Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work very well on genomic data analysis, since they are notoriously known as small data. In our work, we focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients that can guide treatment decisions for a specific individual through training on small data. In fact, doctors and clinicians always address this problem by studying several interrelated clinical variables simultaneously. We attempt to simulate such clinical perspective, and introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks and transfer it to help address new tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network [35], that is a simple yet effective meta learning machine for few-shot image classification. Observing that gene expression data have specifically high dimensionality and high noise properties compared with image data, we proposed a new extension of it by appending two modules to address these issues. Concretely, we append a feature selection layer to automatically filter out the disease-irrelated genes and incorporate a sample reweighting strategy to adaptively remove noisy data, and meanwhile the extended model is capable of learning from a limited number of training examples and generalize well. Simulations and real gene expression data experiments substantiate the superiority of the proposed method for predicting the subtypes of disease and identifying potential disease-related genes.

This sorting and shifting process has allowed researchers from Linköping University to discover new groups of disease-related genes. The basis of the technology should help to advance precision (personalized) medicine leading to more reliable forms of individualized treatments for different conditions. The biotechnology is orientated towards constructing maps of biological networks. These networks relate to how different proteins or genes interact with each other. This is a complex task and the application of artificial intelligence has helped to make the process easier.

Researchers have developed an artificial neural network using deep learning to identify genes that are related to disease. An artificial neural network has revealed patterns in huge amounts of gene expression data and discovered groups of disease-related genes. The developers, from Linköping University, Sweden, hope that the method can eventually be applied within precision medicine and individualised treatment. The scientists created maps of biological systems based on how different proteins or genes interact with each other. Using artificial intelligence (AI), they investigated whether it is possible to discover biological networks with deep learning, in which entities known as artificial neural networks are trained by experimental data.

An artificial neural network can reveal patterns in huge amounts of gene expression data and discover groups of disease-related genes. This has been shown by a new study led by researchers at Linköping University, published in Nature Communications. The scientists hope that the method can eventually be applied within precision medicine and individualized treatment. It's common when using social media that the platform suggests people whom you may want to add as friends. The suggestion is based on you and the other person having common contacts, which indicates that you may know each other.