scholarly article | Q13442814 |
P8978 | DBLP publication ID | journals/widm/BoulesteixJKK12 |
P356 | DOI | 10.1002/WIDM.1072 |
P50 | author | Anne-Laure Boulesteix | Q70962748 |
Silke Janitza | Q70964025 | ||
P2093 | author name string | Inke R. König | |
Jochen Kruppa | |||
P2860 | cites work | Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 |
Simple statistical models predict C-to-U edited sites in plant mitochondrial RNA | Q24798198 | ||
Few amino acid positions in rpoB are associated with most of the rifampin resistance in Mycobacterium tuberculosis | Q24804809 | ||
Screening large-scale association study data: exploiting interactions using random forests | Q24809535 | ||
Gene selection and classification of microarray data using random forest. | Q25255911 | ||
Methods for identifying SNP interactions: a review on variations of Logic Regression, Random Forest and Bayesian logistic regression | Q37850639 | ||
EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis | Q42251040 | ||
A genome-wide screen of gene-gene interactions for rheumatoid arthritis susceptibility | Q42685398 | ||
Microarray-based classification and clinical predictors: on combined classifiers and additional predictive value | Q45506394 | ||
Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: biological validation with functional neuroimaging. | Q45963155 | ||
Machine learning in genome-wide association studies. | Q45963331 | ||
Predictor correlation impacts machine learning algorithms: implications for genomic studies. | Q45963777 | ||
Random forests for classification in ecology. | Q46023145 | ||
Binding profiles of chromatin-modifying proteins are predictive for transcriptional activity and promoter-proximal pausing | Q46091936 | ||
Enriched random forests | Q46440434 | ||
Pathway analysis of single-nucleotide polymorphisms potentially associated with glioblastoma multiforme susceptibility using random forests. | Q46664876 | ||
Relating HIV-1 sequence variation to replication capacity via trees and forests | Q46669496 | ||
A framework to identify physiological responses in microarray-based gene expression studies: selection and interpretation of biologically relevant genes | Q46830631 | ||
Random forest: a classification and regression tool for compound classification and QSAR modeling | Q47370701 | ||
Case studies in reproducibility. | Q50117543 | ||
AUC-RF: a new strategy for genomic profiling with random forest. | Q51519759 | ||
Random forest Gini importance favours SNPs with large minor allele frequency: impact, sources and recommendations. | Q51532942 | ||
Letter to the editor: Stability of Random Forest importance measures. | Q51709553 | ||
Practical experiences on the necessity of external validation. | Q51904901 | ||
Evaluating the ability of tree-based methods and logistic regression for the detection of SNP-SNP interaction. | Q51938639 | ||
Unbiased Recursive Partitioning: A Conditional Inference Framework | Q56340213 | ||
Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula | Q56766803 | ||
Gradient modeling of conifer species using random forests | Q57262614 | ||
Identifying SNPs predictive of phenotype using random forests | Q57363161 | ||
Validation in Genetic Association Studies | Q57619843 | ||
High-dimensional pharmacogenetic prediction of a continuous trait using machine learning techniques with application to warfarin dose prediction in African Americans | Q28740452 | ||
A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification | Q30482925 | ||
Microarray data mining with visual programming. | Q30952033 | ||
Permutation importance: a corrected feature importance measure | Q31140590 | ||
Proteomic mass spectra classification using decision tree based ensemble methods | Q33214986 | ||
The challenge for genetic epidemiologists: how to analyze large numbers of SNPs in relation to complex diseases | Q33240664 | ||
Bias in random forest variable importance measures: illustrations, sources and a solution | Q33270401 | ||
Two-stage approach for identifying single-nucleotide polymorphisms associated with rheumatoid arthritis using random forests and Bayesian networks | Q33333017 | ||
Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests | Q33333034 | ||
Random forests, a novel approach for discrimination of fish populations using parasites as biological tags | Q33345669 | ||
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 | ||
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data | Q33480662 | ||
Identification of genes and haplotypes that predict rheumatoid arthritis using random forests. | Q33519360 | ||
Detecting significant single-nucleotide polymorphisms in a rheumatoid arthritis study using random forests. | Q33519362 | ||
Selection of important variables by statistical learning in genome-wide association analysis | Q33519367 | ||
The behaviour of random forest permutation-based variable importance measures under predictor correlation | Q33534890 | ||
On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data | Q33588377 | ||
An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings | Q33603699 | ||
Maximal conditional chi-square importance in random forests | Q33706441 | ||
Class prediction for high-dimensional class-imbalanced data | Q33723485 | ||
Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers | Q33947336 | ||
A strategy analysis for genetic association studies with known inbreeding | Q33964390 | ||
