III: Small: Network Learning for Integrative Cancer Genomics

III：小：综合癌症基因组学的网络学习

• 批准号: 1117153
• 负责人: Rui Kuang
• 金额: $ 50万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117153&HistoricalAwards=false
• 关键词: III; Small; Network; Learning; Integrative

New large-scale DNA sequencing and array technologies now provide a promising way to study the molecular mechanisms of cancer by generating enormous information measuring aberrations in cancer genome. The genomic information can potentially guide drug design on targeted molecules, and improve clinical decisions in cancer treatment. One of the main obstacles to further progress is to elucidate multiple complex molecular indicators of cancers from the enormous genomic data. This proposal tackles the problem with network-based machine-learning theoretical frameworks and methods that can model the underlying biological mechanisms for an integrative study of cancer genomic information and relevant biomedical knowledge. As a proof of concept, the developed methods will be applied to study chemoresistance in ovarian cancer treatment. This proposal aims at creating a general computation-driven approach for guiding cancer genomics research and improving genomics-based clinical decisions in cancer treatment. The research activities described in the proposal will deliver a collection of effective and efficient computational tools to utilize heterogeneous genomic data combined with biomedical knowledge for clinical practices. The study of the ovarian cancer data will help reveal the crucial pathways driving chemoresistance, and provide useful prediction tools and drug targets for ovarian cancer treatment. This proposal will also integrate the latest research development in computational cancer genomics into new courses in several training programs to prepare students for their future professions to meet the need of workforce in the growing biomedical and health informatics industry in the upper midwest region. The education plan will also have a focus on recruiting students in minority and under-represented groups in computer science and information technology.To achieve the goals, the components of the research plan are 1) to formulate graph kernels and subgraph mining algorithms that can integrate various types of cancer genome aberrations to improve cancer outcome predictions and to discover cancer-causative genome aberration patterns; 2) to formulate semi-supervised matrix factorization methods with Laplacian constraints for predicting novel cancer phenotype and gene associations for identifying potential drug targets, utilizing known relations in phenotype, gene and their association networks; 3) to study the chemoresistance in ovarian cancer treatment to reveal the crucial pathways driving the resistance, and develop useful prediction tools and drug targets for ovarian cancer treatment; 4) to release the developed methods in both software packages and webtools for public use in academia. The two major components of the education plan are: 1) to offer a two-week course, titled Cure Cancer with Computers, in the summer academy of the BioSMART program for Minnesota high school students, and 2) to create a new course Computational Genomics in Biomedical Informatics to support two graduate programs for training students in biomedical/health informatics with knowledge in genomics and computer science.

新的大规模DNA测序和阵列技术现在提供了一个有前途的方法来研究癌症的分子机制，通过产生大量的信息测量癌症基因组中的畸变。基因组信息可以潜在地指导靶向分子的药物设计，并改善癌症治疗的临床决策。进一步进展的主要障碍之一是从庞大的基因组数据中阐明癌症的多个复杂分子指标。该提案通过基于网络的机器学习理论框架和方法解决了这个问题，这些框架和方法可以为癌症基因组信息和相关生物医学知识的综合研究建立潜在的生物机制模型。作为概念验证，所开发的方法将应用于研究卵巢癌治疗中的化疗耐药性。该提案旨在创建一种通用的计算驱动方法，用于指导癌症基因组学研究，并改善癌症治疗中基于基因组学的临床决策。提案中描述的研究活动将提供一系列有效和高效的计算工具，以利用异质基因组数据结合生物医学知识进行临床实践。对卵巢癌数据的研究将有助于揭示导致化疗耐药的关键途径，并为卵巢癌治疗提供有用的预测工具和药物靶点。这项提案还将把计算癌症基因组学的最新研究进展整合到几个培训项目的新课程中，为学生未来的职业做好准备，以满足中西部地区日益增长的生物医学和健康信息学行业的劳动力需求。该教育计划还将重点招收计算机科学和信息技术领域的少数民族和代表性不足的群体的学生。为实现这一目标，研究计划的组成部分是：1）制定图核和子图挖掘算法，可以整合各种类型的癌症基因组畸变，以改善癌症预后预测和发现致癌基因组畸变模式; 2）利用表型、基因及其关联网络中的已知关系，制定具有拉普拉斯约束的半监督矩阵因子分解方法，用于预测新的癌症表型和基因关联，以识别潜在的药物靶点; 3）研究卵巢癌治疗中的化学抗性以揭示驱动抗性的关键途径，并开发有用的卵巢癌治疗预测工具和药物靶点; 4）以软件包和网络工具的形式发布所开发的方法，供学术界公众使用。教育计划的两个主要组成部分是：1）在明尼苏达州高中生BioSMART项目的暑期学院中提供为期两周的课程，名为“用计算机治愈癌症”，以及2）创建一门新课程生物医学信息学中的计算基因组学，以支持两个研究生项目，为生物医学/健康信息学学生提供基因组学和计算机科学知识的培训。