Page 74 - TD-4-3

Tumor Discovery Highly accurate gene panels for cancer screening

Further disclosure enrichment. Genome Med. 2021;13(1):68.

Initial versions of the paper have been deposited in the doi: 10.1186/s13073-021-00864-4
biorXiv preprint server (doi: 10.1101/2022.07.25.501449, 12. Le Priol C, Azencott CA, Gidrol X. Detection of genes
10.1101/2024.07.25.604730). with differential expression dispersion unravels the role
of autophagy in cancer progression. PLoS Comput Biol.
References 2023;19(3):e1010342.

1. Collins FS, Morgan M, Patrinos A. The human genome doi: 10.1371/journal.pcbi.1010342
project: Lessons from Large-scale biology. Science. 13. Li H, Khang TF. clrDV: A differential variability test for
2003;300(5617):286-290.
RNA-Seq data based on the skew-normal distribution. PeerJ.
doi: 10.1126/science.1084564 2023;11:e16126.
2. Chu Y, Corey DR. RNA sequencing: Platform selection, doi: 10.7717/peerj.16126
experimental design, and data interpretation. Nucleic Acid
Ther. 2012;22(4):271-274. 14. Roberts AGK, Catchpoole DR, Kennedy PJ. Identification
of differentially distributed gene expression and distinct sets
doi: 10.1089/nat.2012.0367 of cancer-related genes identified by changes in mean and
3. Haque A, Engel J, Teichmann SA, Lönnberg T. A practical variability. NAR Genom Bioinform. 2022;4(1):lqab124.
guide to single-cell RNA-sequencing for biomedical research doi: 10.1093/nargab/lqab124
and clinical applications. Genome Med. 2017;9(1):75.
15. Andreani TS, Itoh TQ, Yildirim E, Hwangbo DS,
doi: 10.1186/s13073-017-0467-4 Allada R. Genetics of circadian rhythms. Sleep Med Clin.
4. The Cancer Genome Atlas Research Network, Weinstein 2015;10(4):413-421.
J, Collisson E, et al. The cancer genome atlas pan-cancer doi: 10.1016/j.jsmc.2015.08.007
analysis project. Nat Gen. 2013;45(10):1113-1120.
16. Gebert J, Motameny S, Faigle U, Forst CV, Schrader R.
doi: 10.1038/ng.2764 Identifying genes of gene regulatory networks using formal
5. Hutter C, Zenklusen JC. The cancer genome atlas: Creating concept analysis. J Comput Biol. 2008;15(2):185-194.
lasting value beyond its data. Cell. 2018;173(2):283-285. doi: 10.1089/cmb.2007.0107
doi: 10.1016/j.cell.2018.03.042 17. Choi V, Huang Y, Lam V, Potter D, Laubenbacher R, Duca K.
6. The Cancer Genome Atlas Research Network. The Cancer Using formal concept analysis for microarray data
Genome Atlas. 2006. Available from: https://www.cancer. comparison. J Bioinform Comput Biol. 2008;6(1):65-75.
gov/tcga [Last accessed on 2025 Apr 15]. doi: 10.1142/s021972000800328x
7. Cheng PF, Dummer R, Levesque MP. Data mining the 18. Motameny S, Versmold B, Schmutzler R. Formal Concept
cancer genome atlas in the era of precision cancer medicine. Analysis for the Identification of Combinatorial Biomarkers
Swiss Med Wkly. 2015;145:w14183. in Breast Cancer. In: Medina R, Obiedkov S, editors. Formal
doi: 10.4414/smw.2015.14183 Concept Analysis. ICFCA 2008. Lecture Notes in Computer
Science. Vol. 4933. Berlin, Heidelberg: Springer; 2008. p 229–240.
8. Liñares-Blanco, J, Pazos, A, Fernandez-Lozano, C. Machine
learning analysis of TCGA cancer data. PeerJ Comput Sci. doi: 10.1007/978-3-540-78137-0_17
2021;7:e584. 19. Amin II, Kassim SK, Hassanien A, Hefny HA. Formal
doi: 10.7717/peerj-cs.584 Concept Analysis for Mining Hypermethylated Genes
in Breast Cancer Tumor Subtypes. In: 12 International
th
9. Li Q, Dai W, Liu J, Sang Q, Li YX, Li YY. Gene dysregulation Conference on Intelligent Systems Design and Applications
analysis builds a mechanistic signature for prognosis and (ISDA). Kochi, India; 2012. p. 764-769.
therapeutic benefit in colorectal cancer. J Mol Cell Biol.
2020;12(11):881-893. doi: 10.1109/ISDA.2012.6416633
doi: 10.1093/jmcb/mjaa041 20. Kaytoue-Uberall M, Duplessis S, Napoli A. Using
Formal Concept Analysis for the Extraction of Groups of
10. Ali HEA, Lung PY, Sholl AB, et al. Dysregulated gene Co-expressed Genes. In: Le Thi HA, Bouvry P, Pham Dinh T,
expression predicts tumor aggressiveness in African- editors. Modelling, Computation and Optimization in
American prostate cancer patients. Sci Rep. 2018;8(1):16335.
Information Systems and Management Sciences. MCO 2008.
doi: 10.1038/s41598-018-34637-8 Communications in Computer and Information Science.
Vol. 14. Berlin, Heidelberg: Springer; 2008.
11. Mezlini AM, Das S, Goldenberg A. Finding associations
in a heterogeneous setting: Statistical test for aberration doi: 10.1007/978-3-540-87477-5_47

Volume 4 Issue 3 (2025) 66 doi: 10.36922/TD025190035