A p-Median approach for predicting drug response in tumour cells.
A p-Median approach for predicting drug response in tumour cells.
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DOI：
10.1186/s12859-014-0353-7
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发表时间：
2014-10-29
影响因子：
3
通讯作者：
Archetti F
中科院分区：
文献类型：
--
作者：
Fersini E;Messina E;Archetti F
与肿瘤细胞遗传起源相关的生物学数据的复杂性，对收集可用于预测治疗反应的有价值的知识提出了重大挑战。为了发现基因表达谱和药物反应之间的联系，提出了一种基于共识p-中值聚类的计算框架。主要目标是通过提取肿瘤细胞系中的共同模式，选择可能解释治疗结果的基因，并最终学习能够预测治疗反应的概率模型，同时预测（在计算机上）抗癌反应。在NCI 60数据集上进行的实验研究突出了三个主要发现：（1）共识p-Median能够创建在基因表达和药物反应方面高度相关的细胞系组;（2）从生物学角度来看，所提出的方法能够选择强烈参与几种癌症过程的基因;（3）建立在共识p-Median和所选基因基础上的药物反应的最终预测代表了预测潜在有用药物的有希望的步骤。所提出的学习框架代表了一种很有前途的方法，预测肿瘤细胞中的药物反应。本文的在线版本（doi：10.1186/s12859-014-0353-7）包含补充材料，可供授权用户使用。
The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses. The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs. The proposed learning framework represents a promising approach predicting drug response in tumour cells. The online version of this article (doi:10.1186/s12859-014-0353-7) contains supplementary material, which is available to authorized users.
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