• Proceedings of the Korean Society for Bioinformatics Conference
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• 2005.09a
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• pp.339-343
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• 2005

Boolean networks(BN) construction is one of the commonly used methods for building gene networks from time series microarray data. However, BN has two major drawbacks. First, it requires heavy computing times. Second, the binary transformation of the microarray data may cause a loss of information. This paper propose two methods using liner regression to construct gene regulatory networks. The first proposed method uses regression based BN variable selection method, which reduces the computing time significantly in the BN construction. The second method is the regression based network method that can flexibly incorporate the interaction of the genes using continuous gene expression data. We construct the network structure from the simulated data to compare the computing times between Boolean networks and the proposed method. The regression based network method is evaluated using a microarray data of cell cycle in Caulobacter crescentus.

• The Journal of Korean Association of Computer Education
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• v.15 no.1
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• pp.65-72
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• 2012

Generally, a logic function has many factored forms. The problem of finding more compact factored form is one of the basic operations in logic synthesis. In this paper, we present a new method for factoring Boolean functions to assist in educational logic designs. Our method for factorization is to implement two-cube Boolean division with supports of an expression. The number of literals in a factored form is a good estimate of the complexity of a logic function. Our empirical evaluation shows the improvements in literal counts over previous other factorization methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.7
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• pp.3251-3257
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• 2011

This paper presents a new Boolean extraction technique for logic synthesis. This method extracts kernel-kernel pairs as well as cokernel-kernel pairs. The given logic expressions can be translated into Boolean divisors and quotients with kernel-kernel pairs. Next, kernel intersection method provides the common sub-expressions for several logic expressions. Experimental results show the improvement in literal count over previous other extraction methods.

• The Transactions of the Korea Information Processing Society
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• v.6 no.3
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• pp.781-791
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• 1999

DD(Decision Diagrams) is an efficient operational data structure for an optimal expression of boolean functions. In a graph-based synthesis using DD, the goal of optimization decreases representation space for boolean functions. This paper represents boolean functions using OPKFDD(Ordered Pseudo-Kronecker Functional Decision Diagrams) for a graph-based synthesis and is based on the number of nodes as the criterion of DD size. For a property of OPKFDD that is able to select one of different decomposition types for each node, OPKFDD is variable in its size by the decomposition types selection of each node and input variable order. This paper proposes a method for generating OPKFDD efficiently from the current BDD(Binary Decision Diagram) Data structure and an algorithm for minimizing one. In the multiple output functions, the relations of each function affect the number of nodes of OPKFDD. Therefore this paper proposes a method to decide the input variable order considering the above cases. Experimental results of comparing with the current representation methods and the reordering methods for deciding input variable order are shown.

• Proceedings of the KIEE Conference
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• 1985.07a
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• pp.242-245
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• 1985

논리회로의 합성이란 minimize된 Boolean Expression을 실재로 존재라는 TTL IC로 Implement시키는 과정을 말한다. 즉, IC pin assignment 의 과정인 것이다. 본 논문에서는 논리회로를 합성하는 expert system의 초보적인 형태를 제안하고 있다.

• Journal of the Korea Society of Computer and Information
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• v.15 no.11
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• pp.215-224
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• 2010

Most chronic diseases are complex diseases which are caused by interactions of several genes. Studies on finding SNPs and gene-gene interactions involved in the development of complex diseases can contribute to prevention and treatment of the diseases. Previous studies mostly concentrate on finding only the set of SNPs involved. In this study we suggest a way to see how these SNPs interact using boolean expressions. The proposed method consists of two stages. In the first stage we find the set of SNPs involved in the development of diseases using mutual information based on entropy. In the second stage we find the highest accuracy boolean expression that consists of the SNP set obtained in the first stage. We experimented with clinical data to demonstrate the effectiveness of the proposed method. We also compared the differences between our method and the previous results on the SNP associations studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.8
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• pp.3715-3722
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• 2011

This paper presents a new Boolean extraction technique for logic synthesis. This method first calculates divisor/2-cube quotients, 2-cube quotient pairs, and 2-cube quotient matrices. Then we find candidates, which can be common sub-expressions, from 2-cube quotients and matrices. Next, candidate intersection provides the common sub-expressions for several logic expressions. Experimental results show the improvements in literal counts over the previous methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.7 no.1
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• pp.25-28
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• 1982

A method for finding the symbolic reliability expressision of a conbinational logic circuit is presented. The evaluation of the probabilities of the outputs can be symbolically evaluated by the Boolean operation named sharp operation, provided that every input of such a circuit can be treated as random variables with values set(0, 1) and the output of a circuit can be represented by a Boolean sum of produt expression.

• Journal of Korea Society of Industrial Information Systems
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• v.18 no.1
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• pp.25-35
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• 2013

Automatically deriving only individual don't-care minterms that can effectively reduce a Boolean logic expressions are being investigated. Don't-care conditions play an important role in optimizing logic design. The type of unknown don't-care minterms that can always reduce the number of product terms in Boolean expression are referred as single hypothetical don't-care (S-HDC) minterms. This paper describes the Quasi Quine-McCluskey method that systematically derives S-HDC minterms. For the most part, this method is similar to the original Quine-McCluskey method in deriving the prime implicants. However, the Quasi Quine-McCluskey method further derives S-HDC minterms by applying so-called a combinatorial comparison operation. Upon completion of the procedure, the designer can review generated S-HDC minterms to test its appropriateness for a particular application.

• Annual Conference of KIPS
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• 2008.11a
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• pp.722-725
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• 2008

복합질환(complex disease)의 원인과 작용 모델을 찾기 위해 여러 가지 통계적인 방법들과 기계 학습(machine learning)의 방법 등이 사용되고 있다. 소수 SNP의 작용모델을 찾는 방법은 많이 알려져 있지만 다수 SNP의 작용 모델을 효과적으로 찾는 방법은 거의 연구되어 있지 않다. 본 연구에서는 원인 SNP들의 작용을 부울 식(boolean expression)으로 나타내고, 유전 알고리즘(genetic algorithm)을 이용하여 예측 정확도가 높은 부울 식을 구성하였으며 실제 자료와 생성된 자료에 대하여 제안한 모델의 성능을 측정하였다.