메뉴 건너뛰기




Volumn 113, Issue 3, 2014, Pages 792-808

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

Author keywords

Breast colon cancer; K nearest neighbor; Mega trend diffusion; Na ve Bayes; Random forest; Support vector machines

Indexed keywords

BREAST/COLON CANCER; K-NEAREST NEIGHBOR; MEGA-TREND DIFFUSION; NAÏVE BAYES; RANDOM FOREST; SUPPORT VECTOR MACHINES;

EID: 84894240609     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2014.01.001     Document Type: Article
Times cited : (100)

References (63)
  • 2
    • 66749098111 scopus 로고    scopus 로고
    • WHO
    • Fact sheet No 297 2013, WHO. http://www.who.int/mediacentre/factsheets/fs297/en/.
    • (2013) Fact sheet No 297
  • 5
    • 84885423412 scopus 로고    scopus 로고
    • Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors
    • Huang Y.-H., Chang Y.-C., Huang C.-S., Wu T.-J., Chen J.-H., Chang R.-F. Computer-aided diagnosis of mass-like lesion in breast MRI: Differential analysis of the 3-D morphology between benign and malignant tumors. Computer Methods and Programs in Biomedicine 2013, 112:508-517.
    • (2013) Computer Methods and Programs in Biomedicine , vol.112 , pp. 508-517
    • Huang, Y.-H.1    Chang, Y.-C.2    Huang, C.-S.3    Wu, T.-J.4    Chen, J.-H.5    Chang, R.-F.6
  • 7
    • 44649104406 scopus 로고    scopus 로고
    • Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer
    • Ferino G., González-Díaz H., Delogu G., Podda G., Uriarte E. Using spectral moments of spiral networks based on PSA/mass spectra outcomes to derive quantitative proteome-disease relationships (QPDRs) and predicting prostate cancer. Biochemical and Biophysical Research Communications 2008, 372:320-325.
    • (2008) Biochemical and Biophysical Research Communications , vol.372 , pp. 320-325
    • Ferino, G.1    González-Díaz, H.2    Delogu, G.3    Podda, G.4    Uriarte, E.5
  • 8
    • 60749111710 scopus 로고    scopus 로고
    • Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices
    • Munteanu C.R., Magalhães A.L., Uriarte E., González-Díaz H. Multi-target QPDR classification model for human breast and colon cancer-related proteins using star graph topological indices. Journal of Theoretical Biology 2009, 257:303-311.
    • (2009) Journal of Theoretical Biology , vol.257 , pp. 303-311
    • Munteanu, C.R.1    Magalhães, A.L.2    Uriarte, E.3    González-Díaz, H.4
  • 10
    • 0021914514 scopus 로고
    • Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer
    • Simes R.J. Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer. Journal of Chronic Diseases 1985, 38:171-186.
    • (1985) Journal of Chronic Diseases , vol.38 , pp. 171-186
    • Simes, R.J.1
  • 11
    • 33846014233 scopus 로고    scopus 로고
    • A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis
    • Sahan S., Polat K., Kodaz H., Günes S. A new hybrid method based on fuzzy-artificial immune system and k-nn algorithm for breast cancer diagnosis. Computers in Biology and Medicine 2007, 37:415-423.
    • (2007) Computers in Biology and Medicine , vol.37 , pp. 415-423
    • Sahan, S.1    Polat, K.2    Kodaz, H.3    Günes, S.4
  • 12
    • 0028219772 scopus 로고
    • Application of backpropagation neural networks to diagnosis of breast and ovarian cancer
    • Wilding, Peter, Morgan Mark A., Grygotis Anthony E., Shoffner Mann A., Rosato E.F. Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. Cancer Letters 1994, 77:145-153.
