SynthesisBriefings in bioinformatics2016
Comparative study of joint analysis of microarray gene expression data in survival prediction and risk assessment of breast cancer patients.
Synthesis in Briefings in bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Meta-analysis of mRNA expression profiles to identify differentially expressed genes in lung adenocarcinoma tissue from smokers and non-smokers.Oncology reports · 2018Pooled it
- Pooled it
- Transcriptome of Cultured Lung Fibroblasts in Idiopathic Pulmonary Fibrosis: Meta-Analysis of Publically Available Microarray Datasets Reveals Repression of Inflammation and Immunity Pathways.International journal of molecular sciences · 2016Pooled it
- Development of a host-signature-based machine learning model to diagnose bacterial and viral infections in febrile children.Frontiers in pediatrics · 2025Article
- Evaluating Ovarian Cancer Chemotherapy Response Using Gene Expression Data and Machine Learning.BioMedInformatics · 2024Article
- Resistin-like beta reduction is associated to low survival rate and is downregulated by adjuvant therapy in colorectal cancer patients.Scientific reports · 2023Article
- Article
- Article
- An Autophagy-Related Gene Signature can Better Predict Prognosis and Resistance in Diffuse Large B-Cell Lymphoma.Frontiers in genetics · 2022Article
- Meta-Analysis of Microdissected Breast Tumors Reveals Genes Regulated in the Stroma but Hidden in Bulk Analysis.Cancers · 2021Article
- The Transcription FactorCells · 2019Article
- An Efficient Feature Selection Strategy Based on Multiple Support Vector Machine Technology with Gene Expression Data.BioMed research international · 2018Article
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1 author.
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Abstract
Microarray gene expression data sets are jointly analyzed to increase statistical power. They could either be merged together or analyzed by meta-analysis. For a given ensemble of data sets, it cannot be foreseen which of these paradigms, merging or meta-analysis, works better. In this article, three joint analysis methods, Z-score normalization, ComBat and the inverse normal method (meta-analysis) were selected for survival prognosis and risk assessment of breast cancer patients. The methods were applied to eight microarray gene expression data sets, totaling 1324 patients with two clinical endpoints, overall survival and relapse-free survival. The performance derived from the joint analysis methods was evaluated using Cox regression for survival analysis and independent validation used as bias estimation. Overall, Z-score normalization had a better performance than ComBat and meta-analysis. Higher Area Under the Receiver Operating Characteristic curve and hazard ratio were also obtained when independent validation was used as bias estimation. With a lower time and memory complexity, Z-score normalization is a simple method for joint analysis of microarray gene expression data sets. The derived findings suggest further assessment of this method in future survival prediction and cancer classification applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.