LFNDIT:从不确定状态变换学习布尔网络

LFNDIT:Learning Boolean Networks from Nondeterministic Interpretation Transitions

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。

Boolean network is an important mathematical model for gene regulation.It is an important issue that inferring structure from the interpretation transitions of Boolean network to discover the regulatory relationship between genes.Thus,resear-chers in the field of Boolean networks have been paying attention for a long time.Existing inductive logic program algorithms cannot infer the network structure from a set of nondeterministic state transitions.To this end,LFNDIT is proposed to learn the structure from state transitions under the asynchronous update semantics of Boolean network.First it translates a set of uncertain state transitions into the set of certain state transitions,and then uses the LF1T learning algorithm proposed by Inoue et al to calculate the corresponding normal logic program(Boolean network).The completeness of LFNDIT is proofed.The preliminary experimental results show that the algorithm can effectively calculate the Boolean network structure from the uncertain state transitions,thus it provides a new idea for discovering Boolean network structure.