Machine learning applications in cancer prognosis and prediction
暂无分享,去创建一个
Dimitrios I. Fotiadis | Konstantina Kourou | Themis P. Exarchos | Konstantinos P. Exarchos | Michalis V. Karamouzis | D. Fotiadis | M. Karamouzis | T. Exarchos | Konstantina D. Kourou | K. Exarchos | Konstantinos P. Exarchos
[1] Francesco Corea,et al. Introduction to Data , 2017, IBM SPSS Essentials.
[2] Hung-Wen Chiu,et al. Risk classification of cancer survival using ANN with gene expression data from multiple laboratories , 2014, Comput. Biol. Medicine.
[3] Naphtali Rishe,et al. Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer , 2014, Int. J. Medical Eng. Informatics.
[4] U. Pastorino,et al. Assessment of Circulating microRNAs in Plasma of Lung Cancer Patients , 2014, Molecules.
[5] Sanghyun Park,et al. Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning , 2014, PloS one.
[6] อนิรุธ สืบสิงห์,et al. Data Mining Practical Machine Learning Tools and Techniques , 2014 .
[7] Chih-Jen Tseng,et al. Application of machine learning to predict the recurrence-proneness for cervical cancer , 2013, Neural Computing and Applications.
[8] B. Freidlin,et al. Statistical and practical considerations for clinical evaluation of predictive biomarkers. , 2013, Journal of the National Cancer Institute.
[9] Ali Niknejad,et al. Introduction to Computational Intelligence Techniques and Areas of Their Applications in Medicine , 2013 .
[10] Arvin Agah,et al. Introduction to Medical Applications of Artificial Intelligence , 2013 .
[11] Hyunjung Shin,et al. Robust predictive model for evaluating breast cancer survivability , 2013, Eng. Appl. Artif. Intell..
[12] F. Lasheras,et al. Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines , 2013, Expert Syst. Appl..
[13] Hyunjung Shin,et al. Research and applications: Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data , 2013, J. Am. Medical Informatics Assoc..
[14] B. Burwinkel,et al. Cancer diagnosis and prognosis decoded by blood-based circulating microRNA signatures , 2013, Front. Genet..
[15] Yong Wang,et al. iPcc: a novel feature extraction method for accurate disease class discovery and prediction , 2013, Nucleic acids research.
[16] Erhan Bilal,et al. Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling , 2013, PLoS Comput. Biol..
[17] Abbas Toloie Eshlaghy,et al. Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence , 2013 .
[18] Jason H. Moore,et al. Role of genetic heterogeneity and epistasis in bladder cancer susceptibility and outcome: a learning classifier system approach , 2013, J. Am. Medical Informatics Assoc..
[19] Luonan Chen,et al. ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions , 2012, Nucleic acids research.
[20] Sameem Abdul Kareem,et al. Oral cancer prognosis based on clinicopathologic and genomic markers using a hybrid of feature selection and machine learning methods , 2013, BMC Bioinformatics.
[21] Yorgos Goletsis,et al. A multiscale and multiparametric approach for modeling the progression of oral cancer , 2012, BMC Medical Informatics and Decision Making.
[22] Yorgos Goletsis,et al. Multiparametric Decision Support System for the Prediction of Oral Cancer Reoccurrence , 2012, IEEE Transactions on Information Technology in Biomedicine.
[23] Ya Zhang,et al. A gene signature for breast cancer prognosis using support vector machine , 2012, 2012 5th International Conference on BioMedical Engineering and Informatics.
[24] Chen Chen,et al. Revealing metabolite biomarkers for acupuncture treatment by linear programming based feature selection , 2012, BMC Systems Biology.
[25] Rae Woong Park,et al. Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine , 2012, Journal of breast cancer.
[26] K. Zen,et al. Circulating MicroRNAs: a novel class of biomarkers to diagnose and monitor human cancers , 2012, Medicinal research reviews.
[27] J. Cuzick,et al. Prognostic value of a combined estrogen receptor, progesterone receptor, Ki-67, and human epidermal growth factor receptor 2 immunohistochemical score and comparison with the Genomic Health recurrence score in early breast cancer. , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[28] E. Domany,et al. Do Two Machine-Learning Based Prognostic Signatures for Breast Cancer Capture the Same Biological Processes? , 2011, PloS one.
[29] D. Hanahan,et al. Hallmarks of Cancer: The Next Generation , 2011, Cell.
[30] A. Stojadinovic,et al. Development of a Bayesian Belief Network Model for Personalized Prognostic Risk Assessment in Colon Carcinomatosis , 2011, The American surgeon.
[31] Hsueh-Wei Chang,et al. Support Vector Machine-based Prediction for Oral Cancer Using Four SNPs in DNA Repair Genes , 2011 .
