A Promising Novel Approach for Breast Cancer Gene Expression Data Analysis

A Promising Novel Approach for Breast Cancer Gene Expression Data Analysis
shutterstock_72141592Dartmouth’s Norris Cotton Cancer Center researchers revealed in the Pacific Symposium on Biocomputing the possibility of relying on denoising autoencoders (DAs) to extract key biological principles from big data sets of gene expression in breast cancer cells. DAs are a variant of artificial neural networks, which aim to learn compact and efficient representations from the input data by adding perturbations (called “noise”). DAs basically shuffle data around in order to understand it, while attempting to reconstruct the original data. Shuffling the data creates noise, and DAs have to recognize the features within the noise in order to characterize the input. As part of the training, the network generates a model, resamples the shuffled inputs and re-reconstructs the data, until it finds those inputs which bring its model closest to what is known to be true. Incorporating noise during the training yields robust features. The research team, led by Dr. Casey S. Greene, has applied DAs to a large collection of breast cancer gene expression data. “Cancers are very complex,” clarified Dr. Greene in a news release. “Our goal is to measure whi
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