图 书 馆
整合组学中的方法论问题：整合多组学数据集Methodological aspects in integromics: integrating multiple omics data sets |
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| 课程网址: | http://videolectures.net/mlpmsummerschool2014_van_steen_integromi... |
| 主讲教师: | Kristel Van Steen |
| 开课单位: | 列日大学 |
| 开课时间: | 2015-02-17 |
| 课程语种: | 英语 |
| 中文简介: |  高通量技术的出现，包括基于测序仪和基于阵列的分析（表达，SNP，CpG），导致了大量数据的生成，通常称为“大数据”。生物学数据集是异类的，通常包括基因表达，基因型，表观基因组和其他类型的数据，称为“组学”数据。结果，跨学科的科学界做出了巨大的努力，以开发健壮，计算高效且明智的数据处理管道，以有效地分析“组学”数据，以提取生物学和临床相关的信息，即“有用的知识”。 获得大量信息资源的热情伴随着警告。与单组学研究相比，集成组学研究极具挑战性。这些挑战包括用于在集成分析环境中标准化数据生成和预处理或清理的协议开发，开发计算效率高的分析工具以从不同数据类型中提取知识以回答特定研究问题，建立验证和复制程序以及建立可视化工具结果。但是，从个性化医学的角度来看，预期的优势被认为胜过了与“整合学”相关的任何困难。对这个话题的强烈兴趣已经导致基于核融合，概率贝叶斯网络，相关网络，统计数据降维模型和聚类的新的集成交叉学科技术的出现。 贡献，我们将重点介绍组学集成工作中涉及的关键步骤，并总结主要的分析路径。然后，我们将放大一个新颖的集成分析框架（基于基因组MB MDR）。该框架将用作讨论集成分析的主要问题，陷阱和优点的红线。前所未有的机遇在眼前！ |
| 课程简介: | The advent of high-throughput technologies including sequencers and array-based assays (expression, SNP, CpG) have caused the generation of humongous amounts of data often referred to as “Big Data”. The biological datasets are heterogeneous and often include gene expression, genotype, epigenome and other types of data that are referred to as “-omics” data. As a result, there is a strong effort across multi-disciplinary scientific communities to develop robust, computationally efficient and sensible data processing pipelines to effectively analyze “-omics” data in order to extract biologically and clinically relevant information – “useful knowledge”. The enthusiasm of having access to vast amounts of information resources comes with a caveat. In contrast to single omics studies, integrated omics studies are extremely challenging. These challenges include protocol development for standardizing data generation and pre-processing or cleansing in integrative analysis contexts, development of computationally efficient analytic tools to extract knowledge from dissimilar data types to answer particular research questions, the establishment of validation and replication procedures, and tools to visualize results. However, from a personalized medicine point of view the anticipated advantages are believed to outweigh any difficulty related to “integromics”. The strong interest in the topic has already resulted in the emergence of new integrative cross-disciplinary techniques based on for instance kernel fusion, probabilistic Bayesian networks, correlation networks, statistical data-dimensionality reduction models, and clustering. In this contribution, we will highlight the key steps involved in omics integration efforts and will summarize main analytic paths. We will then zoom in on a novel integrated analysis framework (based on genomic MB-MDR). This framework will be used as a red thread to discuss main issues, pitfalls and merits of integrated analyses. Unprecedented opportunities lie ahead! |
| 关 键 词: | 数据集; 生物学 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2021-01-06：zyk |
| 最后编审: | 2021-01-06：zyk |
| 阅读次数: | 54 |