Optimally discriminative subnetwork markers predict response to chemotherapy.
Optimally discriminative subnetwork markers predict response to chemotherapy.
复制标题
DOI：
10.1093/bioinformatics/btr245
复制
发表时间：
2011-07-01
期刊：
影响因子：
--
通讯作者：
Sahinalp SC
中科院分区：
文献类型：
--
作者：
Dao P;Wang K;Collins C;Ester M;Lapuk A;Sahinalp SC
动机：肿瘤样本的分子谱已被广泛和成功地用于分类问题。已经提出了许多算法来基于具有相对高性能的表达谱来预测肿瘤样品的类别。然而，预测对癌症治疗的反应已被证明更具挑战性，并且仍然高度需要具有改进的普遍性的新方法。最近的研究已经清楚地表明，整合蛋白质-蛋白质相互作用（PPI）数据与基因表达谱的优势，在分类问题的子网络标记的发展。结果如下：我们描述了一种新的基于网络的分类算法（OptDis），使用颜色编码技术来识别最佳区分子网标记。关注PPI网络，我们将我们的算法应用于药物反应研究：我们使用已发表的接受联合化疗的乳腺癌患者队列评估我们的算法。我们表明，我们的OptDis方法改进了以前发表的子网络方法，并提供了更好，更稳定的性能相比，其他子网络和单基因方法。我们还表明，我们的子网络方法产生的预测标志物在独立队列中更具重现性，并为治疗反应的生物学过程提供了有价值的见解。可用性：可在以下网址获得该实现：http://www.cs.sfu.ca/~pdao/personal/OptDis.html联系方式：cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com
Motivation: Molecular profiles of tumour samples have been widely and successfully used for classification problems. A number of algorithms have been proposed to predict classes of tumor samples based on expression profiles with relatively high performance. However, prediction of response to cancer treatment has proved to be more challenging and novel approaches with improved generalizability are still highly needed. Recent studies have clearly demonstrated the advantages of integrating protein–protein interaction (PPI) data with gene expression profiles for the development of subnetwork markers in classification problems. Results: We describe a novel network-based classification algorithm (OptDis) using color coding technique to identify optimally discriminative subnetwork markers. Focusing on PPI networks, we apply our algorithm to drug response studies: we evaluate our algorithm using published cohorts of breast cancer patients treated with combination chemotherapy. We show that our OptDis method improves over previously published subnetwork methods and provides better and more stable performance compared with other subnetwork and single gene methods. We also show that our subnetwork method produces predictive markers that are more reproducible across independent cohorts and offer valuable insight into biological processes underlying response to therapy. Availability: The implementation is available at: http://www.cs.sfu.ca/~pdao/personal/OptDis.html Contact: cenk@cs.sfu.ca; alapuk@prostatecentre.com; ccollins@prostatecentre.com
登录
查看更多内容
影响因子：
14.9
作者：
Jensen LJ;Kuhn M;Stark M;Chaffron S;Creevey C;Muller J;Doerks T;Julien P;Roth A;Simonovic M;Bork P;von Mering C
通讯作者：
von Mering C
影响因子：
12.3
作者：
Fortney, Kristen;Kotlyar, Max;Jurisica, Igor
通讯作者：
Jurisica, Igor
影响因子：
1.7
作者：
Bruckner, Sharon;Hueffner, Falk;Sharan, Roded
通讯作者：
Sharan, Roded
影响因子：
45.3
作者：
Liedtke, Cornelia;Mazouni, Chafika;Pusztai, Lajos
通讯作者：
Pusztai, Lajos
影响因子：
8.4
作者：
Bonnefoi, Herve;Underhill, Craig;Cameron, David
通讯作者：
Cameron, David