[32] H. Heneghan,et al. MiRNAs as biomarkers and therapeutic targets in cancer. , 2010, Current opinion in pharmacology.
[33] R. Hofmann-Wellenhof,et al. A support vector machine for decision support in melanoma recognition , 2010, Experimental dermatology.
[34] J. Shavlik,et al. Breast cancer risk estimation with artificial neural networks revisited , 2010, Cancer.
[35] Reyer Zwiggelaar,et al. Machine Learning Techniques and Mammographic Risk Assessment , 2010, Digital Mammography / IWDM.
[36] S. Koscielny. Why Most Gene Expression Signatures of Tumors Have Not Been Useful in the Clinic , 2010, Science Translational Medicine.
[37] Igor Jurisica,et al. Evaluation of linguistic features useful in extraction of interactions from PubMed; Application to annotating known, high-throughput and predicted interactions in I2D , 2009, Bioinform..
[38] Juli D. Klemm,et al. Data submission and curation for caArray, a standard based microarray data repository system , 2009 .
[39] A. Nobel,et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[40] Mehmet Fatih Akay,et al. Support vector machines combined with feature selection for breast cancer diagnosis , 2009, Expert Syst. Appl..
[41] Jacek M. Zurada,et al. Normalized Mutual Information Feature Selection , 2009, IEEE Transactions on Neural Networks.
[42] Dimitrios I. Fotiadis,et al. Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques , 2008, Comput. Biol. Medicine.
[43] Radford M. Neal. Pattern Recognition and Machine Learning , 2007, Technometrics.
[44] A. Dupuy,et al. Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. , 2007, Journal of the National Cancer Institute.
[45] Dennis B. Troup,et al. NCBI GEO: mining tens of millions of expression profiles—database and tools update , 2006, Nucleic Acids Res..
[46] David S. Wishart,et al. Applications of Machine Learning in Cancer Prediction and Prognosis , 2006, Cancer informatics.
[47] Li Liu,et al. Improved breast cancer prognosis through the combination of clinical and genetic markers , 2007, Bioinform..
[48] Bart De Moor,et al. Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks , 2006, ISMB.
[49] L. Ein-Dor,et al. Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. , 2006, Proceedings of the National Academy of Sciences of the United States of America.
[50] David Page,et al. Predicting cancer susceptibility from single-nucleotide polymorphism data: a case study in multiple myeloma , 2005, BIOKDD.
[51] Dursun Delen,et al. Predicting breast cancer survivability: a comparison of three data mining methods , 2005, Artif. Intell. Medicine.
[52] Dimitrios I. Fotiadis,et al. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines , 2005, Artif. Intell. Medicine.
[53] Stefan Michiels,et al. Prediction of cancer outcome with microarrays: a multiple random validation strategy , 2005, The Lancet.
[54] Daniel T. Larose,et al. Discovering Knowledge in Data: An Introduction to Data Mining , 2005 .
[55] Eytan Domany,et al. Outcome signature genes in breast cancer: is there a unique set? , 2004, Breast Cancer Research.
[56] M. Cronin,et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.
[57] F. Gasco´n,et al. Childhood obesity and hormonal abnormalities associated with cancer risk , 2004, European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation.
[58] J. Listgarten,et al. Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms , 2004, Clinical Cancer Research.
[59] John Rand,et al. Using neural networks to diagnose cancer , 1991, Journal of Medical Systems.
[60] C. Begg,et al. Variations in lung cancer risk among smokers. , 2003, Journal of the National Cancer Institute.
[61] B. Weber,et al. Application of breast cancer risk prediction models in clinical practice. , 2003, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.
[62] Igor Kononenko,et al. Machine learning for medical diagnosis: history, state of the art and perspective , 2001, Artif. Intell. Medicine.
[63] Mark A. Hall,et al. Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning , 1999, ICML.
[64] Nello Cristianini,et al. Large Margin DAGs for Multiclass Classification , 1999, NIPS.
[65] A. Cochran,et al. Prediction of outcome for patients with cutaneous melanoma. , 2003, Pigment cell research.
[66] Ron Kohavi,et al. Wrappers for Feature Subset Selection , 1997, Artif. Intell..
[67] L. Bottaci,et al. Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions , 1997, The Lancet.
[68] L. Freedman,et al. The future of prognostic factors in outcome prediction for patients with cancer , 1992, Cancer.
[69] D V Cicchetti,et al. Neural networks and diagnosis in the clinical laboratory: state of the art. , 1992, Clinical chemistry.
[70] R. Simes,et al. Treatment selection for cancer patients: application of statistical decision theory to the treatment of advanced ovarian cancer. , 1985, Journal of chronic diseases.