本文選題：活性　切入點：深度信念網(wǎng)絡　出處：《計算機應用研究》2017年01期 　論文類型：期刊論文

【摘要】：為了提高抗癌藥物的發(fā)現(xiàn)效率并降低研發(fā)成本,針對基于PLK1此類結構和功能均高度保守的絲氨酸/蘇氨酸蛋白激酶在多種腫瘤類型中高表達的特點,提出以PLK1 PBD為靶點的深度信念網(wǎng)絡(deep believe network,DBN)抗癌活性研究方法。利用深度學習思想,對20 000個化合物的抗癌活性進行分析,并分別與ANN、SVM方法進行對比驗證。實驗結果表明,在同等條件下,DBN網(wǎng)絡針對抗癌藥物活性研究具有突出的優(yōu)勢,其平均預測活性的精確度可達91.05%,明顯高于ANN和SVM,從而實現(xiàn)了對化合物抗癌活性的良好評估。
[Abstract]:In order to improve the efficiency of the discovery of anticancer drugs and reduce the cost of research and development, the high expression of serine / threonine protein kinase, which is highly conserved in structure and function of PLK1, was studied in various tumor types. The research method of anticancer activity of deep believe network (DBN) with PLK1 PBD as the target is proposed. The anticancer activity of 20 000 compounds is analyzed by using the idea of deep learning, and compared with the ANN PBD method. The experimental results show that, Under the same conditions, the research on anticancer drug activity of DDBN network has outstanding advantages, its average accuracy of predicting activity can reach 91.0555.It is obviously higher than that of ANN and SVM, thus realizing a good evaluation of the anticancer activity of compounds.
【作者單位】： 新疆大學軟件學院;新疆大學網(wǎng)絡中心;
【基金】：新疆維吾爾自治區(qū)研究生創(chuàng)新基金資助項目(XJGRI2015034)
【分類號】：R979.1;TP181
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本文編號：1632587