Predicting breast cancer risk using interacting genetic and demographic factors and machine learning

[1]  L. Vielva,et al.  Pathways for horizontal gene transfer in bacteria revealed by a global map of their plasmids , 2020, Nature Communications.

[2]  Gary D Bader,et al.  A network analysis to identify mediators of germline-driven differences in breast cancer prognosis , 2020, Nature Communications.

[3]  Hugh Chen,et al.  From local explanations to global understanding with explainable AI for trees , 2020, Nature Machine Intelligence.

[4]  Yili Yang,et al.  A Hallmark-Based Six-Gene Expression Signature to Assess Colorectal Cancer and Its Recurrence Risk. , 2019, Genetic testing and molecular biomarkers.

[5]  Ivo D. Dinov,et al.  Machine learning techniques for personalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models , 2019, Breast Cancer Research.

[6]  Gabriel Erion,et al.  Explainable AI for Trees: From Local Explanations to Global Understanding , 2019, ArXiv.

[7]  Linglin Yu,et al.  Identification of Cancer Hallmarks Based on the Gene Co-expression Networks of Seven Cancers , 2019, Front. Genet..

[8]  M. García-Closas,et al.  BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors , 2019, Genetics in Medicine.

[9]  M. Melbye,et al.  Pregnancy duration and breast cancer risk , 2018, Nature Communications.

[10]  Hamid Behravan,et al.  Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls , 2018, Scientific Reports.

[11]  E. Wang,et al.  eTumorRisk, an algorithm predicts cancer risk based on comutated gene networks in an individual’s germline genome , 2018, bioRxiv.

[12]  Bo Wang,et al.  Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities , 2018, Inf. Fusion.

[13]  Summer S. Han,et al.  Metabolomic profiles in breast cancer:a pilot case-control study in the breast cancer family registry , 2018, BMC Cancer.

[14]  S. Cross,et al.  Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium. , 2018, International journal of epidemiology.

[15]  F. Song,et al.  Assessment of performance of the Gail model for predicting breast cancer risk: a systematic review and meta-analysis with trial sequential analysis , 2018, Breast Cancer Research.

[16]  G. Stein,et al.  Development of a predictive miRNA signature for breast cancer risk among high-risk women , 2017, Oncotarget.

[17]  Astrid Gall,et al.  Ensembl 2018 , 2017, Nucleic Acids Res..

[18]  Michael Jones,et al.  Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer , 2017, Nature Genetics.

[19]  Gary D Bader,et al.  Association analysis identifies 65 new breast cancer risk loci , 2017, Nature.

[20]  J. Visvader,et al.  RE: Bilateral Oophorectomy and Breast Cancer Risk in BRCA1 and BRCA2 Mutation Carriers. , 2017, Journal of the National Cancer Institute.

[21]  J. Berthelsen,et al.  A novel BLK-induced tumor model , 2017, Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine.

[22]  W. Chung,et al.  Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers , 2017, Journal of the National Cancer Institute.

[23]  C. Lee,et al.  Medical big data: promise and challenges , 2017, Kidney research and clinical practice.

[24]  Michael K. Wendt,et al.  The paradoxical functions of EGFR during breast cancer progression , 2017, Signal Transduction and Targeted Therapy.

[25]  B. Knoppers,et al.  Recommendations on breast cancer screening and prevention in the context of implementing risk stratification: impending changes to current policies. , 2016, Current oncology.

[26]  Peter Kraft,et al.  Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types. , 2016, Cancer discovery.

[27]  B. Ponder,et al.  FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness , 2016, Carcinogenesis.

[28]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[29]  S. Astley,et al.  Mammographic density adds accuracy to both the Tyrer-Cuzick and Gail breast cancer risk models in a prospective UK screening cohort , 2015, Breast Cancer Research.

[30]  Andreas Zell,et al.  Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies , 2015, PloS one.

[31]  R. Tamimi,et al.  Established breast cancer risk factors and risk of intrinsic tumor subtypes. , 2015, Biochimica et biophysica acta.

[32]  M. Bell,et al.  Calling Where It Counts: Subordinate Pied Babblers Target the Audience of Their Vocal Advertisements , 2015, PloS one.

[33]  Patrick Neven,et al.  Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer , 2015 .

[34]  I. Gram,et al.  An epidemiologic risk prediction model for ovarian cancer in Europe: the EPIC study , 2015, British Journal of Cancer.

[35]  Takaya Saito,et al.  The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets , 2015, PloS one.

[36]  A. Bednarek,et al.  Alteration of WWOX in human cancer, a clinical view , 2015, Experimental biology and medicine.

[37]  Alison M Dunning,et al.  From candidate gene studies to GWAS and post-GWAS analyses in breast cancer. , 2015, Current opinion in genetics & development.

[38]  Stephen W Duffy,et al.  Risk determination and prevention of breast cancer , 2014, Breast Cancer Research.

[39]  Gos Micklem,et al.  esyN: Network Building, Sharing and Publishing , 2014, PloS one.

