Uncovering the rules for protein-protein interactions from yeast genomic data

Identifying protein-protein interactions is crucial for understanding cellular functions. Genomic data provides opportunities and challenges in identifying these interactions. We uncover the rules for predicting protein-protein interactions using a frequent pattern tree (FPT) approach modified to generate a minimum set of rules (mFPT), with rule attributes constructed from the interaction features of the yeast genomic data. The mFPT prediction accuracy is benchmarked against other commonly used methods such as Bayesian networks and logistic regressions under various statistical measures. Our study indicates that mFPT outranks other methods in predicting the protein-protein interactions for the database used. We predict a new protein-protein interaction complex whose biological function is related to premRNA splicing and new protein-protein interactions within existing complexes based on the rules generated.

Fuzzy inferring mathematical morphology and optical implementation

Fuzzification is introduced into gray-scale mathematical morphology by using two-input one-output fuzzy rule-based inference systems. The fuzzy inferring dilation or erosion is defined from the approximate reasoning of the two consequences of a dilation or an erosion and an extended rank-order operation. The fuzzy inference systems with numbers of rules and fuzzy membership functions are further reduced to a simple fuzzy system formulated by only an exponential two-input one-output function. Such a one-function fuzzy inference system is able to approach complex fuzzy inference systems by using two specified parameters within it-a proportion to characterize the fuzzy degree and an exponent to depict the nonlinearity in the inferring. The proposed fuzzy inferring morphological operators tend to keep the object details comparable to the structuring element and to smooth the conventional morphological operations. Based on digital area coding of a gray-scale image, incoherently optical correlation for neighboring connection, and optical thresholding for rank-order operations, a fuzzy inference system can be realized optically in parallel. (C) 1996 Society of Photo-Optical Instrumentation Engineers.

语义Web中的自动推理和主动规则终止性分析

语义Web是现有Web的一个语义延伸，目标是使Web的应用具有一定的智能，能够进行更准确的信息搜索和一定的决策支持，帮助用户自动完成一些任务。语义Web的实现需要人工智能、数据库等领域的技术支持，需要对已有的技术进行改进和扩展，也需要对现有技术本身如ECA规则进行更深入的研究从而进一步将其扩展到语义Web。自动推理技术是语义Web实现的基础之一，而ECA规则可以为系统增加反应性行为能力，本文围绕自动推理和ECA规则，主要进行了如下研究：（1） 一阶逻辑推理技术对语义Web的推理支持。分析了语义Web语言需要的表达能力，对现有描述逻辑工具、一阶逻辑工具的能力进行了考察。现有的描述逻辑工具不足以处理应用所需要的更丰富的语言成分、任意的一阶逻辑规则等，而一阶逻辑自动定理证明工具和有限模型查找工具有分别都有较好的研究成果。把语义Web的推理问题化成可满足性判定问题后，本文给出了一阶逻辑定理证明器和有限模型查找器形成的并发过程进行可满足性判定的方法。这样既可以解决描述逻辑工具的不足，并可弥补定理证明器对可满足的公式推理的不完备性。（2） ECA规则(主动规则)的终止性分析。终止性是主动规则最重要的行为性质，不终止是由于主动规则的级联触发造成的，意味着定义错误。终止性检查通常是不可判定的。有效的静态分析方法是主动规则得以实际应用的保证。静态分析的关键是合适的数据库状态描述，本文给出用约束表示数据库状态的方法，通过模拟规则执行，使用约束求解进行条件计算和重复状态检查，判断主动规则集合是否终止。用约束精确表示了数据库状态，在规则处理中考虑了执行语义，从而得到更精确的终止性结论。

一种基于知识的自然景物解释

利用我们研制成的基于知识的区域分析系统,我们对自然景物图象做了分析、解释。本文介绍:(1)图象初始分割;(2)图象特征提取;(3)规则集构造。这里,图象初始分割采用的是模糊域方法,它基于Fuzzy C-means 算法,并在此基础上修改了收敛准则,增加了迭代分割功能。图象特征分为主持征及从特征,它们建立在层次化的区域数据结构上。适合于区域分析的规则集已包括三类规则,它们不仅具有较强的知识表示能力,而且易于控制及利用。本文介绍针对包括天空、道路、树木、建筑物等物体的简单景物图象所做的解释实验,并给出了实验结果.

汉语文本语篇语句焦点确定及焦点与重音关系研究

This study investigated the method of the focus identification in Chinese text discourse and the relationship between accent and focus, large corpus analysis and decision tree were used in the research. The main results are: 1. Based on the concept of the Focus and understanding of the discourse, Foci identification is consistent and steady; 2. Special Focus markers and specific Focus constructions have greater influence than special constituent order on identifying Focus in Chinese discourse; while information states also have great influence on focus identifying; part of speech，information state, the relative position in the sentence, focus-sensitive operator, specific Focus constructions, contrast relations, relations between the sentences are important factors to focus identifying; 3. Using multi-dimensional tagging and knowledge discovery, it is a feasible way to construct and employ decision trees by computing tagging results to identify Focus; 4. Focus predicting also depends on literal types and styles of the discourse, several types of decision trees should be constructed for different literal types; 5. In the monologue discourse, the most prominent accent is located on the Focus word or in the scope of the Focus; there are some kinds of rules on accent assignment in broad Focus; it is necessary to analyze and classify focus structure for the research of relations between accent and Focus.