|
1
|
Torre LA, Bray F, Siegel RL, Ferlay J,
Lortet-Tieulent J and Jemal A: Global cancer statistics, 2012. CA
Cancer J Clin. 65:87–108. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
2
|
Chen W, Zheng R, Zhang S, Zeng H, Xia C,
Zuo T, Yang Z, Zou X and He J: Cancer incidence and mortality in
China, 2013. Cancer Letters. 401:63–71. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
3
|
Chen W, Zheng R, Baade PD, Zhang S, Zeng
H, Bray F, Jemal A, Yu XQ and He J: Cancer statistics in China,
2015. CA Cancer J Clin. 66:115–132. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
4
|
Quah HM, Chou JF, Gonen M, Shia J, Schrag
D, Landmann RG, Guillem JG, Paty PB, Temple LK, Wong WD and Weiser
MR: Identification of patients with high-risk stage II colon cancer
for adjuvant therapy. Dis Colon Rectum. 51:503–507. 2008.
View Article : Google Scholar : PubMed/NCBI
|
|
5
|
de Gramont A, Hubbard J, Shi Q, O'Connell
MJ, Buyse M, Benedetti J, Bot B, O'Callaghan C, Yothers G, Goldberg
RM, et al: Association between disease-free survival and overall
survival when survival is prolonged after recurrence in patients
receiving cytotoxic adjuvant therapy for colon cancer: Simulations
based on the 20,800 Patient ACCENT Data Set. J Clin Oncol Official.
28:460–465. 2010. View Article : Google Scholar
|
|
6
|
Saghapour E and Sehhati M: Prediction of
metastasis in advanced colorectal carcinomas using CGH data. J
Theor Biol. 429:116–123. 2017. View Article : Google Scholar : PubMed/NCBI
|
|
7
|
Chen YD, Zheng S, Yu JK and Hu X:
Artificial neural networks analysis of surface-enhanced laser
desorption/ionization mass spectra of serum protein pattern
distinguishes colorectal cancer from healthy population. Clin
Cancer Res. 10:8380–8385. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
8
|
Hagan MT, Demuth HB and Beale M: Neural
network design. InWseas International Conference on Circuits.
357:1997.
|
|
9
|
Kuan CM: Artificial Neural Networks. Ieas
Working Paper Academic Research. 1:1–23. 2006.
|
|
10
|
Shi HY, Lee KT, Wang JJ, Sun DP, Lee HH
and Chiu CC: Artificial neural network model for predicting 5-year
mortality after surgery for hepatocellular carcinoma: A nationwide
study. J Gastrointest Surg. 16:2126–2131. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
11
|
Spelt L, Nilsson J, Andersson R and
Andersson B: Artificial neural networks-a method for prediction of
survival following liver resection for colorectal cancer
metastases. Eur J Surg Oncol. 39:648–654. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
12
|
Tsao CW, Liu CY, Cha TL, Wu ST, Sun GH, Yu
DS, Chen HI, Chang SY, Chen SC and Hsu CY: Artificial neural
network for predicting pathological stage of clinically localized
prostate cancer in a Taiwanese population. J Chin Med Assoc.
77:513–518. 2014. View Article : Google Scholar : PubMed/NCBI
|
|
13
|
Hu HP, Niu ZJ, Bai YP and Tan XH: Cancer
classification based on gene expression using neural networks.
Genet Mol Res. 14:17605–17611. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
14
|
Peng JH, Fang YJ, Li CX, Ou QJ, Jiang W,
Lu SX, Lu ZH, Li PX, Yun JP, Zhang RX, et al: A scoring system
based on artificial neural network for predicting 10-year survival
in stage II A colon cancer patients after radical surgery.
Oncotarget. 7:22939–22947. 2016. View Article : Google Scholar : PubMed/NCBI
|
|
15
|
Cancer Genome Atlas Network: Comprehensive
molecular characterization of human colon and rectal cancer.
Nature. 487:330–337. 2012. View Article : Google Scholar : PubMed/NCBI
|
|
16
|
Whitley D: A genetic algorithm tutorial.
Stat Comput. 4:65–85. 1994. View Article : Google Scholar
|
|
17
|
Shaefer CG: Genetic algorithm. Springer.
(New York, NY). 8191993.
|
|
18
|
Mitchell M, Forrest S and Holland JH: The
royal road for genetic algorithms: Fitness landscapes and GA
Performance. In: Proceedings of the First European Conference on
Artificial Life. MIT Press. (Cambridge, MA). 245–254.
