图 书 馆
整合拓扑从异质组学数据中揭示新生物学Integrative topology uncovers new biology from heterogeneous omics data |
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| 课程网址: | http://videolectures.net/FPSAC2019_przulj_integrative_topology/ |
| 主讲教师: | Nataša Pržulj |
| 开课单位: | 伦敦帝国理工学院 |
| 开课时间: | 2019-07-19 |
| 课程语种: | 英语 |
| 中文简介: |  我们面临着大量的分子和临床数据。我们正在测量形成大型复杂系统的细胞中各种生物分子之间的相互作用。患者组学数据集也越来越可用。这些系统级数据提供了关于细胞、组织和疾病的异质但互补的信息。挑战在于如何共同挖掘它们以回答基本的生物学和医学问题。这很重要，因为许多潜在问题的计算难以处理，因此需要开发启发式方法来寻找近似解。我们开发了从系统级、异构、网络生物医学数据的接线模式中提取新生物医学知识的方法。我们的方法揭示了分子网络和指示生物功能的多尺度网络组织中的模式，将隐藏在拓扑中的信息转化为特定领域的知识。我们引入了一个通用的数据融合（集成）框架来解决精准医学中的关键挑战：更好地对患者进行分层，预测癌症中的驱动基因，以及将批准的药物重新用于特定患者和患者群体。我们的新方法源于新颖的网络科学方法，结合图正则化非负矩阵三因子分解，这是一种用于降维和异构数据集协同聚类的机器学习技术。我们利用我们的新框架开发了执行其他相关任务的方法，包括从现代异构分子水平数据重新分类疾病、推断新的基因本体关系、对齐多个分子网络以及发现新的癌症机制。 |
| 课程简介: | We are faced with a flood of molecular and clinical data. We are measuring interactions between various bio-molecules in a cell that form large, complex systems. Patient omics datasets are also increasingly becoming available. These systems-level data provide heterogeneous, but complementary information about cells, tissues and diseases. The challenge is how to mine them collectively to answer fundamental biological and medical questions. This is nontrivial, because of computational intractability of many underlying problems, necessitating the development of heuristic methods for finding approximate solutions. We develop methods for extracting new biomedical knowledge from the wiring patterns of systems-level, heterogeneous, networked biomedical data. Our methods uncover the patterns in molecular networks and in the multi-scale network organization indicative of biological function, translating the information hidden in the topology into domain-specific knowledge. We introduce a versatile data fusion (integration) framework to address key challenges in precision medicine: better stratification of patients, prediction of driver genes in cancer, and re-purposing of approved drugs to particular patients and patient groups. Our new methods stem from novel network science approaches coupled with graph-regularized non-negative matrix tri-factorization, a machine learning technique for dimensionality reduction and co-clustering of heterogeneous datasets. We utilize our new framework to develop methodologies for performing other related tasks, including disease re-classification from modern, heterogeneous molecular level data, inferring new Gene Ontology relationships, aligning multiple molecular networks, and uncovering new cancer mechanisms. |
| 关 键 词: | 新生物医学; 数据融合; 癌症 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-06-04：yumf |
| 最后编审: | 2021-06-04：yumf |
| 阅读次数: | 88 |