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

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TLDR
The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse.
Abstract
It is widely accepted that orthologous genes between species are conserved at the sequence level and perform similar functions in different organisms. However, the level of conservation of gene expression patterns of the orthologous genes in different species has been unclear. To address the issue, we compared gene expression of orthologous genes based on 2,557 human and 1,267 mouse samples with high quality gene expression data, selected from experiments stored in the public microarray repository ArrayExpress. In a principal component analysis (PCA) of combined data from human and mouse samples merged on orthologous probesets, samples largely form distinctive clusters based on their tissue sources when projected onto the top principal components. The most prominent groups are the nervous system, muscle/heart tissues, liver and cell lines. Despite the great differences in sample characteristics and experiment conditions, the overall patterns of these prominent clusters are strikingly similar for human and mouse. We further analyzed data for each tissue separately and found that the most variable genes in each tissue are highly enriched with human-mouse tissue-specific orthologs and the least variable genes in each tissue are enriched with human-mouse housekeeping orthologs. The results indicate that the global patterns of tissue-specific expression of orthologous genes are conserved in human and mouse. The expression of groups of orthologous genes co-varies in the two species, both for the most variable genes and the most ubiquitously expressed genes.

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Ontology-supported research on vaccine efficacy, safety and integrative biological networks

TL;DR: The author proposes minimal vaccine information standards and their ontology representations, ontology-based linked open vaccine data and meta-analysis, an integrative One Network (‘OneNet’) Theory of Life, and ontology based approaches to study and apply the OneNet theory in the Big Data era.
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Comparative modular analysis of gene expression in vertebrate organs

TL;DR: A modularization algorithm is used to identify inter-species co-modules of organs and genes that are functionally coherent both in terms of genes and of organs from both organisms and show that a large proportion of genes belonging to the same co-module are orthologous between mouse and human.
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Designing Dietary Recommendations Using System Level Interactomics Analysis and Network-Based Inference.

TL;DR: An integrated analysis of publicly available gene expression profiles for foods, diseases and drugs is performed, by calculating pairwise similarity scores for diet and disease gene expression signatures and characterizing their topological features in protein-protein interaction networks.
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Gene Expression and Microcomputed Tomography Analysis of Grafted Bone Using Deproteinized Bovine Bone and Freeze-Dried Human Bone.

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A cross-species analysis method to analyze animal models' similarity to human's disease state

TL;DR: A new cross-species gene expression module comparison method to use animal models' expression data to analyse the effectiveness of animal models in drug research is developed and could be applied for drug research.
References
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Journal ArticleDOI

Exploration, normalization, and summaries of high density oligonucleotide array probe level data

TL;DR: There is no obvious downside to using RMA and attaching a standard error (SE) to this quantity using a linear model which removes probe-specific affinities, and the exploratory data analyses of the probe level data motivate a new summary measure that is a robust multi-array average (RMA) of background-adjusted, normalized, and log-transformed PM values.
Journal ArticleDOI

A gene atlas of the mouse and human protein-encoding transcriptomes

TL;DR: In this paper, high-density oligonucleotide arrays offer the opportunity to examine patterns of gene expression on a genome scale, and the authors have designed custom arrays that interrogate the expression of the vast majority of proteinencoding human and mouse genes and have used them to profile a panel of 79 human and 61 mouse tissues.
Journal ArticleDOI

Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks

TL;DR: The ability of the trained ANN models to recognize SRBCTs is demonstrated, and the potential applications of these methods for tumor diagnosis and the identification of candidate targets for therapy are demonstrated.
Book

Bioinformatics and Computational Biology Solutions Using R and Bioconductor

TL;DR: In this article, the authors present a detailed case study of R algorithms with publicly available data, and a major section of the book is devoted to fully worked case studies, with a companion website where readers can reproduce every number, figure and table on their own computers.
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