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目的运用生物信息学方法预测整合素黏附体信号蛋白间的信号转导通路,为以实验方法研究整合素相关信号转导通路的机制提供参考。方法把相互作用的整合素黏附体信号分子对搭建成相互作用网络;利用相互作用的置信概率构建边权;然后,采用动态规划算法计算出最小权重的线性通路,再把这些线性通路组装成通路网络。结果从147个整合素黏附体蛋白所组成的736对相互作用中预测出7个信号通路网络,并计算出各个通路网络中基因本体论注释蛋白的占有率。结论对信号转导通路的研究能够在分子水平上探索疾病的发病机制。预测出可能的信号通路网络,不仅为基础医学研究疾病机制提供有用信息,也为探索包括力学、化学等外界信号刺激下的信号转导通路提供有益参考信息。
OBJECTIVE: To predict the signal transduction pathways of integrin adhesion protein by using bioinformatics methods and to provide a reference for the study of the mechanism of integrin-related signal transduction pathways by experimental methods. Methods We construct the interacting integrin signaling molecule pair as an interaction network. We use the confidence probability of interaction to construct the edge weight. Then we use the dynamic programming algorithm to calculate the minimum weight linear path, and then assemble the linear path into the path The internet. Results Seven signal pathway networks were predicted from the 736 pairs of 147 integrin adhesion proteins and their share of annotated proteins in each pathway network was calculated. Conclusion The study of signal transduction pathway can explore the pathogenesis of the disease at the molecular level. Predicting the possible network of signaling pathways not only provides useful information for the study of disease mechanisms in basic medical sciences, but also provides useful reference information for exploring signal transduction pathways including mechanics and chemistry stimulated by external signals.