计算机科学 ›› 2020, Vol. 47 ›› Issue (11): 268-274.doi: 10.11896/jsjkx.200100079
黄羿1,2, 孔世明2, 王以松1, 张明义3, 马新强1,2
HUANG Yi1,2, KONG Shi-ming2, WANG Yi-song1 , ZHANG Ming-yi3, MA Xin-qiang1,2
摘要： 布尔网络是一种重要的基因调控数学模型,从布尔网络的状态变换推断其结构以发现基因之间的调控关系是布尔网络研究中长期关注的重要问题。已有的归纳逻辑程序算法不能从布尔网络的不确定(解释)状态变换学习推断其网络结构。为此,文中提出了非确定解释转换学习(Learning From Non-deterministic interpretation Transitions,LFNDIT)算法从布尔网络异步更新语义下的解释变换学习其网络结构。首先将异步更新语义下的不确定解释变换集转换成确定解释变换集,然后利用Inoue等提出的从1步解释转换学习(Learning From 1-step state transition,LF1T)算法计算其对应的正规逻辑程序(布尔网络)。该算法的完备性得到了证明,初步的实验结果表明,该方法能有效地从不确定状态变换计算布尔网络的结构,从而为发现布尔网络的结构提供了新的思路。
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