Artificial intelligence discovers genes associated with disease

An artificial neural network can reveal patterns in huge amounts of gene expression data, and discover groups of disease-related genes.

The researchers behind a new study have used artificial intelligence, AI, to investigate whether it is possible to discover biological networks using deep learning, in which entities known as “artificial neural networks” are trained by experimental data.

Since artificial neural networks are excellent at learning how to find patterns in enormous amounts of complex data, they are used in applications such as image recognition.

The information was “unsorted,” in the sense that the researchers did not give the artificial neural network information about which gene expression patterns were from people with diseases, and which were from healthy people.

Are the designs of the neural network and the familiar biological networks similar? The scientists then investigated whether their model of gene expression could be used to determine which gene expression patterns are associated with disease and which is normal.

Since the model has been trained using unclassified data, it is possible that the artificial neural network has found totally new patterns. Read More

Artificial neural networks consist of several layers in which information is mathematically processed. The system comprises an input layer and an output layer that delivers the result of the information processing. Between these two, are several hidden layers in which calculations are carried out. When the scientists trained the artificial neural network, they wondered whether it was possible to understand exactly how it works.

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