1992.PubMed/NCBI
|
|
19
|
Zhang X, Zhang X, Li Q, Sun Z, Song L and
Sun T: Support vector machine applied to study on quantitative
structure-retention relationships of polybrominated diphenyl ether
congeners. Chromatographia. 77:1387–1398. 2014. View Article : Google Scholar
|
|
20
|
Jalaliheravi M and Kyani A: Application of
genetic algorithm-kernel partial least square as a novel nonlinear
feature selection method: Activity of carbonic anhydrase II
inhibitors. Eur J Med Chem. 42:649–659. 2007. View Article : Google Scholar : PubMed/NCBI
|
|
21
|
Fu ZG, Qi MF and Jing Y: Regression
forecast of main steam flow based on mean impact value and support
vector regression. In: Proceedings of Asia Pacific Power and Energy
Engineering Conference. IEEE. (Shanghai). 1–5. 2012.
|
|
22
|
Jiang JL, Su X, Zhang H, Zhang XH and Yuan
YJ: A novel approach to active compounds identification based on
support vector regression model and mean impact value. Chem Biol
Drug Des. 81:650–657. 2013. View Article : Google Scholar : PubMed/NCBI
|
|
23
|
Hagan MT and Beale M: Neural network
design China Machine Press. 2002.
|
|
24
|
Hassoun MH: Fundamentals of artificial
neural networks. Proceedings of the IEEE. 84:9061996. View Article : Google Scholar
|
|
25
|
Boubaker M, Khalifa KB, Girau B, Dogui M
and Bedoui MH: On-line arithmetic based reprogrammable hardware
implementation of LVQ neural network for alertness classification.
IJCSNS Int J Comput Science Netw Secur. 8:260–266. 2008.
|
|
26
|
Jiang FN, He HC, Zhang YQ, Yang DL, Huang
JH, Zhu YX, Mo RJ, Chen G, Yang SB, Chen YR, et al: An integrative
proteomics and interaction network-based classifier for prostate
cancer diagnosis. PLoS One. 8:e639412013. View Article : Google Scholar : PubMed/NCBI
|
|
27
|
Baldi P, Brunak S, Chauvin Y, Andersen CA
and Nielsen H: Assessing the accuracy of prediction algorithms for
classification: An overview. Bioinformatics. 16:412–424. 2000.
View Article : Google Scholar : PubMed/NCBI
|
|
28
|
Sun Y and Goodison S: Optimizing molecular
signatures for predicting prostate cancer recurrence. Prostate.
69:1119–1127. 2009. View Article : Google Scholar : PubMed/NCBI
|
|
29
|
Heinzerling JH, Anthony T, Livingston EH
and Huerta S: Predictors of distant metastasis and mortality in
patients with stage II colorectal cancer. Am Surg. 73:230–238.
2007.PubMed/NCBI
|
|
30
|
He F, Chen H, Yang P. Wu Q, Zhang T, Wang
C, Wei J, Chen Z, Hu H, Li W and Cao J: Gankyrin sustains
PI3K/GSK-3β/β-catenin signal activation and promotes colorectal
cancer aggressiveness and progression. Oncotarget. 7:81156–81171.
2016. View Article : Google Scholar : PubMed/NCBI
|
|
31
|
Akkoca AN, Yanık S, Ozdemir ZT, Cihan FG,
Sayar S, Cincin TG, Cam A and Ozer C: TNM and modified dukes
staging along with the demographic characteristics of patients with
colorectal carcinoma. Int J Clin Exp Med. 7:2828–2835.
2014.PubMed/NCBI
|
|
32
|
Ullenhag GJ, Frödin JE, Jeddi-Tehrani M,
Strigard K, Eriksson E, Samanci A, Choudhury A, Nilsson B, Rossmann
ED, Mosolits S and Mellstedt H: Durable carcinoembryonic antigen
(CEA)-specific humoral and cellular immune responses in colorectal
carcinoma patients vaccinated with recombinant CEA and
granulocyte/macrophage colony-stimulating factor. Clin Cancer Res.
10:3273–3281. 2004. View Article : Google Scholar : PubMed/NCBI
|
|
33
|
Spindler KLG, Christensen IJ, Nielsen HJ,
Jakobsen A and Brünner N: TIMP-1 and CEA as biomarkers in
third-line treatment with irinotecan and cetuximab for metastatic
colorectal cancer. Tumour Biol. 36:4301–4308. 2015. View Article : Google Scholar : PubMed/NCBI
|
|
34
|
Aldulaymi B, Christensen IJ, Sölétormos G,
Jess P, Nielsen SE, Brünner N and Nielsen HJ: Changes in soluble
CEA and TIMP-1 levels during adjuvant chemotherapy for stage III
colon cancer. Anticancer Res. 30:233–237. 2010.PubMed/NCBI
|
|
35
|
Liu LG, Yan XB, Xie RT, Jin ZM and Yang Y:
Stromal expression of vimentin predicts the clinical outcome of
stage II colorectal cancer for high-risk patients. Med Sci Monit.
23:28972017. View Article : Google Scholar : PubMed/NCBI
|
|
36
|
Lee KW, Lee SS, Kim SB, Sohn BH, Lee HS,
Jang HJ, Park YY, Kopetz S, Kim SS, Oh SC and Lee JS: Significant
association of oncogene YAP1 with poor prognosis and cetuximab
resistance in colorectal cancer patients. Clin Cancer Res.
21:357–364. 2015. View Article : Google Scholar : PubMed/NCBI
|
