Research Direction
Welcome to the Bioinformatics Lab of Inner Mongolia University. Our research interests mainly focus on:
Computational biology research: In this direction, we focus on the identification of important regulatory elements, such as enhancers, promoter and TFBSs from genomic sequences by integrating machine learning methods, feature selection techniques and expression omics data. In this area, we have published over 20 peer-reviewed papers in international journals. The K-MID (Amino Acids 2010, 38:859–867), KNN-ID(Amino Acids. 2013, 44(2):573-80; Mol Biosyst. 2015 11(3):950-7), Protein Block and PseKRAAC method have been firstly developed and extensively used in bioinformatics area(Peptides. 2009, 30(10)1788–1793; Plos one, 2015 10(12):e0145541; Bioinformatics. 2017, 33(1):122-124).
Computational genomics research: In this direction, we focus on the codons optimization of gene between different species, nucleotide synthesis of optimized gene, and redesign of regulatory functional elements based on computational genomics. In this area, a set of bioinformatics-based methods for the transformation of exogenous gene codons and tissue-specific expression regulatory elements was developed, and two National Invention Patents have been approved in the past years (Number：ZL201310344833.5 and ZL201410123506.1).
Bigdata of development and cell reprogramming: For development omics, we mainly focus on the regulation mechanism of embryonic development and cell reprogramming process for animals. Recently, we have successfully performed systematic comparisons based on differential co-expression analysis for preimplantation embryo development, and deciphered the spatiotemporal patterns of gene expression in cloned embryos(Oncotarget,2016 7(45):74120-74131; Oncotarget, 2017). Besides, we are also interested in investigating the effect of transgene on adult stem cell characteristics and animal development by using computational methods（BMC Genomics. 2016 17:188; Brief Bioinform. 2017 18(4):712-721.）. In this area, a series of high-quality studies are being published.
New Publications
Si Z, Li H, Shang W, Zhao Y, Kong L, Long C, et al. Zuo Y, Feng Z. SpaNCMG: improving spatial domains identification of spatial transcriptomics using neighborhood-complementary mixed-view graph convolutional network. Brief Bioinform. 2024 May 23;25(4):bbae259. doi: 10.1093/bib/bbae259. PMID:38811360. PMCID:PMC11136618. (2024 IF:13.994).
Yang S, Xing J, Liu D, Song Y, Yu H, et al.Xu S,Zuo Y.Review and new insights into the catalytic structural domains of the Fe (ll) and 2-Oxoglutarate families.Int J Biol Macromol.2024 Aug 15:134798.doi: 10.1016/j.ijbiomac.2024.134798.PMID:39153678.(2024 IF:8.2)
Hong Y, Li H, Long C, Liang P, Zhou J, Zuo Y. An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data. Fundamental Research. 2024. (2024 IF:6.2)
Liang Y, Guo Y, Zhai Y, Zhou J, Yang W, Zuo Y. Disease trend analysis platform accurately predicts the occurrence of cervical cancer under mixed diseases. Methods.2024 Aug 5:230:108-115.doi: 10.1016/j.ymeth.2024.07.011.Online ahead of print.PMID:39111721.(2024 IF:4.647)
Li H, Su D, Zhang X, He Y, Luo X, Xiong Y, et al. Zuo Y,Yang L.Machine learning-based prediction of diabetic patients using blood routine data. Methods.2024 Sep:229:156-162.doi: 10.1016/j.ymeth.2024.07.001.Epub 2024 Jul 15.PMID:39019099.(2024 IF:4.647)