KIEE - The Transactions of the Korean Institute of Electrical Engineers

1

V. M. G. Olivares, L. M. G. Torres, G. H. Cuartas, M. C. N. De la Hoz, 2019, Immunohistochemical profile of renal cell tumours, Revista Espanola De Patologia, Vol. 52, No. 4, pp. 214-221

2

J. J. Hsieh, M. P. Purdue, S. Signoretti, C. Swanton, L. Albiges, M. Schmidinger, D. Y. Heng, J. Larkin, V. Ficarra, 2017, Renal cell carcinoma, Nat. Rev. Dis. Primers, Vol. 3, No. 17010, pp. 1-19

3

W. M, Linehan, M. M. Walther, B. Zbar, 2003, The genetic basis of cancer of the kidney, J. Urol., Vol. 170, pp. 2163-2172

4

W. M. Linehan, B. Zbar, 2004, Focus on kidney cancer, Cancer Cell, Vol. 6, No. 3, pp. 223-228

5

Cancer Genome Atlas Research Network, 2016, Comprehensive molecular characterization of papillary renal-cell carcinoma, N. Engl. J. Med., Vol. 374, No. 2, pp. 135-145

6

L. A. Torre, B. Trabert, C. E. DeSantis, K. D. Miller, G. Samimi, C. D. Runowicz, M. M. Gaudet, A. Jemal, R. L. Siegel, 2018, Ovarian cancer statistics, 2018, CA Cancer J. Clin., Vol. 68, pp. 284-296

7

A. J. Peired, R. Campi, M. L. Angelotti, G. Antonelli, C. Conte, E. Lazzeri, F. Becherucci, L. Calistri, S. Serni, P. Romagnani, 2021, Sex and Gender Differences in Kidney Cancer: Clinical and Experimental Evidence, Cancers, Vol. 13, No. 18, pp. 4588

8

Y. Zhan, C. Pan, Y. Zhao, J. Li, B. Wu, S. Bai, 2021, Systematic Analysis of the Global, Regional and National Burden of Kidney Cancer from 1990 to 2017: Results from the Global Burden of Disease Study 2017, Eur. Urol. Focus, Vol. 8, No. 1, pp. 302-319

9

N. Chowdhury, C. G. Drake, 2020, Kidney cancer: an overview of current therapeutic approaches, Urol. Clin., Vol. 47, No. 4, pp. 419-431

10

D. A. Siegel, S. J. Henley, J. Li, L. A. Pollack, E. A. Van Dyne, A. White, pp 950-954 2017, Rates and trends of pediatric acute lymphoblastic leukemia—United States, 2001–2014, Morb. Mortal. Wkly. Rep., Vol. 66, No. 36, pp. 950-954

11

A. F. Olshan, Y. M. Kuo, A. M, Meyer, M. E. Nielsen, M. P. Purdue, W. K. Rathmell, 2013, Racial difference in histologic subtype of renal cell carcinoma, Cancer Med., Vol. 2, No. 5, pp. 744-749

12

L. Lipworth, A. K. Morgans, T. L. Edwards, D. A. Barocas, S. S. Chang, S. D. Herrell, D. F. Penson, M. J. Resnick, J. A. Smith, P. E. Clark, 2016, Renal cell cancer histological subtype distribution differs by race and sex, BJU Int., Vol. 117, No. 2, pp. 260-265

13

T. R. Rebbeck, 2018, Prostate cancer disparities by race and ethnicity: from nucleotide to neighborhood, Cold Spring Harbor Persp. Med., Vol. 8, No. 9, pp. a030387

14

S. J. O. Nomura, Y. T. Hwang, S. L. Gomez, T. T. Fung, S. L. Yeh, C. Dash, L. Allen, S. Philips, L. Hilakivi-Clarke, Y. L. Zheng, J. H. Y. Wang, 2017, Dietary intake of soy and cruciferous vegetables and treatment- related symptoms in Chinese-American and non-Hispanic White breast cancer survivors, Breast Cancer Res. Treat., Vol. 168, No. 2, pp. 467-79

15

P. Mamoshina, K. Kochetov, E. Putin, F. Cortese, A. Aliper, W. S. Lee, S. M. Ahn. L. Uhn, N. Skjodt, O. Kovalchuk, M. Scheibye-Knudsen, 2018, Population specific biomarkers of human aging: a big data study using South Korean, Canadian, and Eastern European patient populations, J. Gerontology: Ser. A, Vol. 73, No. 11, pp. 1482-1490

