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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) ½Å°æ¸Á ±â¹ÝÀÇ À¯ÀüÀÚ Á¶ÇÕÀ» ÀÌ¿ëÇÑ ¸¶ÀÌÅ©·Î¾î·¹ÀÌ µ¥ÀÌÅÍ ºÐ·ù ½Ã½ºÅÛ
¿µ¹®Á¦¸ñ(English Title) The System Of Microarray Data Classification Using Significant Gene Combination Method based on Neural Network.
ÀúÀÚ(Author) ¹Ú¼ö¿µ   Á¤Ã¤¿µ   Su-Young Park   Chai-Yeoung Jung  
¿ø¹®¼ö·Ïó(Citation) VOL 12 NO. 07 PP. 1243 ~ 1248 (2008. 07)
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(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. º» ³í¹®¿¡¼­´Â ¾Ï¿¡ °É¸° ÈòÁã ¿ÜÇÇ ±â°£ ¼¼Æ÷ ºÐÈ­ ½ÇÇè¿¡¼­ ¾ò¾îÁø 3840 À¯ÀüÀÚÀÇ ¸¶ÀÌÅ©·Î¾î·¹ÀÌ cDNA¸¦ ÀÌ¿ëÇØ µ¥ÀÌÅÍÀÇ Á¤±ÔÈ­¸¦ °ÅÃÄ º» ³í¹®¿¡¼­ Á¦¾ÈÇÑ À¯»ç¼º ôµµ Á¶ÇÕ ¹æ¹ýÀ¸·Î Á¤º¸·Â ÀÖ´Â À¯ÀüÀÚµéÀ» ÃßÃâÇÑ ÈÄ, À¯»ç¼º ôµµ Á¶ÇÕ ¹æ¹ý°ú °áÇÕÇÑ ¸ÖƼÆÛ¼ÁÆ®·Ð ½Å°æ¸Á ºÐ·ù±â¿Í ±âÁ¸ÀÇ DT, NB, SVM ºÐ·ù±â¸¦ ÀÌ¿ëÇÏ¿© Ŭ·¡½º ºÐ·ù ½Ã½ºÅÛÀ» ±¸ÃàÇÏ°í, ¼º´ÉÀ» ºñ±³ºÐ¼®ÇÏ¿´´Ù. ÇǾ Àû·ü »ó°ü °è¼ö¿Í À¯Å¬¸®µð¾È °Å¸® °è¼ö Á¶ÇÕÀ» ÀÌ¿ëÇÏ¿© ¼±ÅÃµÈ 200 À¯ÀüÀÚµéÀ» ¸ÖƼÆÛ¼ÁÆ®·Ð ½Å°æ¸Á ºÐ·ù±â·Î ºÐ·ùÇÑ °á°ú 98.84%ÀÇ Á¤È®µµ¸¦ º¸¿© ´Ù¸¥ ºÐ·ù±â¸¦ ÀÌ¿ëÇÏ¿© ½ÇÇèÀ» ¼öÇàÇÑ °æ¿ìº¸´Ù Çâ»óµÈ ºÐ·ù ¼º´ÉÀ» º¸¿´´Ù.
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(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. In this thesis, we used cDNA microarrays of 3840 genes obtained from neuronal differentiation experiment of cortical stem cells on white mouse with cancer. It analyzed and compared performance of each of the experiment result using existing DT, NB, SVM and multi-perceptron neural network classifier combined the similar scale combination method after constructing class classification model by extracting significant gene list with a similar scale combination method proposed in this paper through normalization. Result classifying in Multi-Perceptron neural network classifier for selected 200 genes using combination of PC(Pearson correlation coefficient) and ED(Euclidean distance coefficient) represented the accuracy of 98.84%, which show that it improve classification performance than case to experiment using other classifier.
Å°¿öµå(Keyword) microarray   significant gene list   PC-ED   MLP(multi- Layer perceptiron)  
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