Predicting breast cancer risk using interacting genetic and demographic factors and machine learning
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Title
Predicting breast cancer risk using interacting genetic and demographic factors and machine learning
Authors
Keywords
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Journal
Scientific Reports
Volume 10, Issue 1, Pages -
Publisher
Springer Science and Business Media LLC
Online
2020-07-06
DOI
10.1038/s41598-020-66907-9
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Note: Only part of the references are listed.- A network analysis to identify mediators of germline-driven differences in breast cancer prognosis
- (2020) Maria Escala-Garcia et al. Nature Communications
- BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors
- (2019) Andrew Lee et al. GENETICS IN MEDICINE
- Identification of Cancer Hallmarks Based on the Gene Co-expression Networks of Seven Cancers
- (2019) Ling-Hao Yu et al. Frontiers in Genetics
- A Hallmark-Based Six-Gene Expression Signature to Assess Colorectal Cancer and Its Recurrence Risk
- (2019) Huiqi Lu et al. Genetic Testing and Molecular Biomarkers
- Metabolomic profiles in breast cancer:a pilot case-control study in the breast cancer family registry
- (2018) Marcelle M. Dougan et al. BMC CANCER
- Joint associations of a polygenic risk score and environmental risk factors for breast cancer in the Breast Cancer Association Consortium
- (2018) Anja Rudolph et al. INTERNATIONAL JOURNAL OF EPIDEMIOLOGY
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities
- (2018) Marinka Zitnik et al. Information Fusion
- Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls
- (2018) Hamid Behravan et al. Scientific Reports
- Pregnancy duration and breast cancer risk
- (2018) Anders Husby et al. Nature Communications
- RE: Bilateral Oophorectomy and Breast Cancer Risk in BRCA1 and BRCA2 Mutation Carriers
- (2017) Emma Nolan et al. JNCI-Journal of the National Cancer Institute
- Association analysis identifies 65 new breast cancer risk loci
- (2017) Kyriaki Michailidou et al. NATURE
- Identification of ten variants associated with risk of estrogen-receptor-negative breast cancer
- (2017) Roger L Milne et al. NATURE GENETICS
- Ensembl 2018
- (2017) Daniel R Zerbino et al. NUCLEIC ACIDS RESEARCH
- A novel BLK-induced tumor model
- (2017) David Leander Petersen et al. TUMOR BIOLOGY
- Development of a predictive miRNA signature for breast cancer risk among high-risk women
- (2017) Nicholas H. Farina et al. Oncotarget
- RE: Bilateral Oophorectomy and Breast Cancer Risk in BRCA1 and BRCA2 Mutation Carriers
- (2017) Emma Nolan et al. JNCI-Journal of the National Cancer Institute
- FGFR2 risk SNPs confer breast cancer risk by augmenting oestrogen responsiveness
- (2016) Thomas M. Campbell et al. CARCINOGENESIS
- Recommendations on breast cancer screening and prevention in the context of implementing risk stratification: impending changes to current policies
- (2016) J. Gagnon et al. Current Oncology
- 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) S. P. Kar et al. Cancer Discovery
- Established breast cancer risk factors and risk of intrinsic tumor subtypes
- (2015) Mollie E. Barnard et al. BIOCHIMICA ET BIOPHYSICA ACTA-REVIEWS ON CANCER
- An epidemiologic risk prediction model for ovarian cancer in Europe: the EPIC study
- (2015) K Li et al. BRITISH JOURNAL OF CANCER
- From candidate gene studies to GWAS and post-GWAS analyses in breast cancer
- (2015) Laura Fachal et al. CURRENT OPINION IN GENETICS & DEVELOPMENT
- Alteration of WWOX in human cancer, a clinical view
- (2015) Izabela Baryła et al. EXPERIMENTAL BIOLOGY AND MEDICINE
- Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer
- (2015) Kyriaki Michailidou et al. NATURE GENETICS
- Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies
- (2015) Florian Mittag et al. PLoS One
- Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data
- (2015) Edwin Wang et al. SEMINARS IN CANCER BIOLOGY
- The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
- (2015) Takaya Saito et al. PLoS One
- Prediction of Potential Cancer-Risk Regions Based on Transcriptome Data: Towards a Comprehensive View
- (2014) Arghavan Alisoltani et al. PLoS One
- Fine-Scale Mapping of the FGFR2 Breast Cancer Risk Locus: Putative Functional Variants Differentially Bind FOXA1 and E2F1
- (2013) Kerstin B. Meyer et al. AMERICAN JOURNAL OF HUMAN GENETICS
- Beyond GWASs: Illuminating the Dark Road from Association to Function
- (2013) Stacey L. Edwards et al. AMERICAN JOURNAL OF HUMAN GENETICS
- The complex genetic landscape of familial breast cancer
- (2013) Lorenzo Melchor et al. HUMAN GENETICS
- Evidence of Gene–Environment Interactions between Common Breast Cancer Susceptibility Loci and Established Environmental Risk Factors
- (2013) Stefan Nickels et al. PLoS Genetics
- Hereditary Breast Cancer: The Era of New Susceptibility Genes
- (2013) Paraskevi Apostolou et al. Biomed Research International
- Breast cancer detection risk in screening mammography after a false-positive result
- (2012) X. Castells et al. Cancer Epidemiology
- Targeting the EGFR signaling pathway in cancer therapy
- (2012) Parthasarathy Seshacharyulu et al. EXPERT OPINION ON THERAPEUTIC TARGETS
- A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance
- (2011) Catherine Meads et al. BREAST CANCER RESEARCH AND TREATMENT
- Risk factors for breast cancer: epidemiological evidence from Japanese studies
- (2011) Motoki Iwasaki et al. CANCER SCIENCE
- Gene–environment interactions in 7610 women with breast cancer: prospective evidence from the Million Women Study
- (2010) Ruth C Travis et al. LANCET
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