    • (1994) Cancer Letters , vol.77 , pp. 145-153
    • Peter, P.1    Morgan, M.A.2    Grygotis, A.E.3    Shoffner, M.A.4    Rosato, E.F.5
  • 15
    • 0036254780 scopus 로고    scopus 로고
    • Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: statistical, neural network and fuzzy approaches
    • Seker H., Odetayo M.O., Petrovic D. Assessment of nodal involvement and survival analysis in breast cancer patients using image cytometric data: statistical, neural network and fuzzy approaches. Anticancer Research 2002, 22.
    • (2002) Anticancer Research , vol.22
    • Seker, H.1    Odetayo, M.O.2    Petrovic, D.3
  • 16
    • 33746333850 scopus 로고    scopus 로고
    • SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees
    • Ogul H., Mumcuoglu E. SVM-based detection of distant protein structural relationships using pairwise probabilistic suffix trees. Computational Biology and Chemistry 2006, 30:292-299.
    • (2006) Computational Biology and Chemistry , vol.30 , pp. 292-299
    • Ogul, H.1    Mumcuoglu, E.2
  • 17
    • 77958476034 scopus 로고    scopus 로고
    • Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm
    • Emmanuel M., Alvarez M.M., Trevino V. Compact cancer biomarkers discovery using a swarm intelligence feature selection algorithm. Computational Biology and Chemistry 2010, 34:244-250.
    • (2010) Computational Biology and Chemistry , vol.34 , pp. 244-250
    • Emmanuel, M.1    Alvarez, M.M.2    Trevino, V.3
  • 18
    • 84880035900 scopus 로고    scopus 로고
    • Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set
    • Sun T., Wang J., Li X., Lv P., Liu F., Luo Y., Gao Q., Zhu H., Guo X. Comparative evaluation of support vector machines for computer aided diagnosis of lung cancer in CT based on a multi-dimensional data set. Computer Methods and Programs in Biomedicine 2013, 111:519-524.
    • (2013) Computer Methods and Programs in Biomedicine , vol.111 , pp. 519-524
    • Sun, T.1    Wang, J.2    Li, X.3    Lv, P.4    Liu, F.5    Luo, Y.6    Gao, Q.7    Zhu, H.8    Guo, X.9
  • 20
    • 0031143198 scopus 로고    scopus 로고
    • Off-line, handwritten numeral recognition by perturbation
    • Ha T.M., Bunke H. Off-line, handwritten numeral recognition by perturbation. Pattern Analysis and Machine Intelligence 1997, 19:535-539.
    • (1997) Pattern Analysis and Machine Intelligence , vol.19 , pp. 535-539
    • Ha, T.M.1    Bunke, H.2
  • 21
    • 33748743279 scopus 로고    scopus 로고
    • Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge
    • Li D.C., Wu C.S., Tsai T.I., Lina Y.S. Using mega-trend-diffusion and artificial samples in small data set learning for early flexible manufacturing system scheduling knowledge. Computers & Operations Research 2007, 34:966-982.
    • (2007) Computers & Operations Research , vol.34 , pp. 966-982
    • Li, D.C.1    Wu, C.S.2    Tsai, T.I.3    Lina, Y.S.4
  • 22
    • 77952554315 scopus 로고    scopus 로고
    • A learning method for the class imbalance problem with medical data sets
    • Li D.C., Liu C.W., Hu S.C. A learning method for the class imbalance problem with medical data sets. Computers in Biology and Medicine 2010, 40:509-518.
    • (2010) Computers in Biology and Medicine , vol.40 , pp. 509-518
    • Li, D.C.1    Liu, C.W.2    Hu, S.C.3
  • 23
    • 84868522404 scopus 로고    scopus 로고
    • The classification of cancer stage microarray data
    • Chen C.-K. The classification of cancer stage microarray data. Computer Methods and Programs in Biomedicine 2012, 108:1070-1077.
    • (2012) Computer Methods and Programs in Biomedicine , vol.108 , pp. 1070-1077
    • Chen, C.-K.1
  • 25
    • 77957898898 scopus 로고    scopus 로고
    • Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images
    • Lee M.-Y., Yang C.-S. Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images. Computer Methods and Programs in Biomedicine 2010, 100:269-282.
