通过权重共表达网络分析胰腺癌转移的特异基因网络

胰腺癌是一种高度恶性的消化道肿瘤,临床表现不具有特征性,在早期难诊断易转移,发展迅速;胰腺癌转移率高,且转移后难以治愈进而导致死亡率升高。因此相比胰腺癌未转移来说,研究确定预测胰腺癌转移、转移后的新治疗方法及治疗的潜在靶标尤为迫切并具有重要意义。而且近两年兴起的基于基因之间相关关系的共表达网络分析法,为多种复杂疾病的研究提供了新的线索和思路。我们应用TCGA数据库中的大量胰腺癌转录组数据,筛选胰腺癌转移差异表达基因,使用共表达网络分析法(WGCNA)筛选胰腺癌转移后,基因间出现一个52个差异表达基因构建的新共表达网络模块,且挖掘到其中ASIC4,GLDC,MMD2,CTNNA2,FOXC4-AS1,SNHG19和BCAN等11个枢纽基因。我们推测认为它们为胰腺癌转移提供了新的潜在诊断、治疗靶标。同时KEGG富集分析发现,该新基因网络模块内基因富集于炎症介质对色氨酸通道的调节,卵母细胞减数分裂,乙醛酸和二元羧酸盐代谢和甘氨酸、丝氨酸和苏氨酸代谢等4条代谢通路。     

Traffic Forecasting Model Based on Takagi-Sugeno Fuzzy Logical System

The local multiple regression fuzzy(LMRF)model based on Takagi-Sugeno fuzzy logical system and its application in traffic forecasting is proposed. Besides its prediction accuracy is testified and the model is proved much better than conventional forecasting methods. According to the regional traffic system, the model perfectly states the complex non-linear relation of the traffic and the local social economy. The model also efficiently deals with the system lack of enough data.