 依托单位:
- 依托单位国家:加拿大
- 项目类别:Engage Grants Program
- 财政年份:2019
- 资助国家:加拿大
- 起止时间:2019-01-01 至 2020-12-31
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Sightline Innovation is a company that provides artificial intelligence products and solutions for businesses around the world. One of the areas that the company is interested in uses its deep learning cloud services to develop custom applications of the existing drugs for new therapeutic purposes, also called drug repositioning, in cancer that can be easily adjusted to specific client's requirements. Currently, the company has access to a large amount of pharmacogenomic data and drug compound structures for various drug repositioning tasks from different clients. The company is interested in exploiting these data to develop deep learning based solutions for drug repositioning systems that can be quickly adapted to specific clients. In the proposed research, we will accomplish this goal by unifying the structures of small molecules and pharmacogenomic data with neural networks of various architectures . Dr. Hu is a leader in developing deep learning models for drug repositioning. The Hu lab has more than 10 graduate student trainees and research associates with background in computer science, statistics, medical genetics, medicine and others. Sightline Innovation provides cutting-edge artificial intelligence solutions to multiple industries, including pharmaceutical industry. The company is constantly exploring new ways to enhance their products with advanced intelligence and analytic capabilities. The technology solutions developed in this project will provide the partner company with some unique product and market advantage, which may improve the lives of Canadians..
Sightline Innovation是一家为全球企业提供人工智能产品和解决方案的公司。该公司感兴趣的领域之一是使用其深度学习云服务开发现有药物的定制应用程序,用于癌症的新治疗目的,也称为药物重新定位,可以根据特定客户的要求进行轻松调整。目前,该公司可以从不同的客户那里获得大量的药物基因组学数据和药物化合物结构,用于各种药物重新定位任务。该公司有兴趣利用这些数据开发基于深度学习的解决方案,用于药物重新定位系统,可以快速适应特定客户。在拟议的研究中,我们将通过将小分子和药物基因组学数据的结构与各种架构的神经网络统一起来来实现这一目标。 胡博士是开发用于药物重新定位的深度学习模型的领导者。Hu实验室有10多名研究生实习生和研究助理,具有计算机科学,统计学,医学遗传学,医学等背景。Sightline Innovation为包括制药行业在内的多个行业提供尖端的人工智能解决方案。该公司正在不断探索新的方法,以增强其产品的先进智能和分析能力。该项目开发的技术解决方案将为合作伙伴公司提供一些独特的产品和市场优势,这可能会改善加拿大人的生活。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Hu, Pingzhao其他文献
Data integration in genetics and genomics: methods and challenges.
- DOI:
10.4061/2009/869093 - 发表时间:
2009-01-12 - 期刊:
- 影响因子:0
- 作者:
Hamid, Jemila S;Hu, Pingzhao;Beyene, Joseph - 通讯作者:
Beyene, Joseph
YOLO-LOGO: A transformer-based YOLO segmentation model for breast mass detection and segmentation in digital mammograms
- DOI:
10.1016/j.cmpb.2022.106903 - 发表时间:
2022-05-26 - 期刊:
- 影响因子:6.1
- 作者:
Su, Yongye;Liu, Qian;Hu, Pingzhao - 通讯作者:
Hu, Pingzhao
Bioinformatics driven discovery of small molecule compounds that modulate the FOXM1 and PPARA pathway activities in breast cancer.
- DOI:
10.1038/s41397-022-00297-1 - 发表时间:
2023-07 - 期刊:
- 影响因子:2.8
- 作者:
Huang, Shujun;Hu, Pingzhao;Lakowski, Ted M. - 通讯作者:
Lakowski, Ted M.
DTF: Deep Tensor Factorization for predicting anticancer drug synergy
- DOI:
10.1093/bioinformatics/btaa287 - 发表时间:
2020-08-15 - 期刊:
- 影响因子:5.8
- 作者:
Sun, Zexuan;Huang, Shujun;Hu, Pingzhao - 通讯作者:
Hu, Pingzhao
Automated Counting of Cancer Cells by Ensembling Deep Features
- DOI:
10.3390/cells8091019 - 发表时间:
2019-09-01 - 期刊:
- 影响因子:6
- 作者:
Liu, Qian;Junker, Anna;Hu, Pingzhao - 通讯作者:
Hu, Pingzhao
Hu, Pingzhao的其他文献
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{{ truncateString('Hu, Pingzhao', 18)}}的其他基金
Flexible and robust deep learning models for integrative analysis of single-cell RNA sequencing data
灵活而强大的深度学习模型,用于单细胞 RNA 测序数据的综合分析
- 批准号:
RGPIN-2021-04072 - 财政年份:2022
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Flexible and robust deep learning models for integrative analysis of single-cell RNA sequencing data
灵活而强大的深度学习模型,用于单细胞 RNA 测序数据的综合分析
- 批准号:
RGPIN-2021-04072 - 财政年份:2021
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Developing novel machine learning algorithms for network biology
为网络生物学开发新颖的机器学习算法
- 批准号:
RGPIN-2015-06751 - 财政年份:2020
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Developing novel machine learning algorithms for network biology
为网络生物学开发新颖的机器学习算法
- 批准号:
RGPIN-2015-06751 - 财政年份:2019
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Developing novel machine learning algorithms for network biology
为网络生物学开发新颖的机器学习算法
- 批准号:
RGPIN-2015-06751 - 财政年份:2018
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Developing novel machine learning algorithms for network biology
为网络生物学开发新颖的机器学习算法
- 批准号:
RGPIN-2015-06751 - 财政年份:2017
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Developing novel machine learning algorithms for network biology
为网络生物学开发新颖的机器学习算法
- 批准号:
RGPIN-2015-06751 - 财政年份:2016
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
Developing novel machine learning algorithms for network biology
为网络生物学开发新颖的机器学习算法
- 批准号:
RGPIN-2015-06751 - 财政年份:2015
- 资助金额:
$ 1.82万 - 项目类别:
Discovery Grants Program - Individual
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