title: Predicting cancerlectins by the optimal g-gap dipeptides.
abstract: The cancerlectin plays a key role in the process of tumor cell differentiation. Thus, to fully understand the function of cancerlectin is significant because it sheds light on the future direction for the cancer therapy. However, the traditional wet-experimental methods were money- and time-consuming. It is highly desirable to develop an effective and efficient computational tool to identify cancerlectins. In this study, we developed a sequence-based method to discriminate between cancerlectins and non-cancerlectins. The analysis of variance (ANOVA) was used to choose the optimal feature set derived from the g-gap dipeptide composition. The jackknife cross-validated results showed that the proposed method achieved the accuracy of 75.19%, which is superior to other published methods. For the convenience of other researchers, an online web-server CaLecPred was established and can be freely accessed from the website http://lin.uestc.edu.cn/server/CalecPred. We believe that the CaLecPred is a powerful tool to study cancerlectins and to guide the related experimental validations.
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Hao
authors.initials: H
authors.lastname: Lin
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Wei-Xin
authors.initials: WX
authors.lastname: Liu
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Jiao
authors.initials: J
authors.lastname: He
authors.affiliation: School of Linguistics and Literature, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Xin-Hui
authors.initials: XH
authors.lastname: Liu
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Ding
authors.affiliation: Key Laboratory for Neuro-Information of Ministry of Education, Center of Bioinformatics, School of Life Science and Technology, Center for Information in Biomedicine, University of Electronic Science and Technology of China, Chengdu 610054, China.|Department of Physics, School of Sciences, and Center for Genomics and Computational Biology, North China University of Science and Technology, Tangshan 063000, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Chen
mesh_terms: D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D004151:Dipeptides / Q000737:chemistry*; D006801:Humans; D037102:Lectins / Q000737:chemistry* / Q000378:metabolism; D009369:Neoplasms / Q000378:metabolism*; D015203:Reproducibility of Results; D012984:Software; D064878:Web Browser
chemical_list: D004151:Dipeptides; D037102:Lectins