• Journal of KIISE:Software and Applications
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• v.35 no.6
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• pp.347-356
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• 2008

High-throughput microarray is one of the most popular tools in molecular biology, and various computational methods have been developed for the microarray data analysis. While the computational methods easily extract significant features, it suffers from inferring modules of multiple co-regulated genes. Hypernetworhs are motivated by biological networks, which handle all elements based on their combinatorial processes. Hence, the hypernetworks can naturally analyze the biological effects of gene combinations. In this paper, we introduce a hypernetwork classifier for microRNA (miRNA) profile analysis based on microarray data. The hypernetwork classifier uses miRNA pairs as elements, and an evolutionary learning is performed to model the microarray profiles. miTNA modules are easily extracted from the hypernetworks, and users can directly evaluate if the miRNA modules are significant. For experimental results, the hypernetwork classifier showed 91.46% accuracy for miRNA expression profiles on multiple human canters, which outperformed other machine learning methods. The hypernetwork-based analysis showed that our approach could find biologically significant miRNA modules.

• Journal of KIISE:Computing Practices and Letters
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• v.15 no.12
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• pp.983-987
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• 2009

One of the critical themes in the language acquisition is its exposure to linguistic environments. Linguistic environments, which interact with infants, include not only human beings such as its parents but also artificially crafted linguistic media as their functioning elements. An infant learns a language by exploring these extensive language environments around it. Based on such large linguistic data exposure, we propose a machine learning based method on the cognitive mechanism that simulate flexibly and appropriately infant's language learning. The infant's initial stage of language learning comes with sentence learning and creation, which can be simulated by exposing it to a language corpus. The core of the simulation is a memory-based learning model which has language hypernetwork structure. The language hypernetwork simulates developmental and progressive language learning using the structure of new data stream through making it representing of high level connection between language components possible. In this paper, we simulates an infant's gradual and developmental learning progress by training language hypernetwork gradually using 32,744 sentences extracted from video scripts of commercial animation movies for children.

• Proceedings of the Korean Information Science Society Conference
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• 2010.06c
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• pp.235-239
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• 2010

MicroRNA는 RNA로 전사된 유전자와의 상보결합을 통해 유전자 발현을 억제하는 조절인자이다. MicroRNA 생성과정에서 pre-microRNA의 3' 또는 5' 부근의 strand가 선택되어 mature 시퀀스가 되고 유전자 조절에 직접 작용하게 된다. 하지만 어떤 특징을 가진 strand가 선택 되는지에 대한 정확한 메커니즘은 아직 연구되어 있지 않다. 본 논문에서는 microRNA 시퀀스 정보를 바탕으로 하이퍼망을 구성하여 strand 선택 예측 모델을 구축하였다. 실험 결과 하이퍼망 학습을 통해 microRNA strand 선택에 중요한 영향을 미치는 시퀀스 특징을 찾을 수 있었고, strand 선택을 높은 정확도로 예측할 수 있음을 확인하였다.