scholarly article | Q13442814 |
P50 | author | Jianhua Wei | Q96142894 |
Liying Yang | Q88144990 | ||
Zhimin Liu | Q96142893 | ||
P2093 | author name string | Junying Zhang | |
Xiguo Yuan | |||
P2860 | cites work | Multi-class BCGA-ELM based classifier that identifies biomarkers associated with hallmarks of cancer | Q35634621 |
iPcc: a novel feature extraction method for accurate disease class discovery and prediction | Q37080324 | ||
A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain | Q38475717 | ||
Random subspace ensembles for FMRI classification. | Q45917647 | ||
Combining multiple approaches for gene microarray classification | Q48004443 | ||
Methods of forward feature selection based on the aggregation of classifiers generated by single attribute | Q51571429 | ||
Support-vector networks | Q55922708 | ||
The random subspace method for constructing decision forests | Q56502470 | ||
Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
An ensemble of filters and classifiers for microarray data classification | Q58036728 | ||
Gene expression correlates of clinical prostate cancer behavior | Q60156349 | ||
Identifying Important Risk Factors for Survival in Kidney Graft Failure Patients Using Random Survival Forests | Q22673963 | ||
A combinational feature selection and ensemble neural network method for classification of gene expression data | Q24799711 | ||
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling | Q27860529 | ||
Gene expression profiling predicts clinical outcome of breast cancer | Q27860732 | ||
Molecular classification of cancer: class discovery and class prediction by gene expression monitoring | Q27861072 | ||
Prediction of central nervous system embryonal tumour outcome based on gene expression | Q29618619 | ||
Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays | Q29618684 | ||
Translation of microarray data into clinically relevant cancer diagnostic tests using gene expression ratios in lung cancer and mesothelioma | Q30718501 | ||
Use of proteomic patterns in serum to identify ovarian cancer | Q30819686 | ||
Ensemble machine learning on gene expression data for cancer classification. | Q30929145 | ||
Minimum redundancy feature selection from microarray gene expression data | Q30988046 | ||
A genetic filter for cancer classification on gene expression data | Q30997263 | ||
Classification of gene expression data: A hubness-aware semi-supervised approach | Q31061445 | ||
Using uncorrelated discriminant analysis for tissue classification with gene expression data | Q31068182 | ||
Cancer classification from gene expression data by NPPC ensemble | Q33579902 | ||
A Sparse Learning Machine for High-Dimensional Data with Application to Microarray Gene Analysis | Q33732538 | ||
Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns | Q33835800 | ||
A study of performance on microarray data sets for a classifier based on information theoretic learning | Q33942721 | ||
Weighted random subspace method for high dimensional data classification | Q34019831 | ||
Gene network modular-based classification of microarray samples | Q34324671 | ||
Improving accuracy for cancer classification with a new algorithm for genes selection | Q34475554 | ||
DNA microarray and cancer | Q35029862 | ||
FIREWACh: high-throughput functional detection of transcriptional regulatory modules in mammalian cells | Q35128053 | ||
Deep convolutional neural networks for annotating gene expression patterns in the mouse brain | Q35623697 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P407 | language of work or name | English | Q1860 |
P304 | page(s) | 4596326 | |
P577 | publication date | 2016-11-24 | |
P1433 | published in | BioMed Research International | Q17509958 |
P1476 | title | Random Subspace Aggregation for Cancer Prediction with Gene Expression Profiles | |
P478 | volume | 2016 |
Q62495329 | RNA-seq assistant: machine learning based methods to identify more transcriptional regulated genes | cites work | P2860 |
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