R.L. Siegel, A.N. Giaquinto, A. Jemal, Cancer statistics, 2024. CA. Cancer J. Clin. 74, 12–49 (2024).
B. Scott, R.J. Wong, Step-by-Step Thyroidectomy-Incision, check for updates nerve identification, parathyroid preservation, and gland removal. Otolaryngol. Clin. N. Am. E-Book 57, 25 (2023).
Y. Deng et al. Global burden of thyroid cancer from 1990 to 2017. JAMA Netw. Open 3, e208759–e208759 (2020).
Article PubMed PubMed Central Google Scholar
H. Gharib et al. American Association of Clinical Endocrinologists and Associazione Medici Endocrinologi Medical Guidelines for Clinical Practice for the Diagnosis and Management of thyroid nodules. Endocr. Pract. 12, 63–102 (2006).
L. Fugazzola, M. Muzza, G. Pogliaghi, M. Vitale, Intratumoral genetic heterogeneity in papillary thyroid cancer: occurrence and clinical significance. Cancers 12, 383 (2020).
Article CAS PubMed PubMed Central Google Scholar
M. Liao, Z. Wang, J. Yao, H. Xing, Y. Hao, B. Qiu, Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis. Mol. Cell. Biochem. 478, 2111–2123 (2023).
Article CAS PubMed Google Scholar
I. Petrini, R.L. Cecchini, M. Mascaró, I. Ponzoni, J.A. Carballido, Papillary thyroid carcinoma: a thorough bioinformatic analysis of gene expression and clinical data. Genes 14, 1250 (2023).
Article CAS PubMed PubMed Central Google Scholar
H. Ren, X. Liu, F. Li, X. He, N. Zhao, Identification of a six gene prognosis signature for papillary thyroid cancer using multi-omics methods and bioinformatics analysis. Front. Oncol. 11, 624421 (2021).
Article CAS PubMed PubMed Central Google Scholar
J. Shang, Q. Ding, S. Yuan, J.-X. Liu, F. Li, H. Zhang, Network analyses of integrated differentially expressed genes in papillary thyroid carcinoma to identify characteristic genes. Genes 10, 45 (2019).
Article PubMed PubMed Central Google Scholar
S. Li, Y. Yin, H. Yu, Genetic expression profile‑based screening of genes and pathways associated with papillary thyroid carcinoma. Oncol. Lett. (2018). https://doi.org/10.3892/ol.2018.9342
V. Vasko et al. Gene expression and functional evidence of epithelial-to-mesenchymal transition in papillary thyroid carcinoma invasion. Proc. Natl. Acad. Sci. 104, 2803–2808 (2007). https://doi.org/10.1073/pnas.0610733104
Article CAS PubMed PubMed Central Google Scholar
H. He et al. The role of microRNA genes in papillary thyroid carcinoma. Proc. Natl. Acad. Sci. 102, 19075–19080 (2005). https://doi.org/10.1073/pnas.0509603102
Article CAS PubMed PubMed Central Google Scholar
G. Tomás et al. A general method to derive robust organ-specific gene expression-based differentiation indices: application to thyroid cancer diagnostic. Oncogene 31, 4490–4498 (2012).
D. Anguita, L. Ghelardoni, A. Ghio, L. Oneto, S. Ridella, “The’K’in K-fold Cross Validation.,” in ESANN, 2012, pp. 441–446. Accessed: Aug. 25, 2024. [Online].
G.K. Smyth, Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, 1–25 (2004). https://doi.org/10.2202/1544-6115.1027
Y. Benjamini, D. Drai, G. Elmer, N. Kafkafi, I. Golani, Controlling the false discovery rate in behavior genetics research. Behav. Brain Res. 125, 279–284 (2001).
Article CAS PubMed Google Scholar
Y. Bei, P. Hong, A novel approach to minimize false discovery rate in genome-wide data analysis. BMC Syst. Biol. 7, S1 (2013). https://doi.org/10.1186/1752-0509-7-S4-S1
Article PubMed PubMed Central Google Scholar
A. Reiner, D. Yekutieli, Y. Benjamini, Identifying differentially expressed genes using false discovery rate controlling procedures. Bioinformatics 19, 368–375 (2003).
Article CAS PubMed Google Scholar
R. Stevens, C.A. Goble, S. Bechhofer, Ontology-based knowledge representation for bioinformatics. Brief. Bioinform. 1, 398–414 (2000).
