| scholarly article | Q13442814 |
| P819 | ADS bibcode | 2013PLSCB...9E2956C |
| P8978 | DBLP publication ID | journals/ploscb/ChenZ13 |
| P356 | DOI | 10.1371/JOURNAL.PCBI.1002956 |
| P953 | full work available at URL | http://dx.plos.org/10.1371/journal.pcbi.1002956 |
| https://europepmc.org/articles/PMC3591263 | ||
| https://europepmc.org/articles/PMC3591263?pdf=render | ||
| https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1002956&type=printable | ||
| P932 | PMC publication ID | 3591263 |
| P698 | PubMed publication ID | 23505362 |
| P5875 | ResearchGate publication ID | 236057856 |
| P50 | author | Weixiong Zhang | Q90160734 |
| P2093 | author name string | Zheng Chen | |
| P2860 | cites work | Interpretation of genetic association studies: markers with replicated highly significant odds ratios may be poor classifiers | Q21563322 |
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| Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls | Q24550675 | ||
| A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity | Q24650037 | ||
| Gene selection and classification of microarray data using random forest. | Q25255911 | ||
| Genetic and genomic analysis of a fat mass trait with complex inheritance reveals marked sex specificity | Q25257837 | ||
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| Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
| Regularization and variable selection via the elastic net | Q62065858 | ||
| Model selection and estimation in regression with grouped variables | Q62065859 | ||
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| Random Forests | Q115707260 | ||
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| Integrating genotypic and expression data in a segregating mouse population to identify 5-lipoxygenase as a susceptibility gene for obesity and bone traits | Q31008848 | ||
| Regularized ROC method for disease classification and biomarker selection with microarray data | Q31012130 | ||
| Increasing the power to detect causal associations by combining genotypic and expression data in segregating populations | Q31109293 | ||
| Integrating genetic and network analysis to characterize genes related to mouse weight | Q33255339 | ||
| Bias in random forest variable importance measures: illustrations, sources and a solution | Q33270401 | ||
| Conditional variable importance for random forests | Q33351067 | ||
| A random forest approach to the detection of epistatic interactions in case-control studies | Q33407925 | ||
| Performance of random forest when SNPs are in linkage disequilibrium. | Q33415367 | ||
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| Analysis of Alzheimer's disease severity across brain regions by topological analysis of gene co-expression networks | Q33711589 | ||
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| Protein networks as logic functions in development and cancer | Q34042616 | ||
| Classification with correlated features: unreliability of feature ranking and solutions | Q34185282 | ||
| Ranking causal variants and associated regions in genome-wide association studies by the support vector machine and random forest | Q34947272 | ||
| IGF-binding protein-2 protects against the development of obesity and insulin resistance | Q34966710 | ||
| Random forests for genetic association studies. | Q35158129 | ||
| Genetics of global gene expression | Q36626609 | ||
| Genetic control of human brain transcript expression in Alzheimer disease | Q37156046 | ||
| Variations in the transcriptome of Alzheimer's disease reveal molecular networks involved in cardiovascular diseases | Q37384944 | ||
| Harnessing gene expression to identify the genetic basis of drug resistance | Q37425278 | ||
| Enriched random forests | Q46440434 | ||
| Relating HIV-1 sequence variation to replication capacity via trees and forests | Q46669496 | ||
| Identifying network communities with a high resolution | Q47833763 | ||
| P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
| P6216 | copyright status | copyrighted | Q50423863 |
| P433 | issue | 3 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | random forest | Q245748 |
| genetic model | Q67149661 | ||
| P304 | page(s) | e1002956 | |
| P577 | publication date | 2013-03-07 | |
| P1433 | published in | PLOS Computational Biology | Q2635829 |
| P1476 | title | Integrative analysis using module-guided random forests reveals correlated genetic factors related to mouse weight | |
| Integrative Analysis Using Module-Guided Random Forests Reveals Correlated Genetic Factors Related to Mouse Weight | |||
| P478 | volume | 9 |
| Q30574210 | A pathway-based data integration framework for prediction of disease progression |
| Q30695172 | Discovering disease-disease associations by fusing systems-level molecular data |
| Q33787477 | Engineering crassulacean acid metabolism to improve water-use efficiency |
| Q45944839 | Evaluation of variable selection methods for random forests and omics data sets. |
| Q30359391 | Genes associated with genotype-specific DNA methylation in squamous cell carcinoma as candidate drug targets. |
| Q30836930 | Integrative omics analysis. A study based on Plasmodium falciparum mRNA and protein data |
| Q30931355 | Network-constrained forest for regularized classification of omics data. |
| Q35534336 | Predicting the phenotypic values of physiological traits using SNP genotype and gene expression data in mice |
| Q40007032 | SNRFCB: sub-network based random forest classifier for predicting chemotherapy benefit on survival for cancer treatment |
| Q35097543 | The social nestwork: tree structure determines nest placement in Kenyan weaverbird colonies |
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