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svMIL: predicting the pathogenic effect of TAD boundary-disrupting somatic structural variants through multiple instance learning
Bioinformatics ( IF 5.8 ) Pub Date : 2020-12-29 , DOI: 10.1093/bioinformatics/btaa802 Marleen M. Nieboer 1 , Jeroen de Ridder 1
Bioinformatics ( IF 5.8 ) Pub Date : 2020-12-29 , DOI: 10.1093/bioinformatics/btaa802 Marleen M. Nieboer 1 , Jeroen de Ridder 1
Affiliation
Despite the fact that structural variants (SVs) play an important role in cancer, methods to predict their effect, especially for SVs in non-coding regions, are lacking, leaving them often overlooked in the clinic. Non-coding SVs may disrupt the boundaries of Topologically Associated Domains (TADs), thereby affecting interactions between genes and regulatory elements such as enhancers. However, it is not known when such alterations are pathogenic. Although machine learning techniques are a promising solution to answer this question, representing the large number of interactions that an SV can disrupt in a single feature matrix is not trivial.
中文翻译:
svMIL:通过多实例学习预测TAD破坏边界的体细胞结构变异的致病作用
尽管结构变异(SV)在癌症中起着重要作用,但仍缺乏预测其作用的方法,尤其是对于非编码区的SV,这在临床上经常被忽视。非编码SV可能会破坏拓扑相关域(TAD)的边界,从而影响基因与调控元件(如增强子)之间的相互作用。但是,尚不清楚这种改变何时是致病的。尽管机器学习技术是回答该问题的有前途的解决方案,但代表SV可以在单个特征矩阵中破坏的大量交互并不是微不足道的。
更新日期:2020-12-31
中文翻译:
svMIL:通过多实例学习预测TAD破坏边界的体细胞结构变异的致病作用
尽管结构变异(SV)在癌症中起着重要作用,但仍缺乏预测其作用的方法,尤其是对于非编码区的SV,这在临床上经常被忽视。非编码SV可能会破坏拓扑相关域(TAD)的边界,从而影响基因与调控元件(如增强子)之间的相互作用。但是,尚不清楚这种改变何时是致病的。尽管机器学习技术是回答该问题的有前途的解决方案,但代表SV可以在单个特征矩阵中破坏的大量交互并不是微不足道的。