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"Automated Identification Of Cell-type-specific Genes In The Mouse Brain By Image Computing Of Expression Patterns." and the language of the book is English.


“Automated Identification Of Cell-type-specific Genes In The Mouse Brain By Image Computing Of Expression Patterns.” Metadata:

  • Title: ➤  Automated Identification Of Cell-type-specific Genes In The Mouse Brain By Image Computing Of Expression Patterns.
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  • Language: English

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  • Internet Archive ID: pubmed-PMC4078975

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This article is from <a href="//archive.org/search.php?query=journaltitle%3A%28BMC%20Bioinformatics%29" rel="nofollow">BMC Bioinformatics</a>, <a href="//archive.org/search.php?query=journaltitle%3A%28BMC%20Bioinformatics%29%20AND%20volume%3A%2815%29" rel="nofollow">volume 15</a>.<h2>Abstract</h2>Background: Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date. Results: Here, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types. Conclusions: Overall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain.

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