Jinyan Li


Affiliation: University of Technology
Country: Australia


  1. Zheng Y, Peng H, Ghosh S, Lan C, Li J. Inverse similarity and reliable negative samples for drug side-effect prediction. BMC Bioinformatics. 2019;19:554 pubmed publisher
    ..The selection of highly-reliable negative samples can also make significant contributions to the performance improvement. ..
  2. Zheng Y, Peng H, Zhang X, Zhao Z, Yin J, Li J. Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases. BMC Bioinformatics. 2018;19:517 pubmed publisher
  3. Zhao L, Xie J, Bai L, Chen W, Wang M, Zhang Z, et al. Mining statistically-solid k-mers for accurate NGS error correction. BMC Genomics. 2018;19:912 pubmed publisher
    ..The z-score is adequate to distinguish solid k-mers from weak k-mers, particularly useful for pinpointing out solid k-mers having very low frequency. Applying z-score on k-mer can markedly improve the error correction accuracy. ..
  4. Lan C, Peng H, McGowan E, Hutvagner G, Li J. An isomiR expression panel based novel breast cancer classification approach using improved mutual information. BMC Med Genomics. 2018;11:118 pubmed publisher
    ..This novel technique could be directly applied to identify biomarkers in other diseases. ..
  5. Zhao Z, Peng H, Lan C, Zheng Y, Fang L, Li J. Imbalance learning for the prediction of N6-Methylation sites in mRNAs. BMC Genomics. 2018;19:574 pubmed publisher
    ..Our method achieves better correctness and robustness than the existing predictors in independent test and case studies. The results suggest that imbalance learning is promising to improve the performance of m6A prediction. ..
  6. Zhao Z, Han G, Yu Z, Li J. Laplacian normalization and random walk on heterogeneous networks for disease-gene prioritization. Comput Biol Chem. 2015;57:21-8 pubmed publisher
    ..Our algorithms have shown remarkably superior performance over the state-of-the-art algorithms for recovering gene-phenotype relationships. All Matlab codes can be available upon email request. ..
  7. Song R, Catchpoole D, Kennedy P, Li J. Identification of lung cancer miRNA-miRNA co-regulation networks through a progressive data refining approach. J Theor Biol. 2015;380:271-9 pubmed publisher
    ..According to our literature survey and database validation, many of these results are biologically meaningful for understanding the mechanism of the complex post-transcriptional regulations in lung cancer. ..
  8. Wang C, Dong X, Han L, Su X, Zhang Z, Li J, et al. Identification of WD40 repeats by secondary structure-aided profile-profile alignment. J Theor Biol. 2016;398:122-9 pubmed publisher
    ..The WDRR web server and the datasets are available at http://protein.cau.edu.cn/wdrr/. ..
  9. Liu Q, Hoi S, Kwoh C, Wong L, Li J. Integrating water exclusion theory into ? contacts to predict binding free energy changes and binding hot spots. BMC Bioinformatics. 2014;15:57 pubmed publisher
    ..ACVASA is designed using the advantages of both ? contacts and water exclusion. It is an excellent tool to predict binding free energy changes and binding hot spots after alanine mutation. ..

More Information


  1. Zheng Y, Ji B, Song R, Wang S, Li T, Zhang X, et al. Accurate detection for a wide range of mutation and editing sites of microRNAs from small RNA high-throughput sequencing profiles. Nucleic Acids Res. 2016;44:e123 pubmed publisher
    ..Compared with six existing studies or methods, MiRME has shown much superior performance for the identification and visualization of the M/E sites of miRNAs from the ever-increasing sRNA HTS profiles. ..
  2. Ghosh S, Li J, Cao L, Ramamohanarao K. Septic shock prediction for ICU patients via coupled HMM walking on sequential contrast patterns. J Biomed Inform. 2017;66:19-31 pubmed publisher
  3. Hasan M, Li J, Ahmad S, Molla M. predCar-site: Carbonylation sites prediction in proteins using support vector machine with resolving data imbalanced issue. Anal Biochem. 2017;525:107-113 pubmed publisher
    ..A user-friendly web server of predCar-Site is available at http://research.ru.ac.bd/predCar-Site/. ..
  4. Ren J, Song J, Ellis J, Li J. Staged heterogeneity learning to identify conformational B-cell epitopes from antigen sequences. BMC Genomics. 2017;18:113 pubmed publisher
    ..The proposed method uses only antigen sequence information, and thus has much broader applications. ..
  5. Peng H, Lan C, Zheng Y, Hutvagner G, Tao D, Li J. Cross disease analysis of co-functional microRNA pairs on a reconstructed network of disease-gene-microRNA tripartite. BMC Bioinformatics. 2017;18:193 pubmed publisher
    ..We also confirmed that the regulation of the microRNAs for the development of cancers is more complex and have more unique properties than those of non-cancer diseases. ..
  6. Lan C, Chen Q, Li J. Grouping miRNAs of similar functions via weighted information content of gene ontology. BMC Bioinformatics. 2016;17:507 pubmed publisher
    ..Our method has also demonstrated a novel usefulness for the function annotation of new miRNAs, as reported in the detailed case studies. ..
  7. Li Z, He Y, Liu Q, Zhao L, Wong L, Kwoh C, et al. Structural analysis on mutation residues and interfacial water molecules for human TIM disease understanding. BMC Bioinformatics. 2013;14 Suppl 16:S11 pubmed publisher
    ..Our study reveals that a large cluster of water buried in protein interfaces is fragile and high-maintenance, closely related to the structure, function and evolution of the whole protein. ..