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发布者壬:文明办发布时间藕磕杉:2019-06-27浏览次数绩创间:259


主讲人衫:刘丙强 山东大学教授 博士生导师


时间坊凌:2019年6月29日14洪械:20


地点豹每揭:三号楼332会议厅


举办单位杜:数理学院


主讲人介绍呕:山东大学数学学院教授饯青寞、博士生导师毕帕疟。所在学科为运筹学与控制论爸,研究方向为组合最优化与生物信息学臂昆。2003年毕业于山东大学数学学院基础数学专业些惦,获学士学位锑矾吐。2010年毕业于山东大学数学学院运筹学与控制论专业忙杆诵,获博士学位襄呜膳。其间于2007年1月至2010年1月赴美国乔治亚大学联合培养矗,研究方向为生物信息学理。2010年留校任教妹,2013年任山东大学数学学院副教授犀,2017年任教授完涕灸。主要研究方向为利用图与组合优化的模型与理论针对生物信息学问题进行算法设计与数据分析衫玫跨,研究课题包括转录因子结合位点计算预测虽迫街、表达数据分析汹撬、调控网络构建等等规撕赦。


内容介绍睦琅奢:Identification of transcription units (TUs) encoded in prokaryotes is essential  to predict the function of unknown genes, annotate the prokaryotic genome and  construct the transcriptional and translation regulatory networks at the gene  level. The alternative transcription units (ATUs) are the dynamic TUs from a  cluster of genes. The identification of ATUs is recognized as a more challenging  computational problem due to their condition-dependent nature, and the next  generation sequencing technique provided a good opportunity. We are trying to  develop a method to predict ATUs in prokaryotes based on RNA-seq data. The  problem was described as a mathematical programming model, along with the  integrating of other factors including RNA degradation effect, cross-gene reads.  We tested the methods with two RNA-seq data on E.coli genome and compared the  predicted ATUs with experimentally validated ATUs from previous studies. The  comparison results show that our algorithm can北京赛车投注这是最大的悲哀。你就会知道我在哪里。 recover the majority of  previously known ATUs with average precision of 0.70/0.66 and recall of  0.77/0.79 on two datasets. As the first de novo computational ATU prediction  pipeline, the new method will facilitate the research on complex mechanism of  transcriptional regulation, and bring more attention to the function of  alternative transcription units in prokaryotic genomes.

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