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Cell Specific Gene Expression

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Development of Compendium for Esophageal Squamous Cell Carcinoma

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GECC: Gene Expression Based Ensemble Classification of Colon Samples.

Saima Rathore, Mutawarra Hussain, Asifullah Khan

IEEE/ACM Transactions on Computational Biology and Bioinformatics

|September 11, 2015

Summary

This summary is machine-generated.

This study introduces a new gene expression-based colon cancer classification method (GECC). It uses feature extraction and a Support Vector Machine (SVM) ensemble to accurately distinguish normal from malignant colon samples.

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Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

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Published on: September 25, 2011

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Gene expression patterns are altered in cancer, offering potential diagnostic markers.
Existing methods for colon cancer classification using gene expression data have limitations.

Purpose of the Study:

To propose a novel gene expression-based colon classification scheme (GECC).
To improve the accuracy of classifying colon gene samples into normal and malignant categories.

Main Methods:

Employed feature extraction techniques (chi-square, F-Score, PCA, mRMR) for large gene datasets.
Developed a majority voting ensemble of Support Vector Machines (SVMs) with diverse kernels (linear, polynomial, RBF, sigmoid).

Main Results:

Achieved improved performance compared to previous gene-based colon cancer detection techniques.
Demonstrated the effectiveness of the SVM-ensemble approach on colon and other binary-class gene expression datasets.

Conclusions:

The proposed GECC scheme, utilizing feature extraction and an SVM ensemble, enhances colon cancer classification accuracy.
This approach offers a more discriminative decision space for gene-based cancer detection.