[40]  E. Wang,et al.  Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data. , 2014, Seminars in cancer biology.

[41]  R. Scott,et al.  Expanding the genetic basis of copy number variation in familial breast cancer , 2014, Hereditary cancer in clinical practice.

[42]  M. Ebrahimi,et al.  Prediction of Potential Cancer-Risk Regions Based on Transcriptome Data: Towards a Comprehensive View , 2014, PloS one.

[43]  D. Ojcius,et al.  TRAIL-R1 Is a Negative Regulator of Pro-Inflammatory Responses and Modulates Long-Term Sequelae Resulting from Chlamydia trachomatis Infections in Humans , 2014, PloS one.

[44]  Wei Lu,et al.  Fine-scale mapping of the FGFR2 breast cancer risk locus: putative functional variants differentially bind FOXA1 and E2F1. , 2013, American journal of human genetics.

[45]  A. Dunning,et al.  Beyond GWASs: illuminating the dark road from association to function. , 2013, American journal of human genetics.

[46]  J. Benítez,et al.  The complex genetic landscape of familial breast cancer , 2013, Human Genetics.

[47]  Florentia Fostira,et al.  Hereditary Breast Cancer: The Era of New Susceptibility Genes , 2013, BioMed research international.

[48]  Patrick Neven,et al.  Evidence of Gene–Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors , 2013, PLoS genetics.

[49]  W. Chung,et al.  Genome-Wide Association Study in BRCA1 Mutation Carriers Identifies Novel Loci Associated with Breast and Ovarian Cancer Risk , 2013, PLoS genetics.

[50]  X. Castells,et al.  Breast cancer detection risk in screening mammography after a false-positive result. , 2013, Cancer epidemiology.

[51]  Richard D. Riley,et al.  A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance , 2012, Breast Cancer Research and Treatment.

[52]  Moorthy P Ponnusamy,et al.  Targeting the EGFR signaling pathway in cancer therapy , 2012, Expert opinion on therapeutic targets.

[53]  S. Tsugane,et al.  Risk factors for breast cancer: epidemiological evidence from Japanese studies , 2011, Cancer science.

[54]  V. Beral,et al.  Gene–environment interactions in 7610 women with breast cancer: prospective evidence from the Million Women Study , 2010, The Lancet.

[55]  Lior Rokach,et al.  Pattern Classification Using Ensemble Methods , 2009, Series in Machine Perception and Artificial Intelligence.

[56]  Lester L. Peters,et al.  Genome-wide association study identifies novel breast cancer susceptibility loci , 2007, Nature.

[57]  S. Cummings,et al.  Critical assessment of new risk factors for breast cancer: considerations for development of an improved risk prediction model. , 2007, Endocrine-related cancer.

[58]  Jacqueline Clavel,et al.  Progress in the epidemiological understanding of gene-environment interactions in major diseases: cancer. , 2007, Comptes rendus biologies.

[59]  J. Cerhan.  Oral contraceptive use and breast cancer risk: current status. , 2006, Mayo Clinic proceedings.

[60]  P. Argani,et al.  Polymorphisms in estrogen-metabolizing and estrogen receptor genes and the risk of developing breast cancer among a cohort of women with benign breast disease , 2006, BMC Cancer.

[61]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[62]  A. Mannermaa,et al.  Refinement of the 22q12-q13 Breast Cancer–Associated Region: Evidence of TMPRSS6 as a Candidate Gene in an Eastern Finnish Population , 2006, Clinical Cancer Research.

[63]  C. Bunker,et al.  Epidemiological risk factors for breast cancer--a review. , 2005, Nigerian journal of clinical practice.

[64]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition , 2001, Springer Series in Statistics.

[65]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[66]  William D. Foulkes,et al.  Re: Germline BRCA1 Mutations and a Basal Epithelial Phenotype in Breast Cancer , 2004 .

[67]  L. Bégin,et al.  Germline BRCA1 mutations and a basal epithelial phenotype in breast cancer. , 2004, Journal of the National Cancer Institute.

[68]  K. Straif,et al.  Modification of Breast Cancer Risk in Young Women by a Polymorphic Sequence in the egfr Gene , 2004, Cancer Research.

[69]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[70]  Stephen J. Elledge,et al.  Sensing DNA Damage Through ATRIP Recognition of RPA-ssDNA Complexes , 2003, Science.

[71]  Y. Shiloh.  ATM and related protein kinases: safeguarding genome integrity , 2003, Nature Reviews Cancer.

[72]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[73]  B. Weber,et al.  Genetic and hormonal risk factors in breast cancer. , 2000, Journal of the National Cancer Institute.

[74]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[75]  Michael McGill,et al.  Introduction to Modern Information Retrieval , 1983 .

[76]  David Page,et al.  Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants , 2018, AMIA.

[77]  A. Buja,et al.  Loss Functions for Binary Class Probability Estimation and Classification: Structure and Applications , 2005 .

[78]  D. Cox,et al.  Additive and multiplicative models for the joint effect of two risk factors. , 2005, Biostatistics.