构建个体特异性异常指标探测复杂疾病网络临界点

Detecting Critical Point of Complex Disease Network by Constructing Individual-Specific Anomaly Index

探测复杂疾病临界点对疾病早期诊断至关重要,通过对高通量的生物分子数据的挖掘,本文提出一种结合个体特异性网络与隐马尔科夫模型的方法,构建个体特异性异常指标,以探测从相对健康期到疾病临界期的临界点。为验证该方法的有效性,将该方法分别应用在模拟数据集、肺部急性损伤数据、前列腺癌数据中,均成功在疾病恶性突变前找到其各自的临界点。信号基因的有效性和敏感性都通过生物功能分析得到了验证。

Detecting critical points of complex diseases is very important for early diagnosis of diseases. By exploring information of high-throughput data, we combine individual-specific network and hidden Markov model to construct individual-specific abnormal indicators in order to detect the critical points between relative health period and disease critical period. To verify the validity of the method, it was applied to simulated data sets, lung acute injury data and prostate cancer data. The critical points were successfully found before malignant mutation. The validity and sensitivity of signal genes were verified by biological function analysis.