{"title":"基因- cnv:模拟DNA拷贝数变异全基因组共现的布尔隐含网络","authors":"Salvi Singh, N. Guo","doi":"10.1145/3233547.3233652","DOIUrl":null,"url":null,"abstract":"Boolean implication networks (Genet) have been utilized to model gene co-expression networks in our previous research. In this study, they are constructed to model the co-occurrence of amplification/deletion events in DNA copy number variations (CNVs) at a genome-wide scale. The Boolean implication scheme extends the dichotomous nature of the variable under scrutiny such that it can have numerous discrete values corresponding to DNA CNVs, and pairwise co-occurrence of CNVs is computed. The implication network was implemented in a software package (Genet-CNV) and run on 271 patient samples afflicted with non-small cell lung cancer (NSCLC )[GSE31800].","PeriodicalId":131906,"journal":{"name":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Genet-CNV: Boolean Implication Networks for Modelling Genome-Wide Co-occurrence of DNA Copy Number Variations\",\"authors\":\"Salvi Singh, N. Guo\",\"doi\":\"10.1145/3233547.3233652\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Boolean implication networks (Genet) have been utilized to model gene co-expression networks in our previous research. In this study, they are constructed to model the co-occurrence of amplification/deletion events in DNA copy number variations (CNVs) at a genome-wide scale. The Boolean implication scheme extends the dichotomous nature of the variable under scrutiny such that it can have numerous discrete values corresponding to DNA CNVs, and pairwise co-occurrence of CNVs is computed. The implication network was implemented in a software package (Genet-CNV) and run on 271 patient samples afflicted with non-small cell lung cancer (NSCLC )[GSE31800].\",\"PeriodicalId\":131906,\"journal\":{\"name\":\"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics\",\"volume\":\"22 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-08-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3233547.3233652\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3233547.3233652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genet-CNV: Boolean Implication Networks for Modelling Genome-Wide Co-occurrence of DNA Copy Number Variations
Boolean implication networks (Genet) have been utilized to model gene co-expression networks in our previous research. In this study, they are constructed to model the co-occurrence of amplification/deletion events in DNA copy number variations (CNVs) at a genome-wide scale. The Boolean implication scheme extends the dichotomous nature of the variable under scrutiny such that it can have numerous discrete values corresponding to DNA CNVs, and pairwise co-occurrence of CNVs is computed. The implication network was implemented in a software package (Genet-CNV) and run on 271 patient samples afflicted with non-small cell lung cancer (NSCLC )[GSE31800].