PubMed Text Data Mining

• PubMed is considered the optimal database for medical and biomedical engineering research (Albahri et al., 2020).
• Besides, PubMed searches the MEDLINE database and produces a comprehensive search of articles on abstracts (Jurca et al., 2016).
• This text data mining can identify the annotated biomedical terms.

  For example, the genes' list will be identified with the gene names or Entrez IDs, then verified with controlled vocabularies like disease names.

• The relationship between the biomedical concepts can be found, such as the gene-gene relationship or gene-disease relationship.
• Hypothesis generation can occur with support and validation through experimental data.
• Identifying genes hypotheses with the gene expression data can discover the new relationships between genes.

  For example, Gene Entrez ID 11260 points to Gene Entrez ID 5901 in the directed graph, which can be derived from XPOT (gene Entrez ID: 11260) inhibiting RAN (gene Entrez ID: 5901) in the RNA transport (KEGG pathway ID: hsa03013) (Nies et al., 2021).

References

1. Albahri, A. S., Hamid, R. A., Alwan, J. K., Al-Qays, Z. T., Zaidan, A. A., Zaidan, B. B., Albahri, A.O.S., AlAmoodi, A.H., Khlaf, J.M., Almahdi, E.M., Thabet, E., Hadi, S.M., Mohammed, K.I., Alsalem, M.A., Al-Obaidi, J.R., and Madhloom, H. T. (2020). Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. Journal of medical systems, 44, 1-11.
2. Jurca, G., Addam, O., Aksac, A., Gao, S., Özyer, T., Demetrick, D., and Alhajj, R. (2016). Integrating text mining, data mining, and network analysis for identifying genetic breast cancer trends. BMC research notes, 9(1), 1-35.
3. Nies, H. W., Mohamad, M. S., Zakaria, Z., Chan, W. H., Remli, M. A., and Nies, Y. H. (2021). Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression Data. Entropy, 23(9), 1232.
4. Nies, H. W., Zakaria, Z., Chan, W. H., Kamsani, I. I., and Hasan, N. S. (2022). PubMed Text Data Mining Automation for Biological Validation on Lists of Genes and Pathways. International Journal of Innovative Computing, 12(1), 59-64.