    • (2010) Computer Methods and Programs in Biomedicine , vol.100 , pp. 269-282
    • Lee, M.-Y.1    Yang, C.-S.2
  • 26
    • 77956651863 scopus 로고    scopus 로고
    • An intelligent decision support algorithm for diagnosis of colorectal cancer through serum tumor markers
    • Shi J., Su Q., Zhang C., Huang G., Zhu Y. An intelligent decision support algorithm for diagnosis of colorectal cancer through serum tumor markers. Computer Methods and Programs in Biomedicine 2010, 100:97-107.
    • (2010) Computer Methods and Programs in Biomedicine , vol.100 , pp. 97-107
    • Shi, J.1    Su, Q.2    Zhang, C.3    Huang, G.4    Zhu, Y.5
  • 27
    • 0028219772 scopus 로고
    • Application of backpropagation neural networks to diagnosis of breast and ovarian cancer
    • Wilding P., Mark A.M., Grygotis E.A., Shoffner A.M., Rosato F.E. Application of backpropagation neural networks to diagnosis of breast and ovarian cancer. Cancer Letters 1994, 77:145-153.
    • (1994) Cancer Letters , vol.77 , pp. 145-153
    • Wilding, P.1    Mark, A.M.2    Grygotis, E.A.3    Shoffner, A.M.4    Rosato, F.E.5
  • 28
    • 84877054792 scopus 로고    scopus 로고
    • Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples
    • Kalinli A., Sarikoc F., Akgun H., Ozturk F. Performance comparison of machine learning methods for prognosis of hormone receptor status in breast cancer tissue samples. Computer Methods and Programs in Biomedicine 2013, 110:298-307.
    • (2013) Computer Methods and Programs in Biomedicine , vol.110 , pp. 298-307
    • Kalinli, A.1    Sarikoc, F.2    Akgun, H.3    Ozturk, F.4
  • 29
    • 84867404131 scopus 로고    scopus 로고
    • Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA)
    • Sartakhti J.S., Zangooei M.H., Mozafari K. Hepatitis disease diagnosis using a novel hybrid method based on support vector machine and simulated annealing (SVM-SA). Computer Methods and Programs in Biomedicine 2012, 108:570-579.
    • (2012) Computer Methods and Programs in Biomedicine , vol.108 , pp. 570-579
    • Sartakhti, J.S.1    Zangooei, M.H.2    Mozafari, K.3
  • 30
    • 60349124637 scopus 로고    scopus 로고
    • Discriminating prostate cancer patients from control group with connectivity indices
    • González-Díaz H., Ferino G., Podda G., Uriarte E. Discriminating prostate cancer patients from control group with connectivity indices. ECSOC 2007, 11:1-10.
    • (2007) ECSOC , vol.11 , pp. 1-10
    • González-Díaz, H.1    Ferino, G.2    Podda, G.3    Uriarte, E.4
  • 33
    • 56349123043 scopus 로고    scopus 로고
    • An expert system for detection of breast cancer based on association rules and neural network
    • Karabatak M., Ince M.C. An expert system for detection of breast cancer based on association rules and neural network. Expert Systems with Applications 2009, 36:3465-3469.
    • (2009) Expert Systems with Applications , vol.36 , pp. 3465-3469
    • Karabatak, M.1    Ince, M.C.2
  • 35
    • 0032960792 scopus 로고    scopus 로고
    • Obtaining interpretable fuzzy classification rules from medical data
    • Nauck D., Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 1999, 16:149-169.
    • (1999) Artificial Intelligence in Medicine , vol.16 , pp. 149-169
    • Nauck, D.1    Kruse, R.2
  • 37
    • 77949806919 scopus 로고    scopus 로고
    • Colon cancer prediction with genetic profiles using intelligent techniques
    • Alladi S.M., Shinde P.S., Ravi V., Murthy U.S.N. Colon cancer prediction with genetic profiles using intelligent techniques. Bioinformation 2008, 3:130-133.
