Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks
Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks
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DOI：
10.1073/pnas.220392197
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发表时间：
2000-10-24
影响因子：
11.1
通讯作者：
Kohane, IS
中科院分区：
文献类型：
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作者：
Butte, AJ;Tamayo, P;Kohane, IS
为了找到影响癌症对抗癌药物敏感性的基因调控网络和基因簇，我们将一个数据库与一个数据库连接起来，该数据库通过使用微阵列在60个癌细胞系中测量了7,245个基因的基线表达水平，并将一个数据库连接到一个数据库，其中包含抑制这些相同细胞系生长所需的5,084种抗癌剂的数量。计算基因表达和药物敏感性指标之间的综合成对相关性。弱于阈值强度的关联被移除，留下了高度相关的基因和代理网络，称为相关性网络。构建了抗癌药物敏感性的潜在单基因决定因素的假设。在执行的大量计算中，随机机会的影响：通过重复的随机排列测试来经验地确定；只有比在倍数排列的数据中看到的更强的关联被用于聚类。我们讨论了这种方法相对于其他方法的优势，例如系统发育类型的树聚类和自组织地图。
In an-effort to find gene regulatory networks and clusters of genes that affect cancer susceptibility to anticancer agents, we joined a database with baseline expression levels of 7,245 genes measured by using microarrays in 60 cancer cell lines, to a database with the amounts of 5,084 anticancer agents needed to inhibit growth of those same cell lines. Comprehensive pair-wise correlations were calculated between gene expression and measures of agent susceptibility. Associations weaker than a threshold strength were removed, leaving networks of highly correlated genes and agents called-relevance networks. Hypotheses for potential single-gene determinants of anticancer agent susceptibility were constructed. The effect of random chance in the large number of calculations performed:was empirically determined by repeated random permutation testing; only associations stronger than those seen in multiply permuted data were used in clustering. We discuss the advantages of this methodology over alternative approaches, such as phylogenetic-type tree clustering and self-organizing maps.