Construction of personalized genomics model and clinical application in precision medicine

[1] Yuan, Y., Bayer, P. E., Batley, J., & Edwards, D. (2017). Improvements in genomic technologies: application to crop genomics. Trends in Biotechnology. Search in Google Scholar

[2] Laura, G. A., Koonin, E. V., & Kristensen, D. M. (2017). Prokaryotic virus orthologous groups (pvogs): a resource for comparative genomics and protein family annotation. Nucleic Acids Research, D491. Search in Google Scholar

[3] Nielsen, Rasmus, Willerslev, Eske, Pritchard, & Jonathan, et al. (2017). Tracing the peopling of the world through genomics. Nature. Search in Google Scholar

[4] Consortium, P. G. (2018). Computational pan-genomics: status, promises and challenges. Briefings in Bioinformatics(1), 118-135. Search in Google Scholar

[5] Ferenci, T. (2019). Irregularities in genetic variation and mutation rates with environmental stresses. Environmental Microbiology, 21(11). Search in Google Scholar

[6] Dockhorn, A., & Lucas, S. (2022). Choosing representation, mutation, and crossover in genetic algorithms. IEEE computational intelligence magazine. Search in Google Scholar

[7] Amadou, A., Praud, D., Coudon, T., Deygas, F., Grassot, L., & Dubuis, M., et al. (2023). Long-term exposure to nitrogen dioxide air pollution and breast cancer risk: a nested case-control within the french e3n cohort study. Environmental Pollution. Search in Google Scholar

[8] Mao, X., He, W., Eriksson, M., Lindstrm, L., Holowko, N., & Lagercrantz, S., et al. (2022). 133p using breast cancer risk factors of women to estimate incidence of breast cancer in their sisters. Annals of Oncology. Search in Google Scholar

[9] Dhiman, D., & Kumar, A. (2021). Association of preoperative serum adipokines, insulin and sex steroid hormones with breast cancer risk in the indian women. Indian Journal of Cancer. Search in Google Scholar

[10] Morana, G., Tortora, D., Serena Staglianò, Nozza, P., Mascelli, S., & Severino, M., et al. (2018). Pediatric astrocytic tumor grading: comparison between arterial spin labeling and dynamic susceptibility contrast mri perfusion. Neuroradiology. Search in Google Scholar

[11] Boddaert, Nathalie, Dangouloff-Ros, & Volodia. (2017). Arterial spin labeling to predict brain tumor grading: limits of cutoff cerebral blood flow values response. Radiology. Search in Google Scholar

[12] Kryvenko, O. N., Epstein, J. I., Ali, M., Iakymenko, O. A., Almeida, E. S. R. D., & Kumar, C. D., et al. (2024). Radical prostatectomy cancer grade and percentage of gleason pattern 4 estimated by global vs individual tumor grading correlate differently with the risk of biochemical recurrence in grade group 2 and 3 cancers. American Journal of Clinical Pathology. Search in Google Scholar

[13] Bruckmann, N. M., Rischpler, C., Kirchner, J., Umutlu, L., Herrmann, K., & Ingenwerth, M., et al. (2021). Correlation between contrast enhancement, standardized uptake value (suv), and diffusion restriction (adc) with tumor grading in patients with therapy-naive neuroendocrine neoplasms using hybrid ga-68-dotatoc pet/mri. European Journal of Radiology(137-), 137. Search in Google Scholar

[14] Zille, P., Calhoun, V. D., & Wang, Y. P. (2017). Enforcing co-expression within a brain-imaging genomics regression framework. IEEE Transactions on Medical Imaging, PP(99), 1-1. Search in Google Scholar

[15] Cen, X., Dong, W., Lv, W., Zhao, Y., Dubee, F., & Mentis, A. F. A., et al. (2024). Towards interpretable imaging genomics analysis: methodological developments and applications. Information Fusion, 102. Search in Google Scholar

[16] Gossmann, A., Zille, P., Calhoun, V., & Wang, Y. P. (2017). Fdr-corrected sparse canonical correlation analysis with applications to imaging genomics. IEEE Transactions on Medical Imaging. Search in Google Scholar

[17] Shih, Robert, Y., Koeller, Kelly, & K. (2018). Imaging genomics of embryonal tumors of the central nervous system responds. Radiographics, 38(4), 1286-1286. Search in Google Scholar

[18] Mollaei, P., & Farimani, A. B. (2023). Global machine learning model predicting activity level of any gpcrs based on protein structure. Biophysical journal, 122 3S1, 181a. Search in Google Scholar

[19] GuYuanlin, LiBaihua, & MengQinggang. (2022). Hybrid interpretable predictive machine learning model for air pollution prediction. Neurocomputing(468-Jan.11). Search in Google Scholar

