Algorithms Help To Distinguish Diseases at the Molecular Level
Machine learning is playing an increasing role in biomedical research. Scientists at the Technical University of Munich (TUM) have now developed a new method for using molecular data to extract subtypes of disease. In the future, this method may help support the study of larger patient groups.
Currently, doctors define and diagnose most diseases based on symptoms. However, this does not necessarily mean that diseases in patients with similar symptoms have identical causes or show the same molecular changes. In biomedicine, the molecular mechanisms of a disease are often discussed. It refers to changes in the regulation of genes, proteins, or metabolic pathways at the onset of the disease. The goal of stratified medicine is to classify patients into several subtypes at the molecular level in order to provide more targeted treatments.
To extract subtypes of diseases from large groups of patient data, new machine learning algorithms can help. They are designed to independently recognize patterns and correlations in extensive clinical measures. The LipiTUM Junior Research Group, led by Dr. Josch Konstantin Pauling of the Chair of Experimental Bioinformatics, has developed an algorithm for this purpose.
Complex analysis using automated web tool
His method combines the results of existing algorithms to obtain more accurate and robust predictions of clinical subtypes. This unifies the features and benefits of each algorithm and eliminates its time-consuming adjustment. “This makes it much easier to apply the analysis to clinical research,” reports Dr. Pauling. “For this reason, we have developed a web-based tool that enables online analysis of molecular clinical data by professionals with no prior knowledge of bioinformatics.”
On the website (https://exbio.wzw.tum.de/mosbi/), researchers can submit their data for automated analysis and use the results to interpret their studies. “Another important aspect for us was the visualization of the results. Previous approaches were not able to generate intuitive visualizations of the relationships between patient groups, clinical factors, and molecular signatures. That will change with the web-based visualization produced by our MoSBi tool, “said Tim Rose, a scientist at TUM School of Life Sciences. MoSBi stands for” Molecular Signatures Using Biclustering. ” ‘algorithm.
Request clinically relevant questions
With the tool, researchers can now, for example, represent data from cancer studies and simulations for various scenarios. They have already demonstrated the potential of their method in a large-scale clinical study. In a co-operative study with researchers at the Max Planck Institute in Dresden, Dresden Technical University and Kiel University Clinic, they studied the change in lipid metabolism in the liver of patients with non-alcoholic fatty liver disease (NAFLD). .
This widespread disease is associated with obesity and diabetes. It develops from non-alcoholic fatty liver (NAFL), in which lipids are deposited in liver cells, to non-alcoholic steatohepatitis (NASH), in which the liver becomes even more inflamed, to cirrhosis. liver and tumor formation. Apart from dietary adjustments, no treatments have been found so far. Because the disease is characterized and diagnosed by the accumulation of various lipids in the liver, it is important to understand its molecular composition.
Biomarkers of liver disease
Using MoSBi methods, the researchers were able to demonstrate the liver heterogeneity of patients in the NAFL stage at the molecular level. “From a molecular point of view, the liver cells of many patients with NAFL were almost identical to those of patients with NASH, while others were still very similar to healthy patients. We could also confirm our predictions using clinical data. “says Dr. Pauling. “We were then able to identify two potential lipid biomarkers for disease progression.” This is important for early recognition of the disease and its progression and the development of targeted treatments.
The research group is already working on more applications of its method to gain a better understanding of other diseases. “In the future, algorithms will play an even more important role in biomedical research than they already do today. They can make it much easier to detect complex mechanisms and find more specific treatment approaches,” says Dr. Pauling.
Reference: Rose TD, Bechtler T, Ciora OA, et al. MoSBi: Automated signature mining for molecular stratification and subtitling. Proc Natl Acad Sci. 2022; 119 (16): e2118210119. doi: 10.1073 / pnas.2118210119
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