Serum levels of chemical elements in esophageal squamous cell carcinoma in Anyang, China: a case-control study based on machine learning methods
暂无分享,去创建一个
Zhongxue Chen | Zhonghu He | Zhonghu He | Tiebing Liu | Zhongxue Chen | Tiebing Liu | Tong Lin | Yucheng Lin | Chaoting Zhang | Lailai Yan | Jingyu Wang | Lailai Yan | Chaoting Zhang | Jingyu Wang | Yucheng Lin | Tong Lin | Tie-bing Liu
[1] Tom Fawcett,et al. Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions , 1997, KDD.
[2] G. M. Gulzar,et al. Association Between Copper Excess, Zinc Deficiency, and TP53 Mutations in Esophageal Squamous Cell Carcinoma From Kashmir Valley, India—A High Risk Area , 2008, Nutrition and cancer.
[3] C. Abnet,et al. Prospective study of serum selenium concentrations and esophageal and gastric cardia cancer, heart disease, stroke, and total death. , 2004, The American journal of clinical nutrition.
[4] Zhonghu He,et al. The Anyang Esophageal Cancer Cohort Study: Study Design, Implementation of Fieldwork, and Use of Computer-Aided Survey System , 2012, PloS one.
[5] E. Ho. Zinc deficiency, DNA damage and cancer risk. , 2004, The Journal of nutritional biochemistry.
[6] P. Knekt,et al. Serum vitamin E, serum selenium and the risk of gastrointestinal cancer , 1988, International journal of cancer.
[7] Thibault Helleputte,et al. Robust biomarker identification for cancer diagnosis with ensemble feature selection methods , 2010, Bioinform..
[8] Ahmedin Jemal,et al. Global Cancer Incidence and Mortality Rates and Trends—An Update , 2015, Cancer Epidemiology, Biomarkers & Prevention.
[9] Vladimir Vapnik,et al. Statistical learning theory , 1998 .
[10] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[11] D. Ahlquist,et al. Molecular Detection of Gastrointestinal Neoplasia: Innovations in Early Detection and Screening. , 2016, Gastroenterology clinics of North America.
[12] Aquil Ahmad,et al. Studies on Association Between Copper Excess, Zinc Deficiency and TP53 Mutations in Esophageal Squamous Cell Carcinoma From Kashmir Valley, India-A High Risk Area. , 2007, International journal of health sciences.
[13] M. Hatt,et al. Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal Cancer , 2011, The Journal of Nuclear Medicine.
[14] R. Mumper,et al. Elevated copper and oxidative stress in cancer cells as a target for cancer treatment. , 2009, Cancer treatment reviews.
[15] H. Cunzhi,et al. Serum and tissue levels of six trace elements and copper/zinc ratio in patients with cervical cancer and uterine myoma , 2003, Biological Trace Element Research.
[16] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[17] J. Rice. Mathematical Statistics and Data Analysis , 1988 .
[18] Xiaofei He,et al. Locality Preserving Projections , 2003, NIPS.
[19] Ameet Talwalkar,et al. Foundations of Machine Learning , 2012, Adaptive computation and machine learning.
[20] Tiebing Liu,et al. Comparative Study on Serum Levels of 10 Trace Elements in Schizophrenia , 2015, PloS one.
[21] A. Jemal,et al. Global cancer statistics, 2012 , 2015, CA: a cancer journal for clinicians.
[22] Ma El,et al. Ion-exchange chromatography in simultaneous determination of serum copper and zinc levels in patients with cancer of digestive tract. , 1993 .
[23] A. Jemal,et al. Global cancer statistics , 2011, CA: a cancer journal for clinicians.
[24] E. B. Andersen,et al. Modern factor analysis , 1961 .
[25] D. Milde,et al. Serum levels of selenium, manganese, copper, and iron in colorectal cancer patients , 2001, Biological Trace Element Research.
[26] Ethem Alpaydin. Introduction to machine learning, 2rd ed , 2014 .
[27] J. Ioannidis,et al. Strengthening the reporting of genetic association studies (STREGA): an extension of the STROBE statement , 2009, European Journal of Epidemiology.
[28] M. Kubát. An Introduction to Machine Learning , 2017, Springer International Publishing.
[29] Chih-Jen Lin,et al. A Practical Guide to Support Vector Classication , 2008 .
[30] R. Stephens,et al. Unique microRNA molecular profiles in lung cancer diagnosis and prognosis. , 2006, Cancer cell.
[31] Alex M. Andrew,et al. Boosting: Foundations and Algorithms , 2012 .
[32] Patrick Neven,et al. Vitamin D status at breast cancer diagnosis: correlation with tumor characteristics, disease outcome, and genetic determinants of vitamin D insufficiency. , 2012, Carcinogenesis.
[33] L. Poston,et al. Selenium in reproductive health. , 2012, American journal of obstetrics and gynecology.
[34] John T. Wei,et al. Combining urinary detection of TMPRSS2:ERG and PCA3 with serum PSA to predict diagnosis of prostate cancer. , 2013, Urologic oncology.
[35] S. Franceschi,et al. EPIDEMIOLOGY OF ESOPHAGEAL CANCER , 2013 .
[36] Eric O. Postma,et al. Dimensionality Reduction: A Comparative Review , 2008 .
[37] W. Budach,et al. Esophageal cancer: Clinical Practice Guidelines for diagnosis, treatment and follow-up. , 2010, Annals of oncology : official journal of the European Society for Medical Oncology.
[38] Ying Wang,et al. Correlations of Trace Element Levels in the Diet, Blood, Urine, and Feces in the Chinese Male , 2012, Biological trace element research.