Á¤º¸°úÇÐȸ³í¹®Áö (Journal of KIISE)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
´º·² ³×Æ®¿öÅ© ±â¹ÝÀÇ ´ÙÁß ¿À¹Í½º ÅëÇÕ À¯¹æ¾Ï ¼ºêŸÀÔ ºÐ·ù |
¿µ¹®Á¦¸ñ(English Title) |
Breast Cancer Subtype Classification Using Multi-omics Data Integration Based on Neural Network |
ÀúÀÚ(Author) |
ÃÖÁ¤¹Î
ÀÌÁö¿µ
±èÁöÀº
±èÁöÇö
Joungmin Choi
Jiyoung Lee
äÈñÁØ
Jihyun Kim
Heejoon Chae
Jieun Kim
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¿ø¹®¼ö·Ïó(Citation) |
VOL 47 NO. 09 PP. 0835 ~ 0841 (2020. 09) |
Çѱ۳»¿ë (Korean Abstract) |
À¯¹æ¾ÏÀº ´Ù¾çÇÑ »ý¹°ÇÐÀû ¿ä¼Ò·Î ±¸¼ºµÈ º¹ÀâÇÑ Áúº´À¸·Î, ¿©·¯ ºÐÀÚÀû ¼ºê ŸÀÔÀ» À¯¹ßÇÑ´Ù. Á¤È®ÇÑ ¼ºê ŸÀÔ ¿¹ÃøÀº ¾ÏÀÇ ¿¹ÈÄ¿¡ Áß´ëÇÑ ¿µÇâÀ» °¡Áö¸ç, ¼ºê ŸÀÔº° Ä¡·á¹ý Á¦°øÀ» ÅëÇÑ È¯ÀÚÀÇ »ýÁ¸À² Çâ»ó¿¡ Áß¿äÇϳª, »ý¹°ÇÐÀû ÀÌÁú¼ºÀ¸·Î ÀÎÇØ ½±Áö ¾Ê´Ù. ÃÖ±Ù, À¯Àüü ¹× Èļº À¯Àüü µ¥ÀÌÅ͸¦ ó¸®Çϱâ À§ÇØ ¸Ó½Å·¯´× ¸ðµ¨µéÀÌ À¯¹æ¾Ï ºÐ·ù¿¡ Àû¿ëµÇ¾úÀ¸¸ç, ƯÈ÷ ´ÙÁß ¿À¹Í½º¸¦ È°¿ëÇÑ ¿¬±¸µéÀÌ Á¦½ÃµÇ¾ú´Ù. ÇÏÁö¸¸, ³ôÀº Â÷¿ø°ú º¹À⼺À¸·Î ÀÎÇØ Æ¯Â¡ ºÐ¼® ¹× ºÐ·ù Á¤È®¼º¿¡ ÇѰ踦 °®´Â´Ù. º» ³í¹®¿¡¼´Â ´º·² ³×Æ®¿öÅ©¸¦ ±â¹ÝÀ¸·Î ´ÙÁß ¿À¹Í½º ÅëÇÕ µ¥ÀÌÅ͸¦ È°¿ëÇÑ À¯¹æ¾Ï ¼ºê ŸÀÔ ºÐ·ù ¸ðµ¨À» Á¦½ÃÇÑ´Ù. À¯ÀüÀÚ ¹ßÇö, DNA ¸ÞÆ¿·¹À̼Ç, ±×¸®°í miRNA ¿À¹Í½º¸¦ ÅëÇÕÇÑ µ¥ÀÌÅÍ·Î ºÐ·ù ¸ðµ¨À» ±¸ÃàÇÏ¿´À¸¸ç, ¼º´É ºñ±³ °á°ú, Æò±Õ 90.45%ÀÇ Á¤È®µµ·Î ±âÁ¸ ¿¬±¸º¸´Ù ³ôÀº ¼º´ÉÀ» º¸¿´´Ù. Á¦¾ÈµÈ ¸ðµ¨À» ÅëÇØ Á¤È®ÇÑ À¯¹æ¾Ï ȯÀÚÀÇ ¼ºê ŸÀÔ ¿¹ÃøÀ» ±â¹ÝÀ¸·Î ȯÀÚÀÇ ¿¹ÈÄ Çâ»ó¿¡ µµ¿òÀ» ÁÙ °ÍÀ¸·Î ±â´ëµÈ´Ù.
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¿µ¹®³»¿ë (English Abstract) |
Breast cancer is one of the highly heterogeneous diseases comprising multiple biological factors, causing multiple subtypes. Early diagnosis and accurate subtype prediction of breast cancer play a critical role in the prognosis of cancer and are crucial to providing appropriate treatment for each patient with different subtypes. To identify significant patterns from enormous volumes of genetic and epigenetic data, machine learning-based methods have been adopted to the breast cancer subtype classification. Recently, multi-omics data integration has attracted much attention as a promising approach in recognizing complex molecular mechanisms and providing a comprehensive view of patients. However, because of the characteristics of high dimensionality, multi-omics based approaches are limited in prediction accuracy. In this paper, we propose a neural network-based breast cancer subtype classification model using multi-omics data integration. The gene expression, DNA methylation, and miRNA omics dataset were integrated after preprocessing and the classification model was trained based on the neural network using the dataset. Our performance evaluation results showed that the proposed model outperforms all other methods, providing the highest classification accuracy of 90.45%. We expect this model to be useful in predicting the subtypes of breast cancer and improving patients¡¯ prognosis.
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Å°¿öµå(Keyword) |
À¯¹æ¾Ï
´ÙÁß ¿À¹Í½º ÅëÇÕ
´º·² ³×Æ®¿öÅ©
ºÐ·ù ¸ðµ¨
breast cancer
classification model
multi-omics data integration
neural network
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