SPICE: discovery of phenotype-determining component interplays.
SPICE: discovery of phenotype-determining component interplays.
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DOI：
10.1186/1752-0509-6-40
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发表时间：
2012-05-14
影响因子：
--
通讯作者：
Samatova NF
中科院分区：
文献类型：
--
作者：
Chen Z;Padmanabhan K;Rocha AM;Shpanskaya Y;Mihelcic JR;Scott K;Samatova NF
生物细胞的潜在行为是复杂的。推导出潜在的简单性，或者说控制这种行为的基本规则，一直是系统生物学的圣杯。数据驱动的预测系统组件及其组件的相互作用，负责目标系统的表型是一个关键和具有挑战性的一步，在这奋进的努力。我们称之为系统表型相关相互作用组件枚举器（Spice）的方法迭代地枚举统计学上显著的系统组件，这些组件被假设为（1）在定义目标系统的表型的特异性中起重要作用;（2）表现出功能一致的行为，即以协调的方式执行表型特异性功能;以及（3）当在预测模型的集合中共同使用时，提高系统表型的预测技能。Spice可以应用于基于实例的数据和基于网络的数据。经过验证，Spice有效地识别了与三种目标表型相关的系统组件：生物氢生产，运动和癌症。手动结果管理与文献中报告的已知表型相关系统组件一致。此外，与应用于8个基准微阵列数据集的一些最先进的方法相比，使用所识别的系统组件作为判别特征在表型分类任务上将预测准确度提高了10%。我们制定了一个问题，枚举的表型确定系统组件的相互作用，并提出了一个有效的方法（香料）来解决这个问题。Spice改进了从各种微阵列数据集中识别癌症相关基因组的方法，并检测到与微生物生物氢产生和运动相关的基因组，其中许多在文献中有报道。与单个分类器和/或其他集成方法相比，Spice还提高了系统表型确定的预测技能，例如装袋，提升，随机森林，最近的收缩质心和随机森林变量选择方法。
A latent behavior of a biological cell is complex. Deriving the underlying simplicity, or the fundamental rules governing this behavior has been the Holy Grail of systems biology. Data-driven prediction of the system components and their component interplays that are responsible for the target system’s phenotype is a key and challenging step in this endeavor. The proposed approach, which we call System Phenotype-related Interplaying Components Enumerator (Spice), iteratively enumerates statistically significant system components that are hypothesized (1) to play an important role in defining the specificity of the target system’s phenotype(s); (2) to exhibit a functionally coherent behavior, namely, act in a coordinated manner to perform the phenotype-specific function; and (3) to improve the predictive skill of the system’s phenotype(s) when used collectively in the ensemble of predictive models. Spice can be applied to both instance-based data and network-based data. When validated, Spice effectively identified system components related to three target phenotypes: biohydrogen production, motility, and cancer. Manual results curation agreed with the known phenotype-related system components reported in literature. Additionally, using the identified system components as discriminatory features improved the prediction accuracy by 10% on the phenotype-classification task when compared to a number of state-of-the-art methods applied to eight benchmark microarray data sets. We formulate a problem—enumeration of phenotype-determining system component interplays—and propose an effective methodology (Spice) to address this problem. Spice improved identification of cancer-related groups of genes from various microarray data sets and detected groups of genes associated with microbial biohydrogen production and motility, many of which were reported in literature. Spice also improved the predictive skill of the system’s phenotype determination compared to individual classifiers and/or other ensemble methods, such as bagging, boosting, random forest, nearest shrunken centroid, and random forest variable selection method.
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影响因子：
5.6
作者：
Blokesch, M;Albracht, SPJ;Böck, A
通讯作者：
Böck, A
DOI：
10.1007/978-1-4419-7046-6_3
发表时间：
2011-01-01
期刊：
SOFTWARE TOOLS AND ALGORITHMS FOR BIOLOGICAL SYSTEMS
影响因子：
--
作者：
Czajkowski, Marcin;Kretowski, Marek
通讯作者：
Kretowski, Marek
影响因子：
9.9
作者：
通讯作者：
--
DOI：
10.1073/pnas.220392197
发表时间：
2000-10-24
影响因子：
11.1
作者：
Butte, AJ;Tamayo, P;Kohane, IS
通讯作者：
Kohane, IS
影响因子：
3
作者：
Díaz-Uriarte R;Alvarez de Andrés S
通讯作者：
Alvarez de Andrés S