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The Integrative Correlation Coefficient: a Measure of Cross-study

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The article was published on 2007-01-01 and is currently open access. It has received 6 citations till now. The article focuses on the topics: Measure (physics) & Correlation coefficient.

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Citations
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Journal ArticleDOI

Batch effect removal methods for microarray gene expression data integration: a survey

TL;DR: Methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments are reviewed in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process.
Journal ArticleDOI

Merging two gene-expression studies via cross-platform normalization

TL;DR: The proposed normalization method is applied to three existing breast cancer datasets, and is compared to several competing normalization methods using the proposed validation measures.
Journal ArticleDOI

Large scale comparison of global gene expression patterns in human and mouse

TL;DR: The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse.

Detecting, correcting, and preventing the batch effects in multi-site data, with a focus on gene expression Microarrays

TL;DR: This thesis proposes an efficient algorithm to extend the single-study variance-based gene selection method to a multi-study gene selection algorithm and empirical results show this feature selection algorithm outperforms other algorithms in reducing the destructive influence of batch effects.

Detection of Low Rank Signals in Noise and Fast Correlation Mining with Applications to Large Biological Data

TL;DR: A new method is proposed, called FastMap, that exploits the discreteness of SNPs, and uses a permutation approach to account for multiple comparisons in the analysis of biomedical data.
References
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Journal ArticleDOI

Batch effect removal methods for microarray gene expression data integration: a survey

TL;DR: Methods designed to combine genomic data recorded from microarray gene expression (MAGE) experiments are reviewed in a unified framework together with a wide range of evaluation tools, which are mandatory in assessing the efficiency and the quality of the data integration process.
Journal ArticleDOI

Merging two gene-expression studies via cross-platform normalization

TL;DR: The proposed normalization method is applied to three existing breast cancer datasets, and is compared to several competing normalization methods using the proposed validation measures.
Journal ArticleDOI

Large scale comparison of global gene expression patterns in human and mouse

TL;DR: The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse.
Journal ArticleDOI

MergeMaid: R tools for merging and cross-study validation of gene expression data.

TL;DR: An R package and associated object definitions are developed to merge and visualize multiple gene expression datasets that use arbitrary character IDs and generate objects that can efficiently support a variety of joint analyses.