Prediction of conformational B-cell epitopes from 3D structures by random forests with a distance-based feature | Q33994173 | ||
Power of data mining methods to detect genetic associations and interactions | Q34024897 | ||
SNPInterForest: a new method for detecting epistatic interactions | Q34094340 | ||
Use of wrapper algorithms coupled with a random forests classifier for variable selection in large-scale genomic association studies | Q34307433 | ||
Letter to the editor: on the stability and ranking of predictors from random forest variable importance measures | Q35109889 | ||
Random forests for genetic association studies. | Q35158129 | ||
The use of classification trees for bioinformatics | Q35896436 | ||
Variation within DNA repair pathway genes and risk of multiple sclerosis | Q36856159 | ||
Patient-centered yes/no prognosis using learning machines | Q37369606 | ||
P433 | issue | 6 | |
P921 | main subject | bioinformatics | Q128570 |
computational biology | Q177005 | ||
random forest | Q245748 | ||
P304 | page(s) | 493-507 | |
P577 | publication date | 2012-10-18 | |
P1433 | published in | Wiley interdisciplinary reviews. Data mining and knowledge discovery | Q27723849 |
P1476 | title | Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics | |
P478 | volume | 2 |
Q94160154 | A multiple classifier system identifies novel cannabinoid CB2 receptor ligands |
Q47565415 | An application of machine learning to haematological diagnosis |
Q35104219 | Application of data mining methods for classification and prediction of olive oil blends with other vegetable oils. |
Q47553927 | Applications of random forest feature selection for fine-scale genetic population assignment |
Q34028280 | Comparative analyses between retained introns and constitutively spliced introns in Arabidopsis thaliana using random forest and support vector machine |
Q37424069 | Compensation of feature selection biases accompanied with improved predictive performance for binary classification by using a novel ensemble feature selection approach |
Q38989842 | Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI. |
Q28550396 | Different Statistical Approaches to Investigate Porcine Muscle Metabolome Profiles to Highlight New Biomarkers for Pork Quality Assessment |
Q35974454 | Do little interactions get lost in dark random forests? |
Q47095886 | Driver fatigue detection through multiple entropy fusion analysis in an EEG-based system |
Q41018814 | EFS: an ensemble feature selection tool implemented as R-package and web-application. |
Q27902319 | GPURFSCREEN: a GPU based virtual screening tool using random forest classifier |
Q57804740 | Global state and potential scope of investments in watershed services for large cities |
Q37587950 | Guidelines for Developing and Reporting Machine Learning Predictive Models in Biomedical Research: A Multidisciplinary View |
Q94566562 | Hepatic molecular signatures highlight the sexual dimorphism of Non-Alcoholic SteatoHepatitis (NASH) |
Q51252521 | Identifying cryptic diversity with predictive phylogeography. |
Q97653028 | Improving prediction of rare species' distribution from community data |
Q58765380 | Integration of gene interaction information into a reweighted random survival forest approach for accurate survival prediction and survival biomarker discovery |
Q36361192 | Intervention in prediction measure: a new approach to assessing variable importance for random forests. |
Q38203857 | Letter to the Editor: On the term 'interaction' and related phrases in the literature on Random Forests |
Q46498114 | Local Chromatin Features Including PU.1 and IKAROS Binding and H3K4 Methylation Shape the Repertoire of Immunoglobulin Kappa Genes Chosen for V(D)J Recombination |
Q57338047 | Mining data with random forests: current options for real-world applications |
Q100526124 | Modeling Posidonia oceanica shoot density and rhizome primary production |
Q30252404 | Modernizing Relationship Therapy through Social Thermoregulation Theory: Evidence, Hypotheses, and Explorations |
Q57009474 | P2RANK: Knowledge-Based Ligand Binding Site Prediction Using Aggregated Local Features |
Q90011529 | Prediction of Congou Black Tea Fermentation Quality Indices from Color Features Using Non-Linear Regression Methods |
Q92948296 | Prediction of Sub-Monomer A2 Domain Dynamics of the von Willebrand Factor by Machine Learning Algorithm and Coarse-Grained Molecular Dynamics Simulation |
Q35839806 | RAD sequencing reveals within-generation polygenic selection in response to anthropogenic organic and metal contamination in North Atlantic Eels |
Q47428466 | Stripping flow cytometry: How many detectors do we need for bacterial identification? |
Q48011935 | Textural analysis of early-phase spatiotemporal changes in contrast enhancement of breast lesions imaged with an ultrafast DCE-MRI protocol |
Q91060235 | The accuracy of passive phone sensors in predicting daily mood |
Q31020230 | The differential effects of increasing frequency and magnitude of extreme events on coral populations |
Q47758365 | Using cell nuclei features to detect colon cancer tissue in hematoxylin and eosin stained slides |
Q38613697 | Using random forests for assistance in the curation of G-protein coupled receptor databases |
Q39630912 | [Formula: see text] splitting rules in survival forests |
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