计算机科学 ›› 2015, Vol. 42 ›› Issue (4): 177-180.doi: 10.11896/j.issn.1002-137X.2015.04.035
谢倩倩,李订芳,章 文
XIE Qian-qian, LI Ding-fang and ZHANG Wen
摘要: 新药研制成功的关键在于药物靶点的发现和准确定位。在已知的药物靶点中,离子通道蛋白是一类广受欢迎的靶点,它与免疫系统、心血管等疾病密切相关。 对于靶点的发现,传统生物方法成本高、耗时久。因此,探讨了基于机器学习的离子通道蛋白药物靶点的挖掘,以加快药物靶点发现过程,节约经费。由于药物靶点相关序列的长度不一致,考虑了蛋白质序列编码的13种特征,它们能将不等长的蛋白质序列转化成等长序列。通过数值实验筛选能够较好地区分靶点和非靶点的特征子集,并采用集成学习的方法整合特征得到预测模型。通过与已有工作的比较表明,提出的集成模型能得到较高的准确率,具有很好的应用前景。
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