| scholarly article | Q13442814 |
| P2093 | author name string | Kenta Nakai | |
| Myungjin Moon | |||
| P2860 | cites work | Gene selection and classification of microarray data using random forest. | Q25255911 |
| RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome | Q29547458 | ||
| Regularization Paths for Generalized Linear Models via Coordinate Descent | Q29614491 | ||
| A novel feature extraction approach for microarray data based on multi-algorithm fusion | Q30911459 | ||
| MTML-msBayes: Approximate Bayesian comparative phylogeographic inference from multiple taxa and multiple loci with rate heterogeneity | Q30993862 | ||
| The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures | Q34113527 | ||
| Classification models for clear cell renal carcinoma stage progression, based on tumor RNAseq expression trained supervised machine learning algorithms | Q35398065 | ||
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| A review of feature selection techniques in bioinformatics | Q36919689 | ||
| Stable feature selection for biomarker discovery | Q37779401 | ||
| Is cross-validation valid for small-sample microarray classification? | Q47207899 | ||
| Robust biomarker identification for cancer diagnosis with ensemble feature selection methods | Q48244876 | ||
| Bolasso: model consistent Lasso estimation through the bootstrap | Q55922134 | ||
| Gene Selection for Cancer Classification using Support Vector Machines | Q56535529 | ||
| Least angle regression | Q56907124 | ||
| TNM staging of renal cell carcinoma: Workgroup No. 3. Union International Contre le Cancer (UICC) and the American Joint Committee on Cancer (AJCC) | Q73722193 | ||
| P4510 | describes a project that uses | scikit-learn | Q1026367 |
| P433 | issue | Suppl 13 | |
| P407 | language of work or name | English | Q1860 |
| P921 | main subject | support vector machine | Q282453 |
| feature selection | Q446488 | ||
| biomarker | Q864574 | ||
| RNA sequencing | Q2542347 | ||
| P304 | page(s) | 1026 | |
| P577 | publication date | 2016-12-22 | |
| P1433 | published in | BMC Genomics | Q15765854 |
| P1476 | title | Stable feature selection based on the ensemble L 1 -norm support vector machine for biomarker discovery | |
| P478 | volume | 17 |
| Q42328348 | 2016 update on APBioNet's annual international conference on bioinformatics (InCoB) |
| Q92464986 | An Ensemble Feature Selection Method for Biomarker Discovery |
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| Q64921708 | Deep Learning Methods for Predicting Disease Status Using Genomic Data. |
| Q59812769 | Feature Ranking in Predictive Models for Hospital-Acquired Acute Kidney Injury |
| Q64073606 | Feature Selection for Longitudinal Data by Using Sign Averages to Summarize Gene Expression Values over Time |
| Q103826066 | Machine learning predicts live-birth occurrence before in-vitro fertilization treatment |
| Q99607473 | Multi-scale supervised clustering-based feature selection for tumor classification and identification of biomarkers and targets on genomic data |
| Q90024781 | Perspective: Guiding Principles for the Implementation of Personalized Nutrition Approaches That Benefit Health and Function |
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