{"title":"用于读取DNA序列自射线图的多层感知器特征提取器","authors":"M. Murdock, N. Cotter, R. Gesteland","doi":"10.1109/NNSP.1991.239485","DOIUrl":null,"url":null,"abstract":"The authors report on the application of the three-layer, backward error propagation neural network to the problem of reading sequenced DNA autoradiograms. The network is used for band identification by extracting two features: band intensity level and band intensity gradient. A training set of 16000 12*12 gray scale patterns is generated. Trained with these patterns, the network successfully learned to identify the degree of presence and absence of these two low level features.<<ETX>>","PeriodicalId":354832,"journal":{"name":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","volume":"245 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1991-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A multilayer perceptron feature extractor for reading sequenced DNA autoradiograms\",\"authors\":\"M. Murdock, N. Cotter, R. Gesteland\",\"doi\":\"10.1109/NNSP.1991.239485\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The authors report on the application of the three-layer, backward error propagation neural network to the problem of reading sequenced DNA autoradiograms. The network is used for band identification by extracting two features: band intensity level and band intensity gradient. A training set of 16000 12*12 gray scale patterns is generated. Trained with these patterns, the network successfully learned to identify the degree of presence and absence of these two low level features.<<ETX>>\",\"PeriodicalId\":354832,\"journal\":{\"name\":\"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop\",\"volume\":\"245 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1991-09-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/NNSP.1991.239485\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/NNSP.1991.239485","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A multilayer perceptron feature extractor for reading sequenced DNA autoradiograms
The authors report on the application of the three-layer, backward error propagation neural network to the problem of reading sequenced DNA autoradiograms. The network is used for band identification by extracting two features: band intensity level and band intensity gradient. A training set of 16000 12*12 gray scale patterns is generated. Trained with these patterns, the network successfully learned to identify the degree of presence and absence of these two low level features.<>
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