16

H. Y. Xiong, B. Alipanahi, L. J. Lee, H. Bretschneider, D. Merico, R. K. Yuen, Y. Hua, S. Gueroussov, H. S. Najafabadi, T. R. Hughes, Q. Morris, Y. Barash, A. R. Krainer, N. Jojic, S. W. Scherer, B. J. Blencowe, B. J. Frey, 2015, RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease, Science, Vol. 347, No. 6218, pp. 1-20

17

M. Amgad, H. Elfandy, H. Hussein, L. A. Atteya, M. A. T. Elsebaie, L. S. A. Elnasr, R. A. Sakr, H. S. E. Salem, A. F. Ismail, A. M. Saad, J. Ahmed, M. A. T. Elsebaie, M. Rahman, I. A. Ruhban, N. M. Elgazar, Y. Alagha, M. H. Osman, A. M. Alhusseiny, M. M. Khalaf, A. F. Younes, A. Abdulkarim, D. M. Younes, A. M. Gadallah, A. M. Elkashash, S. Y. Fala, B. M. Zaki, J. Beezley, D. R. Chittajallu, D. Manthey, D. A. Gutman, L. A. D. Cooper, 2019, Structured crowdsourcing enables convolutional segmentation of histology images, Bioinformatics, Vol. 35, No. 18, pp. 3461-3467

18

V. M. G. Olivares, L. M. G. Torres, G. H. Cuartas, M. C. N. De la Hoz, 2019, Immunohistochemical profile of renal cell tumours, Rev. Esp. Patol., Vol. 52, No. 4, pp. 214-221

19

accessed on 17 August, 2021, National Cancer Center. Available online: https://ncc.re.kr/ index

20

B. H. Chi, I. H. Chang, 2018, The overdiagnosis of kidney cancer in Koreans and the active surveillance on small renal mass, Korean J. Urol. Oncol., Vol. 16, No. 1, pp. 15-24

21

A. M. Ali, H. Zhuang, A. Ibrahim, O. Rehman, M. Huang, A. Wu, 2018, A machine learning approach for the classification of kidney cancer subtypes using miRNA genome data, Appl. Sci., Vol. 8, No. 12, pp. 1-14

22

H. M. Kim, S. J. Lee, S. J. Park, I. Y. Choi, S. H. Hong, 2021, Machine learning approach to predict the probability of recurrence of renal cell carcinoma after surgery: Prediction model development study, JMIR Med. Inform., Vol. 9, No. 3, pp. e25635

23

accessed on 17 August, 2021, Genomic Data Commons. Available online: https://portal.gdc. cancer.gov

24

B. J. Kim, S. H. Kim, 2018, Prediction of inherited genomic susceptibility to 20 common cancer types by a supervised machine-learning method, PNAS USA, Vol. 115, No. 6, pp. 1322-1327

25

O. G. Troyanskaya, K. Dolinski, A. B. Owen, R. B. Altman, D. Botstein, 2003, A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae), PNAS USA, Vol. 100, No. 14, pp. 8348-8353

26

N. Hadjiyski, 2020, Kidney cancer staging: Deep learning neural network based approach, 2020 International Conference on E-Health and Bioengineering (EHB 2020)

27

H. S. Shon, E. Batbaatar, K. O. Kim, E. J. Cha, K. A. Kim, 2020, Classification of kidney cancer data using cost-sensitive hybrid deep learning approach, Symmetry, Vol. 12, No. 1,154

28

N. Simidjievski, C. Bodnar, I. Tariq, P. Scherer, H. A. Terre, Z. Shams, M. Jamnik, P. Liò, 2019, Variational autoencoders for cancer data integration: Design principles and computational practice. Front. Genet.,, Vol. 10, No. 1205

29

P. Baldi, K. Hornik, 1989, Neural networks and principal component analysis: Learning from examples without local minima., Neural Netw., Vol. 2, pp. 53-58

30

M. Mohri, A. Rostamizadeh, A. Talwalkar, 2012, Foundations of Machine Learning, MIT Press

31

P. Vincent, H. Larochelle, L. Lajoie, Y. Bengio, P. A. Manzagol, 2010, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J. Mach. Learn. Res., Vol. 11, pp. 3371-3408

32

M. A. Ranzato, C. S. Poultney, S. Chopra, Y. LeCun, 2007, Efficient learning of sparse representations with an energy-based model, Adv. Neural Inf. Process. Syst., Vol. 19, pp. 1137-1144

33

D. P. Kingma, M. Welling, 2014, Auto-encoding variational bayes, Proceedings of the 2nd International Conference on Learning Representations