    • (2008) Bioinformation , vol.3 , pp. 130-133
    • Alladi, S.M.1    Shinde, P.S.2    Ravi, V.3    Murthy, U.S.N.4
  • 38
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • Furey T.S., Christianini N., Duffey N., Bednarski D.W., Schummer M., Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 2000, 16:906-914.
    • (2000) Bioinformatics , vol.16 , pp. 906-914
    • Furey, T.S.1    Christianini, N.2    Duffey, N.3    Bednarski, D.W.4    Schummer, M.5    Haussler, D.6
  • 39
    • 29444440889 scopus 로고    scopus 로고
    • Tumor tissue identification based on gene expression data using DWT feature extraction and PNN classifier
    • Sun G., Dong X., Xu G. Tumor tissue identification based on gene expression data using DWT feature extraction and PNN classifier. Neurocomputing 2006, 69:387.
    • (2006) Neurocomputing , vol.69 , pp. 387
    • Sun, G.1    Dong, X.2    Xu, G.3
  • 40
    • 0036139278 scopus 로고    scopus 로고
    • Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
    • Li L., Weinberg C.R., Darden T.A., Pedersen L.G. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 2001, 17:1131.
    • (2001) Bioinformatics , vol.17 , pp. 1131
    • Li, L.1    Weinberg, C.R.2    Darden, T.A.3    Pedersen, L.G.4
  • 42
    • 3242773098 scopus 로고    scopus 로고
    • Prediction of protein function in the absence of significant sequence similarity
    • Dobson P.D., Cai Y.D., Stapley B.J., Doig A.J. Prediction of protein function in the absence of significant sequence similarity. Current Medicinal Chemistry 2004, 11:2135-2142.
    • (2004) Current Medicinal Chemistry , vol.11 , pp. 2135-2142
    • Dobson, P.D.1    Cai, Y.D.2    Stapley, B.J.3    Doig, A.J.4
  • 43
    • 33749993417 scopus 로고    scopus 로고
    • The consensus coding sequences of human breast and colorectal cancers
    • Sjoblom T., Jones S., Wood L.D., Parsons W., Lin J., Barber T.D., et al. The consensus coding sequences of human breast and colorectal cancers. Science 2006, 314:268-274.
    • (2006) Science , vol.314 , pp. 268-274
    • Sjoblom, T.1    Jones, S.2    Wood, L.D.3    Parsons, W.4    Lin, J.5    Barber, T.D.6
  • 45
    • 76449106967 scopus 로고    scopus 로고
    • Predicting protein subcellular location: exploiting amino acid based sequence of feature spaces and fusion of diverse classifiers
    • Khan A., Majid A., Tae-Sun C. Predicting protein subcellular location: exploiting amino acid based sequence of feature spaces and fusion of diverse classifiers. Amino Acids 2010, 38:347-350.
    • (2010) Amino Acids , vol.38 , pp. 347-350
    • Khan, A.1    Majid, A.2    Tae-Sun, C.3
  • 46
    • 33748287103 scopus 로고    scopus 로고
    • Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network
    • Chen C., Zhou X., Tian Y., Zou X., Cai P. Predicting protein structural class with pseudo-amino acid composition and support vector machine fusion network. Analytical Biochemistry 2006, 357:116-121.
    • (2006) Analytical Biochemistry , vol.357 , pp. 116-121
    • Chen, C.1    Zhou, X.2    Tian, Y.3    Zou, X.4    Cai, P.5
  • 47
    • 12744279642 scopus 로고    scopus 로고
    • Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes
    • Chou K.C. Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes. Bioinformatics 2005, 21:10-19.
    • (2005) Bioinformatics , vol.21 , pp. 10-19
    • Chou, K.C.1
  • 48
    • 67650739405 scopus 로고    scopus 로고
    • Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis
    • Li Z.C., Zhou X.B., Dai Z., Zou X.Y. Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis. Amino Acids 2009, 37:415-425.
