0


用于理解系统生物学模型的时间特征提取

Extracting Temporal Signatures for Comprehending Systems Biology Models
课程网址: http://videolectures.net/kdd2010_sundaravaradan_ets/  
主讲教师: Naren Sundaravaradan
开课单位: 弗吉尼亚理工大学
开课时间: 2010-10-01
课程语种: 英语
中文简介:
系统生物学近年来取得了长足的进步,具有模拟复杂系统的能力,包括细胞分裂,应激反应,能量代谢和信号通路。然而,伴随着他们改进的建模能力,这种生化网络模型也变得非常复杂,人类无法理解。我们建议将网络理解作为KDD社区的一个关键问题,其目标是创建复杂生物网络的可解释表示。我们将此问题表述为从多变量时间序列数据中提取时间签名之一,其中签名由时间序列分量之间的序数比较组成。我们展示了如何通过将数据挖掘问题公式化为排序空间中的特征选择之一来推断出这种签名。我们为排名顺序空间提出了五种新的特征选择策略,并评估了它们的选择优势。芽殖酵母细胞周期模型的实验结果显示出与人类对细胞周期的解释相当的令人信服的结果。
课程简介: Systems biology has made massive strides in recent years, with capabilities to model complex systems including cell division, stress response, energy metabolism, and signaling pathways. Concomitant with their improved modeling capabilities, however, such biochemical network models have also become notoriously complex for humans to comprehend. We propose network comprehension as a key problem for the KDD community, where the goal is to create explainable representations of complex biological networks. We formulate this problem as one of extracting temporal signatures from multi-variate time series data, where the signatures are composed of ordinal comparisons between time series components. We show how such signatures can be inferred by formulating the data mining problem as one of feature selection in rank-order space. We propose five new feature selection strategies for rank-order space and assess their selective superiorities. Experimental results on budding yeast cell cycle models demonstrate compelling results comparable to human interpretations of the cell cycle.
关 键 词: 系统生物学; 复杂系统; 建模能力
课程来源: 视频讲座网
最后编审: 2020-05-23:杨雨(课程编辑志愿者)
阅读次数: 35