34

Y. Pu, Z. Gan, R. Henao, X. Yuan, C. Li, A. Stevens, L. Carin, 2016, Variational autoencoder for deep learning of images, labels and captions, 30th Conference on Neural Information Processing Systems (NIPS 2016)

35

K. Simonyan, A. Zisserman, 2015, Very deep convolutional networks for large-scale image recognition, The 3rd International Conference on Learning Representations (ICLR)

36

L. Le, A. Patterson, M. White, 2018, Supervised autoencoders: Improving generalization performance with unsupervised regularizers, 32nd Conference on Neural Information Processing Systems (NIPS 2018)

37

M. Mohri, A. Rostamizadeh, D. Storcheus, 2015, Generalization bounds for supervised dimensionality reduction, JMLR: Workshop and Conf. Proc., Vol. 44, pp. 226-241

38

L. A. Gottlieb, A. Kontorovich, R. Krauthgamer, 2016, Adaptive metric dimensionality reduction, Theor. Comput. Sci., Vol. 620, pp. 105-118

39

K. Sohn, H. Lee, X. Yan, 2015, Learning structured output representation using deep conditional generative models, Proceedings of the 28th International Conference on Neural Information Processing Systems, Vol. 2, pp. 3483-3491

40

S. Belharbi, R. Hérault, C. Chatelain, S. Adam, 2018, Deep neural networks regularization for structured output prediction, Neurocomputing, Vol. 281, pp. 169-177

41

Y. Bengio, E. Laufer, G. Alain, J. Yosinski, 2014, Deep generative stochastic networks trainable by backprop, Proceeding of the 31st International Conference on Machine Learning, Vol. 32, pp. 226-234

42

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, 2011, Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., Vol. 12, pp. 2825-2830

43

A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, 2019, Pytorch: An imperative style, high-performance deep learning library, Proceedings of the 33rd International Conference on Neural Information Processing Systems, pp. 8026-8037

44

I. Lucca, T. Klatte, H. Fajkovic, M. De Martino, S. F. Shariat, 2015, Gender differences in incidence and outcomes of urothelial and kidney cancer, Nat. Rev. Urol., Vol. 12, No. 12, pp. 585-592

45

M. Mancini, M. Righetto, G. Baggio, 2020, Gender-related approach to kidney cancer management: Moving forward, Int. J. Mol. Sci., Vol. 21, No. 9, pp. 3378

46

D. Hepps, A. Chernoff, 2006, Risk of renal insufficiency in African-Americans after radical nephrectomy for kidney cancer, Urologic Oncology: Seminars and Original Investigations, Vol. 24, No. 5, pp. 391-395

47

B. Shuch, S. Vourganti, C. J. Ricketts, L. Middleton, J. Peterson, M. J. Merino, A. R. Metwalli, R. Srinivasan, W. M. Linehan, 2014, Defining early-onset kidney cancer: implications for germline and somatic mutation testing and clinical management, J. Clin. Oncol., Vol. 32, No. 5, pp. 431-437

48

J. R. Vasselli, J. H. Shih, S. R. Iyengar, J. Maranchie, J. Riss, R. Worrell, C. Torres-Cabala, R. Tabios, A. Mariotti, R. Stearman, M. Merino, W. M. Linehan, 2003, Predicting survival in patients with metastatic kidney cancer by gene- expression profiling in the primary tumor, Proceedings of the National Academy of Sciences, Vol. 100, No. 12, pp. 6958-6963

49

M. Mostavi, Y. C. Chiu, Y Huang, Y. Chen, 2020, Convolutional neural network models for cancer type prediction based on gene expression, BMC Med. Genom., Vol. 13, No. 44, pp. 1-13

50

N. E. M. Khalifa, M. H. N. Taha, D. E. Ali, A. Slowik, A. E. Hassanien, 2020, Artificial intelligence technique for gene expression by tumor RNA-Seq data: a novel optimized deep learning approach, IEEE Access, Vol. 8, pp. 22874-22883

51

R. Tabares-Soto, S. Orozco-Arias, V. Romero-Cano, V. S. Bucheli, J. L Rodríguez-Sotelo, C. F. Jiménez-Varón, 2020, A comparative study of machine learning and deep learning algorithms to classify cancer types based on microarray gene expression data, PeerJ Comput. Sci., Vol. 6, No. e270

KIEEThe Transactions of
the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.

Journal Search

Journal XML

References

KIEEThe Transactions ofthe Korean Institute of Electrical Engineers