Long non-coding RNAs (lncRNAs) play crucial roles in a variety of human diseases. As wet-lab experiments for identifying lncRNA-disease associations (LDAs) are often costly and time-consuming, computational prediction methods offer a valuable alternative. Such approaches can help elucidate the molecular mechanisms of diseases and contribute to the discovery of diagnostic biomarkers. Traditional LDA prediction methods primarily rely on capturing local features of lncRNAs or diseases. In contrast, we propose a novel computational framework, GRLDAMAN, for LDA prediction. To achieve robust predictive performance, GRLDAMAN utilizes the GraRep network embedding algorithm to learn informative and representative feature vectors. Compared to existing methods, GRLDAMAN demonstrates superior prediction performance, attaining an area under the curve (AUC) of 0.9201 under 5-fold cross-validation. This result indicates that the combination of GraRep and XGBoost yields stable and reliable predictions. Furthermore, case studies confirm that GRLDAMAN can holistically enhance the performance of LDA prediction.
References
[1] Rosandić, M., Paar, V. (2022) Standard genetic code vs. supersymmetry genetic code–alphabetical table vs. physicochemical table. BioSystems, 218, 104695.
[2] Mahat, D. B., Tippens, N. D., Martin-Rufino, J. D., Waterton, S. K., Fu, J., Blatt, S. E., Sharp, P. A. (2024) Single-cell nascent RNA sequencing unveils coordinated global transcription. Nature, 631(8019), 216-223.
[3] Zhang, S., Duan, J., Du, Y., Xie, J., Zhang, H., Li, C., Zhang, W. (2021) Long non-coding RNA signatures associated with liver aging in senescence-accelerated mouse prone 8 model. Frontiers in Cell and Developmental Biology, 9, 698442.
[4] Moftakhar, A., Khoshnam, S. E., Farzaneh, M., Abouali Gale Dari, M. (2024) Functional Roles of Long Non-coding RNAs on Stem Cell-related Pathways in Glioblastoma. Current Signal Transduction Therapy, 19(3), 40-52.
[5] Zhang, X., Jin, M., Yao, X., Liu, J., Yang, Y., Huang, J., Zhang, B. (2024) Upregulation of LncRNA WT1-AS inhibits tumor growth and promotes autophagy in gastric cancer via suppression of PI3K/Akt/mTOR pathway. Current Molecular Pharmacology, 17(1), e18761429318398.
[6] Liu, S. J., Dang, H. X., Lim, D. A., Feng, F. Y., Maher, C. A. (2021) Long noncoding RNAs in cancer metastasis. Nature Reviews Cancer, 21(7), 446-460.
[7] Bure, I. V., Nemtsova, M. V., Kuznetsova, E. B. (2022) Histone modifications and non-coding RNAs: mutual epigenetic regulation and role in pathogenesis. International Journal of Molecular Sciences, 23(10), 5801.
[8] Ha, J. (2024) LncRNA expression profile-based matrix factorization for predicting lncRNA-disease association. IEEE Access, 12, 70297-70304.
[9] Zeng, M., Lu, C., Fei, Z., Wu, F. X., Li, Y., Wang, J., Li, M. (2020) DMFLDA: a deep learning framework for predicting lncRNA–disease associations. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(6), 2353-2363.
[10] Yu, J., Ping, P., Wang, L., Kuang, L., Li, X., Wu, Z. (2018) A novel probability model for lncRNA–disease association prediction based on the naïve bayesian classifier. Genes, 9(7), 345.
[11] Ouyang, L., Huang, J., Zhang, X. F., Li, Y. R., Sun, Y., He, S., Zhu, Z. (2019) LncRNA-disease association prediction using two-side sparse self-representation. Frontiers in Genetics, 10, 476.
[12] Pei, F., Shi, Q., Zhang, H., Bahar, I. (2021) Predicting protein–protein interactions using symmetric logistic matrix factorization. Journal of Chemical Information and Modeling, 61(4), 1670-1682.
[13] Wodecki, J., Michalak, A., Zimroz, R., Wyłomańska, A. (2020) Separation of multiple local-damage-related components from vibration data using Nonnegative Matrix Factorization and multichannel data fusion. Mechanical Systems and Signal Processing, 145, 106954.
[14] Li, M., Liu, M., Bin, Y., Xia, J. (2020) Prediction of circRNA-disease associations based on inductive matrix completion. BMC Medical Genomics, 13(Suppl 5), 42.
[15] Liu, L., Zhu, S. (2021) Computational methods for prediction of human protein-phenotype associations: a review. Phenomics, 1(4), 171-185.
[16] Ding, L., Wang, M., Sun, D., Li, A. (2018) TPGLDA: Novel prediction of associations between lncRNAs and diseases via lncRNA-disease-gene tripartite graph. Scientific Reports, 8(1), 1065.
[17] Mori, T., Ngouv, H., Hayashida, M., Akutsu, T., Nacher, J. C. (2018) ncRNA-disease association prediction based on sequence information and tripartite network. BMC Systems Biology, 12(Suppl 1), 37.
[18] Ping, P., Wang, L., Kuang, L., Ye, S., Iqbal, M. F. B., Pei, T. (2018) A novel method for lncRNA-disease association prediction based on an lncRNA-disease association network. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(2), 688-693.
[19] Sumathipala, M., Maiorino, E., Weiss, S. T., Sharma, A. (2019) Network diffusion approach to predict lncRNA disease associations using multi-type biological networks: LION. Frontiers in Physiology, 10, 888.
[20] Yue, X., Wang, Z., Huang, J., Parthasarathy, S., Moosavinasab, S., Huang, Y., Sun, H. (2020) Graph embedding on biomedical networks: methods, applications and evaluations. Bioinformatics, 36(4), 1241-1251.
