李霞
致力于复杂疾病功能基因识别方法的研究
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- 姓名:李霞
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学术头衔:
博士生导师
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学科领域:
生物物理学
- 研究兴趣:致力于复杂疾病功能基因识别方法的研究
李霞,哈尔滨医科大学生物信息科学与技术学院院长、教授、博士、博士生导师、黑龙江省优秀中青年专家、“龙江学者”特聘教授、北京“百千万人才工程”入选者。学术兼职有:国家自然科学基金评审专家、国家自然科学基金学科组评审专家、国家863项目专家、《中国生物工程》杂志理事、《生物信息学》、《生物物理学报》、《中华现代妇产科学》杂志编委、中国信息协会常务理事、黑龙江省自然科学基金评审专家、同济大学兼职教授(博士生导师)。
李霞教授作为生物信息学学科带头人,先后承担科研课题28项,其中国家863高科技计划项目2项(主持1项,副组长1项)、国家973前期项目1项(主持)、国家自然科学基金4项(主持3项),获省部级奖3项,厅局级奖8项,在国内外重要学术刊物和学术会议上发表学术论文110余篇(SCI 42篇、EI 18篇),累计SCI影响因子90.137,总引次数114,主编著作等8部。近年来,李霞教授致力于复杂疾病功能基因识别方法的研究,科学研究论文发表在国外著名生命科学杂志《Nucleic Acids Research》(SCI影响因子:7.26),《BMC Bioinformatics》(SCI影响因子:5.42),《Bioinformatics》、《BMC Genomics》、《Genomics》、《Journal of Medical Genetics》(SCI影响因子7.7)、《中国科学》、《Progress in Natural Science》等上,获得省政府自然科学2等奖两项,在复杂疾病基因作图的模式识别方法研究领域做出重要贡献,其中论文“Gene mining: a novel …”受到了Nobel奖得主Rich Roberts博士的好评,并迅速成为英国牛津出版集团旗舰科学杂志《Nucleic Acids Research》的热点文章(Hot Papers)。主编《计算分子生物学与基因组信息学》、《医学遗传学与遗传流行病学数据分析》等著作7部。研制的人类遗传群体与家系资料分析系统(PPAP)已推广到中国科学院、北京大学、上海第二医科大学等60余个单位,并已成功地应用于20多项国家自然科学基金课题与博士论文的数据信息分析工作。
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李霞, Xia Lil, Shaoqi Rao, Yadong Wang, and Binsheng Gong
Nucleic Acids Research, 2004, 9, (32): 2685~2694,-0001,():
-1年11月30日
Current applications of microarrays focus on precise classification or discovery of biological types, for example tumor versus normal phenotypes in cancer research. Several challenging scientific tasks in the post-genomic epoch, like hunting for the genes underlying complex diseases from genome-wide gene expression profiles and thereby building the corresponding gene networks, are largely overlooked because of the lack of an efficient analysis approach. We have thus developed an innovative ensemble decision approach, which can efficiently perform multiple gene mining tasks.An application of this approach to analyze two publicly available data sets (colon data and leukemia data) identified 20 highly significant colon cancer genes and 23 highly significant molecular signatures for refining the acute leukemia pheno-type, most of which have been verified either by biological experiments or by alternative analysis approaches. Furthermore,the globally optimal gene subsets identified by the novel approach have so far achieved the highest accuracy for classification of colon cancer tissue types. Establishment of this analysis strategy has offered the promise of advancing microarray technology as a means of deciphering the involved genetic complexities of complex diseases.
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【期刊论文】An ensemble method for gene discovery based on DNA microarray data
李霞, LI Xia, RAO Shaoqj, ZHANG Tianwenl, GU heng, ZHANG Qingpu, Kathy L. MOSER Eric J. TOPOL,
Science in China Ser. C Life Sciences, 2004, 47, (5) 396-405,-0001,():
-1年11月30日
The advent of DNA microarray technology has offered the promise of casting new insights onto deciphering secrets of life by monitoring activities of thousands of genes simultaneously.Current analyses of microarray data focus on precise classification of biological types,for example,tumor versus normal tissues. A further scientific challenging task is to extract disease-relevant genes from the bewildering amounts of raw data,which is one of the most critical themes in the post-genomic era,but it is generally ignored due to lack of an efficient approach.In this paper,we present a novel ensemble method for gene extraction that can be tailored to fulfill multiple biological tasks including (i) precise classification of biological types; (ii) disease gene mining; and (iii) target-d riven gene networking. We also give a numerical application for (i) and (ii) using a public microarrary data set and set aside a separate paper to address (iii).
microarrays,, ensemble decision,, recursive partition tree,, feature gene selection.,
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