Genetics is another sector that produces vast quantities of data. In the future, we could use the power of algorithms to help manage them: It is estimated that humans have about 25,500 genes, which themselves or in combination can cause diseases as genes determine the molecular processes in the body that determine whether we are healthy or sick. Due to the vast number of genes and their effects, using machine learning for pattern recognition is a great idea.
The MoSBi project at the Technical University of Munich studies the molecular mechanisms of human diseases to identify subtypes of diseases that appear similar in terms of their symptoms. However, a better distinction can be critical to make the right treatment decision and provide a more targeted therapy.
"Cancer is perhaps the most notable example of this," explain Dr. Josch Konstantin Pauling and Tim Rose in a MEDICA-tradefair.com interview. "A multitude of mutations can lead to changes in tissue, which can develop into a tumor. Doctors examine the specific changes in the tumor and adjust the patient’s treatment accordingly."
The team behind MoSBi developed the algorithm and makes it available as a free web-based application to allow researchers to analyze data from their own studies.
Another example of the benefits of big data and algorithms in laboratory medicine is single-cell sequencing. A team from the Max Delbrück Center for Molecular Medicine in the Helmholtz Association has developed ikarus, a machine learning program for just this purpose: "We developed a software that works in single-cell resolution and finds gene expression patterns to specific cell types. In case of cancer, the method found a pattern in tumor cells that is common to different types of cancer, consisting of a characteristic combination of genes," explains Dr. Altuna Alkalin in a MEDICA-tradefair.com interview.
These insights not only help facilitate the diagnosis: pathologists visually examine tissue samples to identify cancerous cells. This means they must identify the various cell types under the microscope. If the ikarus algorithm makes more information about a tumor and its immediate environment available in the future, it could also affect the medication used for targeted treatment.