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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)

Current Result Document :

ÇѱÛÁ¦¸ñ(Korean Title) º£ÀÌÁö¾È ±â¹ýÀ» Àû¿ëÇÑ ¸¶ÀÌÅ©·Î¾î·¹ÀÌ µ¥ÀÌÅÍ ºÐ·ù ¾Ë°í¸®Áò ¼³°è¿Í ±¸Çö
¿µ¹®Á¦¸ñ(English Title) The Algorithm Design and Implement of Microarray Data Classification using the Byesian Method
ÀúÀÚ(Author) ¹Ú¼ö¿µ   Á¤Ã¤¿µ   Su-Young Park   Chai-Yeoung Jung  
¿ø¹®¼ö·Ïó(Citation) VOL 10 NO. 12 PP. 2283 ~ 2288 (2006. 12)
Çѱ۳»¿ë
(Korean Abstract)
ÃÖ±Ù »ý¸í Á¤º¸ÇÐ ±â¼úÀÇ ¹ß´Þ·Î ¸¶ÀÌÅ©·Î ´ÜÀ§ÀÇ ½ÇÇèÁ¶ÀÛÀÌ °¡´ÉÇØÁü¿¡ µû¶ó ÇϳªÀÇ chip»ó¿¡¼­ Àüü genomeÀÇ expression patternÀ» °üÂûÇÒ ¼ö ÀÖ°Ô µÇ¾ú°í, µ¿½Ã¿¡ ¼ö ¸¸°³ÀÇ À¯ÀüÀÚµé °£ÀÇ »óÈ£ÀÛ¿ëµµ ¿¬±¸ °¡´ÉÇÏ°Ô µÇ¾ú´Ù. ÀÌó·³ DNA ¸¶ÀÌÅ©·Î¾î·¹ÀÌ ±â¼úÀº º¹ÀâÇÑ »ý¹°Ã¼¸¦ ÀÌÇØÇÏ´Â »õ·Î¿î ¹æÇâÀ» Á¦½ÃÇØÁÖ°Ô µÇ¾ú´Ù. µû¶ó¼­ ÀÌ·¯ÇÑ ±â¼úÀ» ÅëÇØ ¾ò¾îÁø ´ë·®ÀÇ À¯ÀüÀÚ Á¤º¸µéÀ» È¿°úÀûÀ¸·Î ºÐ¼®ÇÏ´Â ¹æ¹ýÀÌ ½Ã±ÞÇÏ´Ù. º» ³í¹®¿¡¼­´Â ½ÇÇè¿ë µ¥ÀÌÅÍ·Î ÇϹöµå´ëÇб³ÀÇ ¹ÙÀÌ¿ÀÀÎÆ÷¸Þƽ½º ÄÚ¾î ±×·ìÀÇ »ùÇõ¥ÀÌÅÍ ÀÌ¿ëÇÏ¿© ¸¶ÀÌÅ©·Î¾î·¹ÀÌ ½ÇÇè¿¡¼­ ´Ù¾çÇÑ ¿øÀο¡ ÀÇÇØ ¹ß»ýÇÏ´Â ÀâÀ½(noise)À» ÁÙÀ̰ųª Á¦°ÅÇÏ´Â °úÁ¤ÀΠǥÁØÈ­ °úÁ¤À» °ÅÃÄ Æ¯Â¡ ÃßÃâ¹æ¹ýÀÎ º£ÀÌÁö¾È ¾Ë°í¸®Áò ASA(Adaptive Simulated Annealing) ¹æ¹ýÀ» ÀÌ¿ëÇÏ¿© µ¥ÀÌÅ͸¦ 2°³ÀÇ Å¬·¡½º·Î ³ª´©°í, Á¤È®µµ¸¦ Æò°¡ÇÏ´Â ½Ã½ºÅÛÀ» ¼³°èÇÏ°í ±¸ÇöÇÏ¿´´Ù. Lowess Ç¥ÁØÈ­ ÈÄ 98.23%ÀÇ Á¤È®µµ¸¦ º¸¿´´Ù.
¿µ¹®³»¿ë
(English Abstract)
As development in technology of bioinformatics recently makes it possible to operate micro-level experiments, we can observe the expression pattern of total genome through on chip and analyze the interactions of thousands of genes at the same time. Thus, DNA microarray technology presents the new directions of understandings for complex organisms. Therefore, it is required how to analyze the enormous gene information obtained through this technology effectively. In this thesis, We used sample data of bioinformatics core group in harvard university. It designed and implemented system that evaluate accuracy after dividing in class of two using Bayesian algorithm, ASA, of feature extraction method through normalization process, reducing or removing of noise that occupy by various factor in microarray experiment. It was represented accuracy of 98.23% after Lowess normalization.
Å°¿öµå(Keyword) microarray expression data   normalization   ASA(Adaptive Simulated Annealing)  
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