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Linear Separability of Gene Expression Dataset

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We examine simple geometric properties of gene expression datasets, where samples are taken from two distinct classes (e.g. two types of cancer). Specifically, the problem of linear separability for pairs of genes is investigated. We developed and implemented novel, highly efficient algorithmic tools for finding all pairs of genes that induce a linear separation of the two sample classes. These tools are based on computational geometric properties, and were applied to ten publicly available cancer datasets. We discovered that seven out of the ten datasets examined are highly separable. Statistically, this phenomenon is highly significant, and is very unlikely to occur at random.

This talk is part of the Computational and Systems Biology series.

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