[21] Ouyang, M., Zhang, Y., Xia, X., Xu, X. (2023) Grarep++: flexible learning graph representations with weighted global structural information. IEEE Access, 11, 98217-98229.
[22] Guo, Z. H., Yi, H. C., You, Z. H. (2019) Construction and comprehensive analysis of a molecular association network via lncRNA–miRNA–disease–drug–protein graph. Cells, 8(8), 866.
[23] Lin, X., Lu, Y., Zhang, C., Cui, Q., Tang, Y. D., Ji, X., Cui, C. (2024) LncRNADisease v3. 0: an updated database of long non-coding RNA-associated diseases. Nucleic Acids Research, 52(D1), D1365-D1369.
[24] Miao, Y. R., Liu, W., Zhang, Q., Guo, A. Y. (2018) lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs. Nucleic Acids Research, 46(D1), D276-D280.
[25] Cheng, L., Wang, P., Tian, R., Wang, S., Guo, Q., Luo, M., Jiang, Q. (2019) LncRNA2Target v2. 0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Research, 47(D1), D140-D144.
[26] Cui, C., Zhong, B., Fan, R., Cui, Q. (2024) HMDD v4. 0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Research, 52(D1), D1327-D1332.
[27] Hu, Y., Guo, X., Yun, Y., Lu, L., Huang, X., Jia, S. (2025) DisGeNet: a disease-centric interaction database among diseases and various associated genes. Database, 2025, baae122.
[28] Wiegers, T. C., Davis, A. P., Wiegers, J., Sciaky, D., Barkalow, F., Wyatt, B., Mattingly, C. J. (2025) Integrating AI-powered text mining from PubTator into the manual curation workflow at the Comparative Toxicogenomics Database. Database, 2025, baaf013.
[29] Chou, C. H., Shrestha, S., Yang, C. D., Chang, N. W., Lin, Y. L., Liao, K. W., Huang, H. D. (2018) miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions. Nucleic Acids Research, 46(D1), D296-D302.
[30] Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., Wilson, M. (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074-D1082.
[31] Fernando, P. C., Mabee, P. M., Zeng, E. (2020) Integration of anatomy ontology data with protein–protein interaction networks improves the candidate gene prediction accuracy for anatomical entities. BMC Bioinformatics, 21(1), 442.
[32] Xu, D., Xu, H., Zhang, Y., Chen, W., Gao, R. (2020) Protein-protein interactions prediction based on graph energy and protein sequence information. Molecules, 25(8), 1841.
[33] Kantarjian, H., Kadia, T., DiNardo, C., Daver, N., Borthakur, G., Jabbour, E., Ravandi, F. (2021) Acute myeloid leukemia: current progress and future directions. Blood Cancer Journal, 11(2), 41.
[34] Marrapodi, M. M., Mascolo, A., Di Mauro, G., Mondillo, G., Pota, E., Rossi, F. (2022) The safety of blinatumomab in pediatric patients with acute lymphoblastic leukemia: a systematic review and meta-analysis. Frontiers in Pediatrics, 10, 929122.
[35] Chen, E. C., Garcia, J. S. (2024) Immunotherapy for acute myeloid leukemia: current trends, challenges, and strategies. Acta Haematologica, 147(2), 198-218.
[36] Li, J., Zi, Y., Wang, W., Li, Y. (2018) Long noncoding RNA MEG3 inhibits cell proliferation and metastasis in chronic myeloid leukemia via targeting miR-184. Oncology Research, 26(2), 297.
[37] Ketab, F. N. G., Gharesouran, J., Ghafouri-Fard, S., Dastar, S., Mazraeh, S. A., Hosseinzadeh, H., Rezazadeh, M. (2020) Dual biomarkers long non-coding RNA GAS5 and its target, NR3C1, contribute to acute myeloid leukemia. Experimental and Molecular pathology, 114, 104399.
[38] Ferlay, J., Colombet, M., Soerjomataram, I., Mathers, C., Parkin, D. M., Piñeros, M., Bray, F. (2019) Estimating the global cancer incidence and mortality in 2018: Globocan sources and methods. International Journal of Cancer, 144(8), 1941-1953.
[39] Zavala, V. A., Bracci, P. M., Carethers, J. M., Carvajal-Carmona, L., Coggins, N. B., Cruz-Correa, M. R., Fejerman, L. (2021) Cancer health disparities in racial/ethnic minorities in the United States. British journal of cancer, 124(2), 315-332.
[40] Sun, N., Zhang, G., Liu, Y. (2018) Long non-coding RNA XIST sponges miR-34a to promote colon cancer progression via Wnt/β-catenin signaling pathway. Gene, 665, 141-148.
[41] Song, J., Shu, H., Zhang, L., Xiong, J. (2019) Long noncoding RNA GAS5 inhibits angiogenesis and metastasis of colorectal cancer through the Wnt/β‐catenin signaling pathway. Journal of Cellular Biochemistry, 120(5), 6937-6951.
[42] Hsieh, T. C., Wu, J. M. (2020) Resveratrol suppresses prostate cancer epithelial cell scatter/invasion by targeting inhibition of hepatocyte growth factor (HGF) secretion by prostate stromal cells and upregulation of E-cadherin by prostate cancer epithelial cells. International Journal of Molecular Sciences, 21(5), 1760.
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Zhang, P., Zeng, Y. (2025) GRLDAMAN: A predictive framework for lncRNA-disease associations based on GraRep network embedding. Scientific Research Bulletin, 2(6), 170-183. https://doi.org/10.71052/srb2024/CSKE7257