赵仲明(下文简称"赵"):周老师好!时光荏苒。您在美国创办和主编的英文学报《音乐中国》(Journal of Music in China),从1999年创刊号算起,迄今已经超过了20个年头。我一直关注这份刊物,前不久还在一篇文章中呼吁过正视您所默...赵仲明(下文简称"赵"):周老师好!时光荏苒。您在美国创办和主编的英文学报《音乐中国》(Journal of Music in China),从1999年创刊号算起,迄今已经超过了20个年头。我一直关注这份刊物,前不久还在一篇文章中呼吁过正视您所默默做出的特殊贡献。或许由于这个原因,《人民音乐》编辑部约我为《音乐中国》今年第10卷出版写一篇文章。但20年前您曾与于庆新先生有过一番对话,。因此我想仍以访谈的方式,请您谈谈之后的新情况、新问题,会更有意义。展开更多
Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases.Several computational algorithms have recently been developed for this purpose ...Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases.Several computational algorithms have recently been developed for this purpose by using transcriptome and network data.However,it remains largely unclear which algorithm performs better under a specific condition.Such knowledge is important for both appropriate application and future enhancement of these algorithms.Here,we systematically evaluated seven main algorithms(TED,TDD,TFactS,RIF1,RIF2,dCSA_t2t,and dCSA_r2t),using both simulated and real datasets.In our simulation evaluation,we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators.We found that all these algorithms could effectively discern signals arising from regulatory network differences,indicating the validity of our simulation schema.Among the seven tested algorithms,TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered.When applied to two independent lung cancer datasets,both TED and TFactS replicated a substantial fraction of their respective differential regulators.Since TED and TFactS rely on two distinct features of transcriptome data,namely differential co-expression and differential expression,both may be applied as mutual references during practical application.展开更多
文摘赵仲明(下文简称"赵"):周老师好!时光荏苒。您在美国创办和主编的英文学报《音乐中国》(Journal of Music in China),从1999年创刊号算起,迄今已经超过了20个年头。我一直关注这份刊物,前不久还在一篇文章中呼吁过正视您所默默做出的特殊贡献。或许由于这个原因,《人民音乐》编辑部约我为《音乐中国》今年第10卷出版写一篇文章。但20年前您曾与于庆新先生有过一番对话,。因此我想仍以访谈的方式,请您谈谈之后的新情况、新问题,会更有意义。
基金partially supported by US National Institutes of Health(R01LM011177,R03CA167695,P30CA68485,P50CA095103 and P50CA098131)Ingram Professorship Funds(to Zhao ZhongMing)The Robert J.Kleberg,Jr.and Helen C.Kleberg Foundation(to Zhao ZhongMing)
文摘Identification of differential regulators is critical to understand the dynamics of cellular systems and molecular mechanisms of diseases.Several computational algorithms have recently been developed for this purpose by using transcriptome and network data.However,it remains largely unclear which algorithm performs better under a specific condition.Such knowledge is important for both appropriate application and future enhancement of these algorithms.Here,we systematically evaluated seven main algorithms(TED,TDD,TFactS,RIF1,RIF2,dCSA_t2t,and dCSA_r2t),using both simulated and real datasets.In our simulation evaluation,we artificially inactivated either a single regulator or multiple regulators and examined how well each algorithm detected known gold standard regulators.We found that all these algorithms could effectively discern signals arising from regulatory network differences,indicating the validity of our simulation schema.Among the seven tested algorithms,TED and TFactS were placed first and second when both discrimination accuracy and robustness against data variation were considered.When applied to two independent lung cancer datasets,both TED and TFactS replicated a substantial fraction of their respective differential regulators.Since TED and TFactS rely on two distinct features of transcriptome data,namely differential co-expression and differential expression,both may be applied as mutual references during practical application.
