ABI Innovation: A New Automated Data Integration, Annotations, and Interaction Network Inference System for Analyzing Drosophila Gene Expression

ABI Innovation：用于分析果蝇基因表达的新型自动化数据集成、注释和交互网络推理系统

• 批准号: 1356628
• 负责人: Heng Huang
• 金额: $ 61.04万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1356628&HistoricalAwards=false
• 关键词: ABI; Innovation; New; Automated; Data

Large-scale in situ hybridization (ISH) screens are providing an abundance of data showing spatio-temporal patterns of gene expression that are valuable for understanding the mechanisms of gene regulation. Knowledge gained from analysis of Drosophila expression patterns is widely important, because a large number of genes involved in fruit fly development are commonly found in humans and other species. Thus, research efforts into the spatial and temporal characteristics of Drosophila gene expression images have been at the leading-edge of scientific investigations into the fundamental principles of different species development. Drosophila gene expression pattern images enable the integration of spatial expression patterns with other genomic datasets that link regulator with their downstream targets. This project addresses the computational challenges in analyzing Drosophila gene expression patterns by leveraging a new bioinformatics software system. It focuses on designing principled bioinformatics and computational biology algorithms and tools that will integrate multi-modal spatial patterns of gene expression for Drosophila embryos' developmental stage recognition and anatomical ontology term annotation, and will infer gene interaction networks to generate a more comprehensive picture of gene function and interaction. The bioinformatics methods resulting from the project activities are broadly applicable to a variety of fields such as biomedical science and engineering, systems biology, clinical pathology, oncology, and pharmaceutics. Novel tools to enhance courses and research experiences for diverse populations of students are planned to broaden participation in science. This project investigates three challenging problems for studying the Drosophila embryo ISH Images via innovative bioinformatics algorithms: 1) the sparse multi-dimensional feature learning method to integrate the multimodal spatial gene expression patterns for annotating Drosophila ISH images, 2) the heterogeneous multi-task learning models using the high-order relational graph to jointly recognize the developmental stages and annotate anatomical ontology terms, 3) the embedded sparse representation algorithm to infer the gene interaction network. It is innovative to apply structured sparse learning, multi-task learning, and high-order relational graph models to Drosophila gene expression patterns analysis and holds great promise for scientific investigations into the fundamental principles of animal development. The algorithms and tools as outcomes of this research are expected to help knowledge discovery for applications in broader scientific and biological domains with massive high-dimensional and heterogeneous data sets. This project facilitates the development of novel educational tools to enhance several current courses at University of Texas at Arlington. The PIs engage minority students and under-served populations in research activities to provide opportunities for exposure to cutting-edge scientific research. For further information see the web site at: http://ranger.uta.edu/~heng/NSF-DBI-1356628.html

大规模原位杂交（ISH）筛选提供了丰富的数据，显示了基因表达的时空模式，这对理解基因调控机制有价值。从果蝇表达模式的分析中获得的知识非常重要，因为与果蝇发育有关的大量基因在人类和其他物种中普遍存在。因此，对果蝇基因表达图像时空特征的研究一直处于不同物种发育基本原理科学研究的前沿。果蝇基因表达模式图像能够将空间表达模式与其他基因组数据集整合起来，将调节因子与其下游靶点联系起来。该项目通过利用新的生物信息学软件系统解决了分析果蝇基因表达模式的计算挑战。其重点是设计有原则的生物信息学和计算生物学算法和工具，这些算法和工具将整合基因表达的多模态空间模式，用于果蝇胚胎发育阶段识别和解剖本体术语注释，并将推断基因相互作用网络，以产生更全面的基因功能和相互作用图像。项目活动产生的生物信息学方法广泛适用于各种领域，如生物医学科学与工程、系统生物学、临床病理学、肿瘤学和药剂学。计划采用新颖的工具来提高不同学生群体的课程和研究经验，以扩大对科学的参与。本项目探讨了通过创新的生物信息学算法研究果蝇胚胎ISH图像的三个具有挑战性的问题：1)采用稀疏多维特征学习方法整合多模态空间基因表达模式进行果蝇ISH图像标注；2)采用高阶关系图的异构多任务学习模型联合识别发育阶段和标注解剖本体术语；3)采用嵌入式稀疏表示算法推断基因交互网络。将结构化稀疏学习、多任务学习和高阶关系图模型应用于果蝇基因表达模式分析是一种创新，对动物发育基本原理的科学研究具有很大的前景。作为本研究成果的算法和工具有望帮助在更广泛的科学和生物领域应用大量高维和异构数据集的知识发现。该项目促进了新型教育工具的开发，以加强德克萨斯大学阿灵顿分校目前的几门课程。pi让少数民族学生和服务不足的人群参与研究活动，为接触尖端科学研究提供机会。欲了解更多信息，请访问网站：http://ranger.uta.edu/~heng/NSF-DBI-1356628.html

项目成果

Semi-Supervised Classifications via Elastic and Robust Embedding

• DOI: 10.1609/aaai.v31i1.10946
• 发表时间: 2017-02
• 作者: Yun Liu;Yiming Guo;Hua Wang;F. Nie;Heng Huang

Asynchronous Mini-Batch Gradient Descent with Variance Reduction for Non-Convex Optimization

• DOI: 10.1609/aaai.v31i1.10940
• 发表时间: 2017-02
• 作者: Zhouyuan Huo;Heng Huang

Theoretic Analysis and Extremely Easy Algorithms for Domain Adaptive Feature Learning

• DOI: 10.24963/ijcai.2017/272
• 发表时间: 2015-09
• 作者: Wenhao Jiang;Cheng Deng;W. Liu;F. Nie;K. F. Chung;Heng Huang

Learning Task Relational Structure for Multi-task Feature Learning

• DOI: 10.1109/icdm.2016.0166
• 发表时间: 2016-12
• 期刊: 2016 IEEE 16th International Conference on Data Mining (ICDM)
• 作者: De Wang;F. Nie;Heng Huang

Joint Capped Norms Minimization for Robust Matrix Recovery

鲁棒矩阵恢复的联合上限范数最小化

• DOI: 10.24963/ijcai.2017/356
• 发表时间: 2017
• 期刊: The 26th International Joint Conference on Artificial Intelligence (IJCAI 2017
• 作者: Nie, Feiping;Huo, Zhouyuan;Huang, Heng
• 通讯作者: Huang, Heng