About: Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls

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About: Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls Goto Sponge NotDistinct Permalink

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scientific article published in Scientific Reports

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rdf:type	Item
description	artículu científicu (ast) wetenschappelijk artikel (nl) article publié dans la revue scientifique Scientific Reports (fr) наукова стаття, опублікована у вересні 2018 (uk) im September 2018 veröffentlichter wissenschaftlicher Artikel (de) scientific article published in Scientific Reports (en)
publication date	wds:Q58769239-7B0E9356-2474-4BE7-8826-08291F1EA9E6
publication date	2018-09-03 00:00:00Z (xsd:dateTime)
language of work or name	wds:Q58769239-16CC9A16-16F3-48EE-A90F-0DB2337DAF01
language of work or name	English
exact match	wds:Q58769239-5A0C80E3-2732-4313-AF0F-4926EC3AB206
exact match	https://scigraph.springernature.com/pub.10.1038/s41598-018-31573-5
cites work	wds:Q58769239-00F06E93-D75C-4550-BEFD-325085237271 wds:Q58769239-04AF7BC9-4A48-41E0-8AB9-4DB5562E6A17 wds:Q58769239-0997E63B-ADD1-43F0-81D4-EC6639906A5B wds:Q58769239-1600F523-156A-486B-B7EA-4476001C4790 wds:Q58769239-21F00263-5F05-405F-B0C1-0B1F99AA4194 wds:Q58769239-29651305-7030-46D6-BC76-53B753065324 wds:Q58769239-3225E758-9149-4125-9296-D9B79AC36BF3 wds:Q58769239-37EAEC64-AC37-4BC3-8167-CB43AC0E6D70 wds:Q58769239-41A780C3-C302-463D-9730-26E723B67CBE wds:Q58769239-449F3D1E-81DF-482F-9736-541F6F868FBF wds:Q58769239-4562508D-6E1E-4B8B-8ED1-CF04388545E5 wds:Q58769239-490FCB7F-6883-4EB5-BCA2-9EB84F34881B wds:Q58769239-56AC8736-7A96-4F5A-850A-DCF9191F2C70 wds:Q58769239-5C509D89-050A-4537-A8A8-846B4FB5EF82 wds:Q58769239-606B20AE-44C5-407E-8403-40D4F34DA838 wds:Q58769239-62C0ADDE-E423-449E-81DC-40EB230E51A2 wds:Q58769239-64BCFA75-113E-443A-9008-1692A9BFFB6D wds:Q58769239-65AA8503-9754-4620-AB0B-C59AE01A777E wds:Q58769239-6E9BE40F-4E2C-45B9-948B-E5A73AAEFE0B wds:Q58769239-7443163A-8C00-4CA6-91DA-2CFA781FE192 wds:Q58769239-78606E49-1A86-40EA-BEFE-43B8F50462E6 wds:Q58769239-7967BE00-32E1-4779-BE15-02EB0DBAA16D wds:Q58769239-7FE6DA9D-A9C2-4B94-BCF3-C76E810254EF wds:Q58769239-81FBE6CD-BB62-4728-AD8F-139A88BB1105 wds:Q58769239-87124EB5-C3AA-4164-BB5D-D1144CE6E58F wds:Q58769239-91137DB9-E5E0-4A59-810C-C20B557E331E wds:Q58769239-92038ADE-870E-4493-B14E-C697E47768B3 wds:Q58769239-98FC6271-6108-4C6B-B502-282D6709A0C0 wds:Q58769239-A2BB94D2-565E-4A6A-BF0C-B81125153953 wds:Q58769239-A9C46196-E251-412C-AF3D-D426B975985A wds:Q58769239-ACF413F0-49E3-4F0C-9A2F-998C229A1D0F wds:Q58769239-B0D1A955-EE5B-476B-AA71-439BEF67793F wds:Q58769239-BBED6214-4D70-47FF-BE55-9728202D8D1A wds:Q58769239-BC8FE869-C0B7-45A8-B675-68C2C6C2955E wds:Q58769239-BCAC154A-EC6A-43AB-8CAC-894B2B1B618F wds:Q58769239-C2F19538-3BE8-4C4A-9F71-8463210C45D6 wds:Q58769239-C36AB280-CCEF-41A7-812C-BCCA2DF06B87 wds:Q58769239-D28775F6-663F-4DE6-89FC-C20AFEECE308 wds:Q58769239-EA61EB2A-95DB-442B-B4A3-56B948323711 wds:Q58769239-EEE2254D-B4C9-41DD-9908-11342CCC985F
