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Diversity of gene expression in adenocarcinoma of the lung

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TLDR
Gene expression analysis promises to extend and refine standard pathologic analysis and make possible the subclassification of adenocarcinoma into subgroups that correlated with the degree of tumor differentiation as well as patient survival.
Abstract
The global gene expression profiles for 67 human lung tumors representing 56 patients were examined by using 24,000-element cDNA microarrays. Subdivision of the tumors based on gene expression patterns faithfully recapitulated morphological classification of the tumors into squamous, large cell, small cell, and adenocarcinoma. The gene expression patterns made possible the subclassification of adenocarcinoma into subgroups that correlated with the degree of tumor differentiation as well as patient survival. Gene expression analysis thus promises to extend and refine standard pathologic analysis.

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“Bioinformatics” 특집을 내면서

TL;DR: Assessment of medical technology in the context of commercialization with Bioentrepreneur course, which addresses many issues unique to biomedical products.
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ONCOMINE: A Cancer Microarray Database and Integrated Data-Mining Platform

TL;DR: ONCOMINE is presented, a cancer microarray database and web-based data-mining platform aimed at facilitating discovery from genome-wide expression analyses and novel biomarkers and therapeutic targets are discovered.
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ConsensusClusterPlus: a class discovery tool with confidence assessments and item tracking

TL;DR: The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset and ConsensusClusterPlus implements the CC method in R and extends it with new functionality and visualizations including item tracking, item- Consensus and cluster-consensus plots.
References
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Journal ArticleDOI

Cluster analysis and display of genome-wide expression patterns

TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
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Molecular portraits of human breast tumours

TL;DR: Variation in gene expression patterns in a set of 65 surgical specimens of human breast tumours from 42 different individuals were characterized using complementary DNA microarrays representing 8,102 human genes, providing a distinctive molecular portrait of each tumour.
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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

TL;DR: A generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case and suggests a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications

TL;DR: Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.
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