Breast cancer is a common malignant tumor of which pathogenic genes are widely studied. Since gene pairs are considered as biomarkers to identify cancer patients, in this paper, we use information theory to study the collaboration features of gene pairs. The measure of synergy based on mutual information (MI) is introduced to determine whether genes collaborate with each other in breast cancer. Part mutual information (PMI) is introduced to further select collaborative genes and construct a synergy network, which overcomes the shortage of MI. Furthermore, a dual network of synergy network is constructed and structural indices are calculated to identify vital genes. By decision tree and support vector machine, synergy is considered as a suitable index and dual network with PMI improves the accuracy of cancer identification. This method can be extended to identify other biological phenomenon and find collaborative genes as biomarkers.

中文翻译：

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部分互信息通过协同网络识别乳腺癌重要基因

乳腺癌是一种常见的恶性肿瘤，其致病基因被广泛研究。由于基因对被认为是识别癌症患者的生物标志物，因此在本文中，我们使用信息论来研究基因对的协作特征。引入基于互信息 (MI) 的协同作用测量来确定基因是否在乳腺癌中相互协作。引入部分互信息（PMI）进一步选择协同基因，构建协同网络，克服了MI的不足。此外，构建协同网络的双重网络并计算结构指数以识别重要基因。通过决策树和支持向量机，协同被认为是一个合适的指标，与PMI的双重网络提高了癌症识别的准确性。