{"title":"EPI-HAN:利用层次注意网络识别增强子启动子相互作用","authors":"Fatma S. Ahmed, Saleh Aly, X. Liu","doi":"10.2174/0115748936294743240524113731","DOIUrl":null,"url":null,"abstract":"\n\nEnhancer-Promoter Interaction (EPI) recognition is crucial for understanding\nhuman development and transcriptional regulation. EPI in the genome plays a significant role in\nregulating gene expression. In Genome-Wide Association Studies (GWAS), EPIs help to improve\nthe mechanistic understanding of disease- or trait-associated genetic variants.\n\n\n\nExperimental methods for classifying EPIs are time-consuming and expensive. Consequently,\nthere has been a growing emphasis on research focused on developing computational approaches\nthat leverage deep learning and other machine learning techniques. One of the main challenges\nin EPI prediction is the long sequences of enhancers and promoters, which most existing computational\napproaches struggle with. This paper proposes a new deep learning model based on the Hierarchical\nAttention Network (HAN) for EPI detection. The proposed EPI-HAN model has two\nunique features: (i) a hybrid embedding strategy (ii) a hierarchical HAN structure comprising two\nattention layers that operate at both the individual token and smaller sequence levels.\n\n\n\nIn benchmark comparisons, the EPI-HAN model demonstrates superior performance over\nstate-of-the-art methods, as evidenced by AUROC and AUPR metrics for specific cell lines. Specifically,\nfor the cell lines HeLa-S3, HUVEC, and NHEK, the AUROC values are 0.962, 0.946, and\n0.987, respectively, and the AUPR values are 0.842, 0.724, and 0.926, respectively.\n\n\n\nThe comparative results indicate that our model surpasses other state-of-the-art models\nin three out of six cell lines. The Superior performance in recognizing EPIs is attributed to the hierarchical\nstructure of the attention mechanism.\n","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":"23 8","pages":""},"PeriodicalIF":4.6000,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"EPI-HAN: Identification of Enhancer Promoter Interaction Using Hierarchical Attention Network\",\"authors\":\"Fatma S. Ahmed, Saleh Aly, X. Liu\",\"doi\":\"10.2174/0115748936294743240524113731\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\nEnhancer-Promoter Interaction (EPI) recognition is crucial for understanding\\nhuman development and transcriptional regulation. EPI in the genome plays a significant role in\\nregulating gene expression. In Genome-Wide Association Studies (GWAS), EPIs help to improve\\nthe mechanistic understanding of disease- or trait-associated genetic variants.\\n\\n\\n\\nExperimental methods for classifying EPIs are time-consuming and expensive. Consequently,\\nthere has been a growing emphasis on research focused on developing computational approaches\\nthat leverage deep learning and other machine learning techniques. One of the main challenges\\nin EPI prediction is the long sequences of enhancers and promoters, which most existing computational\\napproaches struggle with. This paper proposes a new deep learning model based on the Hierarchical\\nAttention Network (HAN) for EPI detection. The proposed EPI-HAN model has two\\nunique features: (i) a hybrid embedding strategy (ii) a hierarchical HAN structure comprising two\\nattention layers that operate at both the individual token and smaller sequence levels.\\n\\n\\n\\nIn benchmark comparisons, the EPI-HAN model demonstrates superior performance over\\nstate-of-the-art methods, as evidenced by AUROC and AUPR metrics for specific cell lines. Specifically,\\nfor the cell lines HeLa-S3, HUVEC, and NHEK, the AUROC values are 0.962, 0.946, and\\n0.987, respectively, and the AUPR values are 0.842, 0.724, and 0.926, respectively.\\n\\n\\n\\nThe comparative results indicate that our model surpasses other state-of-the-art models\\nin three out of six cell lines. The Superior performance in recognizing EPIs is attributed to the hierarchical\\nstructure of the attention mechanism.\\n\",\"PeriodicalId\":2,\"journal\":{\"name\":\"ACS Applied Bio Materials\",\"volume\":\"23 8\",\"pages\":\"\"},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2024-06-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACS Applied Bio Materials\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.2174/0115748936294743240524113731\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, BIOMATERIALS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.2174/0115748936294743240524113731","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
EPI-HAN: Identification of Enhancer Promoter Interaction Using Hierarchical Attention Network
Enhancer-Promoter Interaction (EPI) recognition is crucial for understanding
human development and transcriptional regulation. EPI in the genome plays a significant role in
regulating gene expression. In Genome-Wide Association Studies (GWAS), EPIs help to improve
the mechanistic understanding of disease- or trait-associated genetic variants.
Experimental methods for classifying EPIs are time-consuming and expensive. Consequently,
there has been a growing emphasis on research focused on developing computational approaches
that leverage deep learning and other machine learning techniques. One of the main challenges
in EPI prediction is the long sequences of enhancers and promoters, which most existing computational
approaches struggle with. This paper proposes a new deep learning model based on the Hierarchical
Attention Network (HAN) for EPI detection. The proposed EPI-HAN model has two
unique features: (i) a hybrid embedding strategy (ii) a hierarchical HAN structure comprising two
attention layers that operate at both the individual token and smaller sequence levels.
In benchmark comparisons, the EPI-HAN model demonstrates superior performance over
state-of-the-art methods, as evidenced by AUROC and AUPR metrics for specific cell lines. Specifically,
for the cell lines HeLa-S3, HUVEC, and NHEK, the AUROC values are 0.962, 0.946, and
0.987, respectively, and the AUPR values are 0.842, 0.724, and 0.926, respectively.
The comparative results indicate that our model surpasses other state-of-the-art models
in three out of six cell lines. The Superior performance in recognizing EPIs is attributed to the hierarchical
structure of the attention mechanism.
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.