Limin Li

Work place: Department of Mathematics, Xi’an Jiaotong University,Xi’ an, China



Research Interests: Bioinformatics, Computational Biology, Data Mining, Data Structures and Algorithms


Limin Li got her B.S. in mathematics (2004) from Zhejiang University, M.S. in applied mathematics (2006) from Zhejiang University and Ph.D. in computational mathematics(2010) from the university of Hong Kong. She has been a research fellow at Center for Biotechnology and InformaticsThe Methodist Hospital Research Institute,Weill Cornell Medical College Houston, USA,Soka UniversityJapan and Pathway engineeringLaboratory,BioinformatiesCenterKyoto University, Japan. Currently she is an assistant professor in Institute of information ane Sustem Science. Faculty of Seience. Xi'an Jiaotong University and a post-doc.fellow in Max Planck institute for biological eyberneticsTuebingenGermany.Her research interests are Bioinformatics andComputational Biology, matrix computation and numerical linear algebra. manifold learning,data mining and data modeling.

Author Articles
On Construction of Gene-PDB Structure Mapping with Applications in Functional Annotation of Human Genes

By Xi Chen Hao Jiang Wai-Ki Ching Limin Li

DOI:, Pub. Date: 8 Mar. 2011

Protein 3D structure is one of the key factors in recognizing gene functions. The availability of protein structure data in Protein Data Bank (PDB) enables us to conduct gene function analysis. However, the molecules in the PDB, whose structures have been determined, are always not corresponding to a unique gene. That is to say, the mapping from gene to PDB is not one-to-one. Thus this uncertain property complicates the analysis and increases the difficulty of gene function analysis. In this paper, we attempt to tackle this challenging issue and we study the problem of predicting gene function from protein structures based on the gene-PDB mapping. We first obtain the gene-PDB mapping, which is important in representing a gene by the structure set of all its corresponding PDB molecules. We then define a new gene-gene similarity measurement based on the structure similarity between PDB molecules. We further show that this new measurement matches with gene functional similarity nicely. This means that the measurement we introduced here can be useful for gene function prediction. Numerical examples are given to demonstrate our claim.

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