[20] Jen, K. Y., Albahra, S., Yen, F., Sageshima, J., & Rashidi, H. H. (2021). Automated en masse machine learning model generation shows comparable performance as classic regression models for predicting delayed graft function in renal allografts. Transplantation. Search in Google Scholar

[21] Shepherdson, M., Kilburn, D., Ullah, S., Price, T., Karapetis, C. S., & Nguyen, P., et al. (2023). Survival outcomes for patients with colorectal cancer with synchronous liver only metastasis. ANZ journal of surgery. Search in Google Scholar

[22] Ito, D., Yogosawa, S., Mimoto, R., Hirooka, S., & Yoshida, K. (2017). Dyrk2 is a suppressor and potential prognostic marker for liver metastasis of colorectal cancer. Cancer Science, 108(8). Search in Google Scholar

[23] Shin, S., Choi, C. W., Moon, J. M., Kim, H. S., & Choi, C. H. (2021). P150 histologic features predicting prognosis and their relationship with endoscopic findings in ulcerative colitis patients with mucosal healing. Journal of Crohn's and Colitis(Supplement_1), Supplement_1. Search in Google Scholar

[24] Basu, S., Sussman, J. B., & Hayward, R. A. (2017). Detecting heterogeneous treatment effects to guide personalized blood pressure treatment. Annals of Internal Medicine. Search in Google Scholar

[25] Celik, S., Gokbayrak, O., Erol, A., Yorukoglu, K., Aktas, T., & Sari, H., et al. (2023). Anna karenina principle in personalized treatment of bladder cancer according to oncogram: which drug for which patient?. Personalized medicine(2), 20. Search in Google Scholar

[26] Yi, Du, Hirohito, Yamaguchi, Jennifer, & L., et al. (2017). Parp inhibitors as precision medicine for cancer treatment. National Science Review. Search in Google Scholar

[27] Nazha, A., & Sekeres, M. A. (2017). Precision medicine in myelodysplastic syndromes and leukemias: lessons from sequential mutations. Annual Review of Medicine, 68(1), 127. Search in Google Scholar

[28] Schütte, Moritz, Ogilvie, L. A., Rieke, D. T., Lange, B. M. H., Yaspo, M. L., & Lehrach, H. (2017). Cancer precision medicine: why more is more and dna is not enough. Public Health Genomics, 20(2), 70-80. Search in Google Scholar

[29] Bush, & Andrew. (2017). Translating asthma: dissecting the role of metabolomics, genomics and personalized medicine. Indian Journal of Pediatrics. Search in Google Scholar

[30] Tsoli, M., Wadham, C., Pinese, M., Failes, T., & Ziegler, D. S. (2018). Abstract lb-137: integrated genomics: drug screening and personalized xenograft development approach to identify precision treatments for aggressive pediatric brain tumors. Cancer Research, 78(13 Supplement), LB-137-LB-137. Search in Google Scholar

[31] Wang, D. R., Guadagno, C. R., Xiaowei, M., Scott, M. D., Pleban, J. R., & Baker, R. L., et al. (2019). A framework for genomics-informed ecophysiological modeling in plants. Journal of Experimental Botany(9), 9. Search in Google Scholar

[32] Graham Rose,David J. Wooldridge,Catherine Anscombe,Edward T. Mee,Raju V. Misra & Saheer Gharbia.(2015).Challenges of the Unknown: Clinical Application of Microbial Metagenomics. International Journal of Genomics292950. Search in Google Scholar

[33] Peter Z. Yan,Fei Wang,Nathaniel Kwok,Baxter B. Allen,Sotirios Keros & Zachary Grinspan.(2019). Automated spectrographic seizure detection using convolutional neural networks.Seizure: European Journal of Epilepsy124-131. Search in Google Scholar

[34] KirstenMcAulay.(2024).Inducing Targeted Protein Degradation: From Chemical Biology to Drug Discovery and Clinical Applications. Edited by Philipp Cromm.ChemMedChem(7), Search in Google Scholar

[35] Yu-Bao Zou,Ru-Tai Hui & Lei Song.(2019).The era of clinical application of gene diagnosis in cardiovascular diseases is coming.Chronic Diseases and Translational Medicine(4),214-220. Search in Google Scholar

[36] Patil V M,Patel F D,Chakraborty S,Oinam A S & Sharma S C.(2011).Can point doses predict volumetric dose to rectum and bladder: a CT-based planning study in high dose rate intracavitary brachytherapy of cervical carcinoma?.The British journal of radiology(1001),441-8. Search in Google Scholar