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学习监督基因调控推理马尔可夫逻辑网：在人表皮角质形成细胞的抑制调控网络应用Learning a Markov logic network for supervised gene regulation inference: application to the ID2 regulatory network in human keratinocytes |
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| 课程网址: | http://videolectures.net/mlsb2012_dalche_buc_learning/ |
| 主讲教师: | Florence d Alche-Buc |
| 开课单位: | 埃夫里大学 |
| 开课时间: | 2012-10-23 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | **Motivation:** Gene regulatory network inference remains a challenging problem in systems biology despite numerous approaches. When substantial knowledge on a gene regulatory network is already available, supervised network inference also is appropriate. Such a method builds a binary classifier able to assign a class (Regulation/No regulation) to an ordered pair of genes. Once learnt, the classifier can be used to predict new regulations. In this work, we explore the framework of Markov Logic Network (MLN) recently introduced by Richardson & Domingos (2004, 2006). A MLN is a random Markov network that codes for a set of weighted formula. It therefore combines features of probabilistic graphical models with the expressivity of 1st order logic rules.\\ **Results:** Starting from a known gene regulatory network involved in the switch proliferation differentiation of keratinocytes cells, a set of experimental transcriptomic data, and description of genes in terms of GO terms encoded into first order logic, we learn a Markov Logic network, e.g. a set of weighted rules that conclude on the predicate ”regulates”. As a side contribution, we define a list of basic tests for performance assessment, valid for any binary classifier. A first test consists of measuring the average performance on balanced edge prediction problem; a 2nd one deals with the ability of the classifier, once enhanced by asymmetric bagging, to update a given network; finally a 3rd test measures the ability of the method to predict new interactions with new genes. Conclusion: The numerical studies show that MLNs achieve very good prediction while opening the door to some interpretability of the decisions. Additionally to the ability to suggest new regulation, such an approach allows to cross-validate experimental data with existing knowledge.\\ **Availability:** The code will be available on demand. |
| 关 键 词: | 马尔可夫过程; 遗传学; 生物学 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-01：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 75 |