Effective expression analysis using gene interaction matrices and convolutional neural networks

Pillai, A.; Grabowski, P.; John, B.

doi:10.1101/2021.09.07.459284

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bioRxiv · 10.1101/2021.09.07.459284

Effective expression analysis using gene interaction matrices and convolutional neural networks

Pillai, A.·Grabowski, P.·John, B.

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Abstract

Artificial intelligence recently experienced a renaissance with the advancement of convolutional neural networks (CNNs). CNNs require spatially meaningful matrices (e.g., image data) with recurring patterns, limiting its applicability to high-throughput omics data. We present GIM, a simple, CNN-ready framework for omics data to detect both individual and network-level entities of biological importance. Using gene expression data, we show that GIM-CNNs can outperform comparable neural networks in performance and their design facilitates network-level interpretability. GIM-CNNs provide a means to discover novel disease-relevant factors beyond individual genes and their expression, factors that are likely missed by standard differential gene expression approaches.

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Pillai, A., Grabowski, P., John, B.. 2021-09-07. Effective expression analysis using gene interaction matrices and convolutional neural networks. https://doi.org/10.1101/2021.09.07.459284

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