cites work	Genomewide association studies and assessment of the risk of disease Analysis of a complex of statistical variables into principal components modMine: flexible access to modENCODE data Prediction of breast cancer risk based on profiling with common genetic variants Greedy function approximation: A gradient boosting machine The Challenges of Whole-Genome Approaches to Common Diseases Regression Shrinkage and Selection Via the Lasso Accommodating linkage disequilibrium in genetic-association analyses via ridge regression Identification of high-quality cancer prognostic markers and metastasis network modules Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing Ridge Regression: Biased Estimation for Nonorthogonal Problems Screen and clean: a tool for identifying interactions in genome-wide association studies Genome-wide association analysis by lasso penalized logistic regression Genome-wide association analysis of more than 120,000 individuals identifies 15 new susceptibility loci for breast cancer Meta-analysis of 74,046 individuals identifies 11 new susceptibility loci for Alzheimer's disease Hdac3 is essential for the maintenance of chromatin structure and genome stability Predictive genomics: a cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data eTumorType, An Algorithm of Discriminating Cancer Types for Circulating Tumor Cells or Cell-free DNAs in Blood Identification and Construction of Combinatory Cancer Hallmark-Based Gene Signature Sets to Predict Recurrence and Chemotherapy Benefit in Stage II Colorectal Cancer The relationship between Precision-Recall and ROC curves esyN: network building, sharing and publishing Genome-Wide Meta-Analyses of Breast, Ovarian, and Prostate Cancer Association Studies Identify Multiple New Susceptibility Loci Shared by at Least Two Cancer Types Heritability Estimation using a Regularized Regression Approach (HERRA): Applicable to continuous, dichotomous or age-at-onset outcome A Lasso multi-marker mixed model for association mapping with population structure correction. Correction for multiple testing in a gene region A comparative survey of functional footprints of EGFR pathway mutations in human cancers. Pan-Cancer Analysis Links PARK2 to BCL-XL-Dependent Control of Apoptosis Ensembl 2018. Evaluation of Polygenic Risk Scores for Breast and Ovarian Cancer Risk Prediction in BRCA1 and BRCA2 Mutation Carriers The EGFR/miR-338-3p/EYA2 axis controls breast tumor growth and lung metastasis Association analysis identifies 65 new breast cancer risk loci. Genome-wide association studies of drug response and toxicity: an opportunity for genome medicine XGBoost: A Scalable Tree Boosting System Refinement of the 22q12-q13 breast cancer--associated region: evidence of TMPRSS6 as a candidate gene in an eastern Finnish population Case-control analysis of truncating mutations in DNA damage response genes connects TEX15 and FANCD2 with hereditary breast cancer susceptibility Influence of Feature Encoding and Choice of Classifier on Disease Risk Prediction in Genome-Wide Association Studies. A network-driven approach for genome-wide association mapping Fine-Scale Genetic Structure in Finland A miR-20a/MAPK1/c-Myc regulatory feedback loop regulates breast carcinogenesis and chemoresistance.
author name string	wds:Q58769239-0B244733-EE31-4356-99F8-6872516EE8F4 wds:Q58769239-2CB14F12-8542-4A2E-A616-65D757359C15 wds:Q58769239-8A66406A-58AB-4751-96E2-45A7A742CCF5
author name string	Katri Pylkäs Hamid Behravan Maria Tengström
rdfs:label	Machine learning identifies interacting genetic variants contributing to breast cancer risk: A case study in Finnish cases and controls (en)

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