Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Missing-value estimation using linear and non-linear regression with Bayesian gene selection.

Bioinformatics (Oxford, England) - 22 Nov 2003

Zhou Xiaobo, Wang Xiaodong, Dougherty Edward R

Abstract excerpt

MOTIVATION: Data from microarray experiments are usually in the form of large matrices of expression levels of genes under different experimental conditions. Owing to various reasons, there are frequently missing values. Estimating these missing values is important because they affect downstream analysis, such as clustering, classification and network design. Several methods of missing-value estimation are in...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Artifacts
Share to Bayes Theorem
Share to BRCA2 Protein

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Linear Models

Share to Models, Statistical

Share to Nonlinear Dynamics

Share to Regression Analysis

Share to Ubiquitin-Protein Ligases

Source

Identifiers

PMID: 14630659
DOI: 10.1093/bioinformatics/btg323

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence. Graph snapshot: 2026-09-29.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Missing-value estimation using linear and non-linear regression with Bayesian gene selection.PMID 14630659Read ↗

Multi-Omics Regulatory Network Inference in the Presence of Missing DataSemantic proximity Read ↗

Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization.Semantic proximity Read ↗

Comparative Assessment and Outlook on Methods for Imputing Proteomics DataSemantic proximity Read ↗

A sparse negative binomial classifier with covariate adjustment for RNA-seq dataSemantic proximity Read ↗

Gene selection: a Bayesian variable selection approach.Semantic proximity Read ↗

Biologically Informative NA Deconvolution (BIND) excavates hidden features of the proteome from missing values in large-scale datasetsSemantic proximity Read ↗

Comparison of sparse biclustering algorithms for gene expression datasetsSemantic proximity Read ↗

Predicting human miRNA disease association with minimize matrix nuclear norm.Semantic proximity Read ↗

Gene regulatory network reconstruction using Bayesian networks, the Dantzig Selector, the Lasso and their meta-analysis.Semantic proximity Read ↗

Comparison of classification accuracy and feature selection between sparse and non-sparse modeling of metabolomics dataSemantic proximity Read ↗

Robust pathway sampling in phenotype prediction. Application to triple negative breast cancer.Semantic proximity Read ↗

Multivariate analysis of tumour gene expression profiles applying regularisation and Bayesian variable selection techniquesSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.