Article CAS PubMed Google Scholar
M. Kanehisa et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36, D480–D484 (2007).
Article PubMed PubMed Central Google Scholar
H. Wickham, ggplot2. in Use R! Cham: Springer International Publishing, (2016). https://doi.org/10.1007/978-3-319-24277-4
C.-H. Gao, G. Yu, and P. Cai, ggVennDiagram: an intuitive, easy-to-use, and highly customizable R package to generate Venn diagram. Front. Genet., 1598 (2021).
P. Braun, A. Gingras, History of protein–protein interactions: From egg‐white to complex networks. PROTEOMICS 12, 1478–1498 (2012). https://doi.org/10.1002/pmic.201100563
Article CAS PubMed Google Scholar
L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)
T. Mahboob, S. Irfan, A. Karamat, “A machine learning approach for student assessment in E-learning using Quinlan’s C4. 5, Naive Bayes and Random Forest algorithms,” in 2016 19th International Multi-topic Conference (INMIC), IEEE, 2016, pp. 1–8. Accessed: Feb. 05, 2024.
S.G. Eraldemir, M.T. Arslan, Y. Esen, “Comparison of random forest and J48 decision tree classifiers using HHT based features in EEG,” in International Advanced Researches & Engineering Congress-2017, 2017. Accessed: Feb. 05, 2024.
S. Kilicarslan, A. Kemal, O. Cömert, Parçacık sürü optimizasyonu kullanılarak boyutu azaltılmış mikrodizi verileri üzerinde makine öğrenmesi yöntemleri ile prostat kanseri teşhisi. Düzce Üniversitesi Bilim Ve Teknol. Derg. 7, 769–777 (2019).
S. Kilicarslan, M. Celik, Ş. Sahin, “Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification,”. Biomed. Signal Process. Control 63, 102231 (2021).
T.M. Cover, Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans. Electron. Comput. 3, 326–334 (1965).
V. Vapnik, The nature of statistical learning theory. Springer science & business media, 1999. Accessed: Feb. 05, 2024.
S. Kiliçarslan and E. Dönmez, Improved multi-layer hybrid adaptive particle swarm optimization based artificial bee colony for optimizing feature selection and classification of microarray data. Multimed. Tools Appl. (2023), https://doi.org/10.1007/s11042-023-17234-4
J. Li et al. Feature selection: a data perspective. ACM Comput. Surv. 50, 1–45 (2018). https://doi.org/10.1145/3136625
F. Aragón-Royón, A. Jiménez-Vílchez, A. Arauzo-Azofra, J.M. Benítez, FSinR: an exhaustive package for feature selection. (2020). arXiv: arXiv:2002.10330.
M. Kuhn, Caret package. J. Stat. Softw. 28, 1–26 (2008).
S. García, A. Fernández, J. Luengo, F. Herrera, A study of statistical techniques and performance measures for genetics-based machine learning: accuracy and interpretability. Soft Comput. 13, 959–977 (2009).
M. Doshi, Correlation based feature selection (CFS) technique to predict student performance. Int. J. Comput. Netw. Commun. 6, 197 (2014).
R.J. Urbanowicz, M. Meeker, W. La Cava, R.S. Olson, J.H. Moore, Relief-based feature selection: Introduction and review. J. Biomed. Inform. 85, 189–203 (2018).
Article PubMed PubMed Central Google Scholar
D. Meng, Z. Li, X. Ma, L. Wu, L. Fu, G. Qin, ETV5 overexpression contributes to tumor growth and progression of thyroid cancer through PIK3CA. Life Sci. 253, 117693 (2020).
Article CAS PubMed Google Scholar
S.M. Gaikwad, L. Gunjal, A.R. Junutula, A. Astanehe, S.S. Gambhir, P. Ray, Non-invasive imaging of phosphoinositide-3-kinase-catalytic-subunit-alpha (PIK3CA) promoter modulation in small animal models. PLoS One 8, e55971 (2013).
Article CAS PubMed PubMed Central Google Scholar
C. Da et al. N-cadherin promotes thyroid tumorigenesis through modulating major signaling pathways. Oncotarget 8, 8131 (2017).
X. Lin et al. TFF3 contributes to epithelial-mesenchymal transition (EMT) in papillary thyroid carcinoma cells via the MAPK/ERK signaling pathway. J. Cancer 9, 4430 (2018).
留言 (0)