An artificial neural network can
reveal patterns in huge amounts of gene expression data, and discover groups of
disease-related genes. The scientists hope that the method can eventually be
applied within precision medicine and individualised treatment. The researchers
used 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. However, this machine learning
method has until now seldom been used in biological research. The scientists
used a large database with information about the expression patterns of 20,000
genes in a large number of people.
The information was unsorted, and
the AI model was then trained to find patterns of gene expression. When they
analysed their neural network, it turned out that the first hidden layer
represented to a large extent interactions between various proteins. Deeper in
the model, they found groups of different cell types. 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.
They confirmed that the model finds relevant patterns that agree well with
biological mechanisms in the body. Since the model has been trained using
unclassified data, it is possible that the artificial neural network has found
totally new patterns. The researchers plan now to investigate whether such,
previously unknown patterns, are relevant from a biological perspective.
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