| scholarly article | Q13442814 |
| P953 | full work available at URL | http://www.jmlr.org/papers/volume17/15-444/15-444.pdf |
| P856 | official website | http://www.jmlr.org/papers/v17/15-444.html |
| P932 | PMC publication ID | 5446896 |
| P698 | PubMed publication ID | 28559747 |
| P2093 | author name string | Jie Liu | |
| Jun Fan | |||
| Ming Yuan | |||
| David Page | |||
| Irene M Ong | |||
| Peggy Peissig | |||
| Elizabeth Burnside | |||
| Yirong Wu | |||
| P2860 | cites work | Large-scale genotyping identifies 41 new loci associated with breast cancer risk | Q29416989 |
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| New genetic variants improve personalized breast cancer diagnosis | Q35098946 | ||
| Comparing the value of mammographic features and genetic variants in breast cancer risk prediction | Q35570099 | ||
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| Using Hamming Distance as Information for SNP-Sets Clustering and Testing in Disease Association Studies. | Q35753730 | ||
| Comparing Mammography Abnormality Features to Genetic Variants in the Prediction of Breast Cancer in Women Recommended for Breast Biopsy | Q36387040 | ||
| Prediction of breast cancer risk based on profiling with common genetic variants | Q36583004 | ||
| Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk | Q36858843 | ||
| A comprehensive methodology for determining the most informative mammographic features | Q37196223 | ||
| Value of adding single-nucleotide polymorphism genotypes to a breast cancer risk model | Q37246495 | ||
| Incorporating group correlations in genome-wide association studies using smoothed group Lasso. | Q41492147 | ||
| Modeling Disease Progression via Fused Sparse Group Lasso | Q41876019 | ||
| Marshfield Clinic Personalized Medicine Research Project (PMRP): design, methods and recruitment for a large population-based biobank | Q57194133 | ||
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | breast cancer | Q128581 |
| P577 | publication date | 2016-12-01 | |
| P1433 | published in | Journal of Machine Learning Research | Q1660383 |
| P1476 | title | Structure-Leveraged Methods in Breast Cancer Risk Prediction | |
| P478 | volume | 17 |
| Q91989559 | Improving breast cancer risk prediction by using demographic risk factors, abnormality features on mammograms and genetic variants |
| Q55315486 | Quantifying predictive capability of electronic health records for the most harmful breast cancer. |
| Q55074868 | Utility of Genetic Testing in Addition to Mammography for Determining Risk of Breast Cancer Depends on Patient Age. |
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