副教授 同专业博导 同专业硕导
主要任职：教学和科研
其他任职：中国计算机协会(CCF)高级会员（No.73301S），中国计算机协会(CCF)专委会委员；人工智能学会生命与健康协会会员（BIIP，No.E661506634M），人工智能学会生命与健康专委委员；中国生物工程学会计算生物学与生物信息学协会会员（No.E441504848Z），中国生物工程学会计算生物学与生物信息学专委委员；山东生物信息学学会理事，国家自然基金委青年和面上项目评审专家；全国硕士毕业论文评审专家；《Briefings in Bioinformatics》、《计算机学报》等国内外二十余个SCI期刊评审人和客座编辑
性别：男
毕业院校：西安电子科技大学
学历：博士研究生毕业
学位：博士生
在职信息：在职
所在单位：软件学院
入职时间：2020-10-12
学科：软件工程其他专业
计算机应用技术
计算机科学与技术
办公地点：山东大学软件园校区
山东省济南市高新区舜华路1500号
联系方式：邮箱：haowu@sdu.edu.cn
电子邮箱：haowu@sdu.edu.cn
学术荣誉：邮编 : 250101
邮箱 : haowu@sdu.edu.cn
2021-12-20曾获荣誉当选： 山东大学软件学院优秀教师
2020-12-20曾获荣誉当选： 山东大学教师教学创新大赛三等奖
2018-07-10曾获荣誉当选： 西北农林科技大学信息工程学院本科毕业设计优秀指导教师
2016-12-25曾获荣誉当选： 西北农林科技大学青年教师讲课比赛二等奖，比赛现场评分第一名
2016-12-30曾获荣誉当选： 西北农林科技大学信息工程学院“特殊课时津贴”奖
2013-06-10曾获荣誉当选： 教改项目《计算机网络应用技术课程建设改革与实践》荣获西北农林科技大学教学成果二等奖
2009-10-10曾获荣誉当选： 西北农林科技大学 “优秀班主任”
2008-12-30曾获荣誉当选： 西北农林科技大学“校级优秀教师”
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所属单位：山东大学
教研室：高性能中心
发表刊物：Briefings in Functional Genomics
刊物所在地：英国
项目来源：国家自然科学基金
关键字：Differential Expression Genes，Protein Interaction Networks， Dysregulated Modules
摘要：In the human genome, abnormal gene expression usually causes gene mutations, leading to abnormal gene networks, and then triggering certain complex diseases. At the bio-molecular level, gene sets composed of differential expression genes usually lead to dysregulation of the biological pathways that they regulate. Therefore, differential expression genes are usually used as biomarkers for early diagnosis of disease in clinical practice. Detection of cancer-causing dysregulated modules provides a new perspective to study the mechanism of cancer. However, it has become a big challenge in cancer research how to accurately and effectively detect dysregulated modules that promote diseases in massive data. In this study, we propose a dysregulated module detection method (Netkmeans) based on a network model, which integrates differential expression genes and human protein interaction networks. This method integrates the characteristics of high mutual exclusivity, high coverage and complex network topology among genes widely existed in the genome. Firstly, the study constructs an undirected-weighted gene interaction network based on the above three characteristics. Secondly, the study constructs a comprehensive evaluation function to select the number of clusters scientifically and effectively. Finally, the K-means clustering method is applied to perform cluster analysis and obtain the optimal dysregulated modules. Compared with the differential expression genes detected by IBA and CCEN methods, the results of the Netkmeans have higher statistical significance and biological relevance. Besides, compared with the dysregulated modules detected by MCODE, CFinder and ClusterONE, the results of Netkmeans have higher accuracy, precision and F-measure values. The experimental results show that the multiple dysregulated modules detected by Netkmeans play an important role in the generation, development and progression of cancer, and thus they play a vital role in the precise diagnosis, treatment and new drug development for cancer patients.
全部作者：Biting Liang,Yingfu Wu,Hongming Zhang,Quanzhong Liu
第一作者：Zhongli Chen
论文类型：期刊论文
通讯作者：Hao Wu*
论文编号：10.1093/bfgp/elac010
学科门类：工学
一级学科：软件工程
文献类型：J
卷号：4
期号：23
是否译文：否
发表时间：2022-04-01
收录刊物：SCI
