| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2013PLoSO...871114F |
| P356 | DOI | 10.1371/JOURNAL.PONE.0071114 |
| P932 | PMC publication ID | 3737136 |
| P698 | PubMed publication ID | 23940698 |
| P5875 | ResearchGate publication ID | 255791182 |
| P2093 | author name string | Yen-Feng Chiu | |
| Yao-Hwei Fang | |||
| P2860 | cites work | Association screening of common and rare genetic variants by penalized regression | Q34514291 |
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| Methods for detecting associations with rare variants for common diseases: application to analysis of sequence data | Q33359015 | ||
| A groupwise association test for rare mutations using a weighted sum statistic | Q33408787 | ||
| Comparing artificial neural networks, general linear models and support vector machines in building predictive models for small interfering RNAs | Q33511756 | ||
| A data-adaptive sum test for disease association with multiple common or rare variants | Q33562753 | ||
| An evaluation of R2 as an inadequate measure for nonlinear models in pharmacological and biochemical research: a Monte Carlo approach | Q33596726 | ||
| A new testing strategy to identify rare variants with either risk or protective effect on disease | Q33815362 | ||
| Testing for an unusual distribution of rare variants | Q33847792 | ||
| Rare-variant association testing for sequencing data with the sequence kernel association test | Q33954046 | ||
| Disease risk prediction with rare and common variants | Q34176843 | ||
| Extending rare-variant testing strategies: analysis of noncoding sequence and imputed genotypes | Q34300842 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 8 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | support vector machine | Q282453 |
| P304 | page(s) | e71114 | |
| P577 | publication date | 2013-08-07 | |
| P1433 | published in | PLOS One | Q564954 |
| P1476 | title | A novel support vector machine-based approach for rare variant detection | |
| P478 | volume | 8 |
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