Jiang Hao, Ching Wai-Ki, Cheung Wai-Shun, Hou Wenpin, Yin Hong
Department of Mathematics, School of Information, Renmin University of China, No.59 Zhong Guan Cun Avenue, Hai Dian District, Beijing, 100872, China.
Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong, Hong Kong.
BMC Syst Biol. 2017 Dec 21;11(Suppl 7):138. doi: 10.1186/s12918-017-0514-1.
Breast cancer is one of the leading causes of deaths for women. It is of great necessity to develop effective methods for breast cancer detection and diagnosis. Recent studies have focused on gene-based signatures for outcome predictions. Kernel SVM for its discriminative power in dealing with small sample pattern recognition problems has attracted a lot attention. But how to select or construct an appropriate kernel for a specified problem still needs further investigation.
Here we propose a novel kernel (Hadamard Kernel) in conjunction with Support Vector Machines (SVMs) to address the problem of breast cancer outcome prediction using gene expression data. Hadamard Kernel outperform the classical kernels and correlation kernel in terms of Area under the ROC Curve (AUC) values where a number of real-world data sets are adopted to test the performance of different methods.
Hadamard Kernel SVM is effective for breast cancer predictions, either in terms of prognosis or diagnosis. It may benefit patients by guiding therapeutic options. Apart from that, it would be a valuable addition to the current SVM kernel families. We hope it will contribute to the wider biology and related communities.
乳腺癌是女性主要死因之一。开发有效的乳腺癌检测和诊断方法非常必要。近期研究聚焦于基于基因的特征用于预后预测。核支持向量机因其在处理小样本模式识别问题上的判别能力而备受关注。但如何为特定问题选择或构建合适的核仍需进一步研究。
在此，我们提出一种新型核（哈达玛核）并结合支持向量机（SVM）来解决使用基因表达数据进行乳腺癌预后预测的问题。在采用多个真实数据集测试不同方法性能时，哈达玛核在ROC曲线下面积（AUC）值方面优于经典核和相关核。
哈达玛核支持向量机在乳腺癌预测方面无论是预后还是诊断都很有效。它可能通过指导治疗选择使患者受益。除此之外，它将是当前支持向量机核家族的一个有价值补充。我们希望它将为更广泛的生物学及相关领域做出贡献。
