Logistic regression for disease classification using microarray data: model selection in a large p and small n case.
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BayesHammer: Bayesian clustering for error correction in single-cell sequencingStatistical analysis and modeling of mass spectrometry-based metabolomics data.Extracting causal relations on HIV drug resistance from literatureWolbachia enhance Drosophila stem cell proliferation and target the germline stem cell nicheMolecular pathway identification using biological network-regularized logistic modelsVolcano plots in analyzing differential expressions with mRNA microarrays.Feature selection and classifier performance on diverse bio- logical datasetsMulti-TGDR: a regularization method for multi-class classification in microarray experiments.Utilizing ECG-Based Heartbeat Classification for Hypertrophic Cardiomyopathy Identification.Lung cancer gene expression database analysis incorporating prior knowledge with support vector machine-based classification method.Modified logistic regression models using gene coexpression and clinical features to predict prostate cancer progression.Applications of Bayesian gene selection and classification with mixtures of generalized singular g-priors.Peculiar Genes Selection: A new features selection method to improve classification performances in imbalanced data setsAn EEG-based machine learning method to screen alcohol use disorder.Improving tRNAscan-SE Annotation Results via Ensemble Classifiers.Folded concave penalized learning in identifying multimodal MRI marker for Parkinson's disease.Penalized model-based clustering with unconstrained covariance matrices.Penalized model-based clustering with cluster-specific diagonal covariance matrices and grouped variablesThe value of prior knowledge in machine learning of complex network systems.Integrated genetic and epigenetic prediction of coronary heart disease in the Framingham Heart Study.Identifying joint biomarker panel from multiple level dataset by an optimization model.microRNA profiles in urine by next-generation sequencing can stratify bladder cancer subtypes.Predicting improved protein conformations with a temporal deep recurrent neural networkIntegrated Use of Statistical-Based Approaches and Computational Intelligence Techniques for Tumors Classification Using Microarray
P2860
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P2860
Logistic regression for disease classification using microarray data: model selection in a large p and small n case.
description
2007 nî lūn-bûn
@nan
2007 թուականի Մայիսին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի մայիսին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Logistic regression for diseas ...... in a large p and small n case.
@ast
Logistic regression for diseas ...... in a large p and small n case.
@en
type
label
Logistic regression for diseas ...... in a large p and small n case.
@ast
Logistic regression for diseas ...... in a large p and small n case.
@en
prefLabel
Logistic regression for diseas ...... in a large p and small n case.
@ast
Logistic regression for diseas ...... in a large p and small n case.
@en
P356
P1433
P1476
Logistic regression for diseas ...... in a large p and small n case.
@en
P2093
Khew-Voon Chin
P304
P356
10.1093/BIOINFORMATICS/BTM287
P407
P577
2007-05-31T00:00:00Z