    • (2009) Amino Acids , vol.37 , pp. 415-425
    • Li, Z.C.1    Zhou, X.B.2    Dai, Z.3    Zou, X.Y.4
  • 49
    • 78649324596 scopus 로고    scopus 로고
    • A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction
    • Sahu S.S., Panda G. A novel feature representation method based on Chou's pseudo amino acid composition for protein structural class prediction. Computational Biology and Chemistry 2010, 34:320-327.
    • (2010) Computational Biology and Chemistry , vol.34 , pp. 320-327
    • Sahu, S.S.1    Panda, G.2
  • 50
    • 77955964484 scopus 로고    scopus 로고
    • Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform
    • Qiu J.D., Huang J.H., Shi S.P., Liang R.P. Using the concept of Chou's pseudo amino acid composition to predict enzyme family classes: an approach with support vector machine based on discrete wavelet transform. Protein and Peptide Letters 2010, 17:715-722.
    • (2010) Protein and Peptide Letters , vol.17 , pp. 715-722
    • Qiu, J.D.1    Huang, J.H.2    Shi, S.P.3    Liang, R.P.4
  • 51
    • 42749090472 scopus 로고    scopus 로고
    • The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition
    • Lin H. The modified Mahalanobis Discriminant for predicting outer membrane proteins by using Chou's pseudo amino acid composition. Journal of Theoretical Biology 2008, 252:350-356.
    • (2008) Journal of Theoretical Biology , vol.252 , pp. 350-356
    • Lin, H.1
  • 52
    • 33845536164 scopus 로고    scopus 로고
    • The class imbalance problem: a systematic study
    • Japkowicz N., Stephen S. The class imbalance problem: a systematic study. Intelligent Data Analysis 2002, 6:429-449.
    • (2002) Intelligent Data Analysis , vol.6 , pp. 429-449
    • Japkowicz, N.1    Stephen, S.2
  • 53
    • 1442275185 scopus 로고    scopus 로고
    • Learning when training data are costly: the effect of class distribution on tree induction
    • Weiss G.M., Provost F.J. Learning when training data are costly: the effect of class distribution on tree induction. Journal of Artificial Intelligence Research 2003, 19:315-354.
    • (2003) Journal of Artificial Intelligence Research , vol.19 , pp. 315-354
    • Weiss, G.M.1    Provost, F.J.2
  • 59
    • 79959741759 scopus 로고    scopus 로고
    • CE-PLoc: an ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition
    • Khan A., Majid A., Hayat M. CE-PLoc: an ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition. Computational Biology and Chemistry 2011, 35:218-229.
    • (2011) Computational Biology and Chemistry , vol.35 , pp. 218-229
    • Khan, A.1    Majid, A.2    Hayat, M.3
  • 60
    • 77958497871 scopus 로고    scopus 로고
    • Prediction of cyclin proteins using Chous pseudo amino acid composition
    • Mohabatkar H. Prediction of cyclin proteins using Chous pseudo amino acid composition. Protein and Peptide Letters 2010, 17:1207.
    • (2010) Protein and Peptide Letters , vol.17 , pp. 1207
    • Mohabatkar, H.1
  • 61
    • 78649756877 scopus 로고    scopus 로고
    • Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition
    • Hayat M., Khan A. Predicting membrane protein types by fusing composite protein sequence features into pseudo amino acid composition. Journal of Theoretical Biology 2011, 271:10-17.
    • (2011) Journal of Theoretical Biology , vol.271 , pp. 10-17
    • Hayat, M.1    Khan, A.2
  • 62
    • 26944454497 scopus 로고    scopus 로고
    • ROC graphs: Notes and practical considerations for researchers
    • Tom F. ROC graphs: Notes and practical considerations for researchers. Machine Learning 2004, 31:1-38.
    • (2004) Machine Learning , vol.31 , pp. 1-38
    • Tom, F.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.