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候选基因排序的基因组数据融合
Candidate gene prioritization by genomic data fusion |
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| 课程网址: |
http://videolectures.net/mlsb07_moreau_cgp/
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| 主讲教师: |
Yves Moreau
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| 开课单位: |
鲁汶大学
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| 开课时间: |
2007-11-20
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| 课程语种: |
英语
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| 中文简介: |
大量的生物学数据使得将候选基因分配给疾病和生物学途径成为一个巨大的挑战。我们提出了ENDEAVOUR，这是一种通用的计算方法，可根据其与案例特定参考基因集的相似性来对候选基因进行优先排序。与以前的方法不同，ENDEAVOR能够灵活地利用来自不同来源的多个数据集。它允许从头生成的数据集进行模块化合并，并通过应用订单统计信息将不同的优先级排序集成到全局排名中。我们首先通过对29种疾病和3种生物学途径的统计交叉验证来验证总体表现。我们在斑马鱼模型中验证了DiGeorge综合征的新型候选人，并提出了一些先天性心脏病的新候选人。我们使用来自多个物种（人类，小鼠，大鼠，果蝇和秀丽隐杆线虫）的数据扩展了基本的ENDEAVOR方法。我们还提出了一种替代的机器学习方法，该方法使用核方法进行新颖性检测的基因优先级排序方法要优于我们先前的结果。
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| 课程简介: |
The overwhelming amount of biological data makes the assignment of candidate genes to diseases and biological pathways a formidable challenge. We present ENDEAVOUR, a generally applicable computational methodology to prioritize candidate genes based on their similarity to case-specific reference gene sets. Unlike previous methods, ENDEAVOUR is capable of flexibly utilizing multiple data sets from diverse sources. It allows the modular incorporation of de novo generated data sets and integrates distinct prioritizations into a global ranking by applying order statistics. We first validate the overallperformance in a statistical cross validation of 29 diseases and 3 biological pathways. We validate a novel candidate for DiGeorge syndrome in a zebrafish model and present several new candidates for congenital heart disease. We extend the basic ENDEAVOUR methodology using data from multiple species (human, mouse, rat, drosophila and C. elegans). We also present an alternative machine learning methodology for gene prioritization using kernel methods for novelty detection that outperforms our previous results.
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| 关 键 词: |
候选基因; 基因排序; 数据融合; ENDEAVOR方法
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| 课程来源: |
视频讲座网
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| 数据采集: |
2020-04-03：zhouxj
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| 最后编审: |
2020-05-25：cxin
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| 阅读次数: |
49
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图 书 馆