Arias Carlos Roberto, Yeh Hsiang-Yuan, Soo Von-Wun
Institute of Information Systems and Applications, National Tsing Hua University, Hsinchu 30013, Taiwan.
ScientificWorldJournal. 2012;2012:842727. doi: 10.1100/2012/842727. Epub 2012 May 2.
Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.
找到与遗传疾病相关的基因并非易事。因此，需要计算方法为生物医学界提供线索，以探索更有可能作为生物标志物与特定疾病相关的基因。我们提出了一种生物标志物识别问题，使用一种名为基于最短路径的微阵列数据基因优先级排序方法，该方法通过投票方案扩展了结构和生物学特性以及边通量（GP-MIDAS-VXEF）。该方法基于在蛋白质相互作用网络上找到相关相互作用，然后使用最短路径和拓扑分析对基因进行评分，使用投票方案和生物增强对结果进行整合。我们进行了两个实验，一个是前列腺原发样本和正常样本，另一个是有和没有淋巴结转移的前列腺原发性肿瘤。我们使用137个真正的前列腺癌基因作为基准。在第一个实验中，GP-MIDAS-VXEF在基准测试中优于所有其他现有方法，通过在前50个得分中从候选集中检索到最真实相关的基因。我们应用相同的技术推断前列腺癌伴有未充分确立的淋巴结转移中的显著生物标志物。
