Vilja Pietiäinen, senior scientist and adjunct professor (docent) in cell and molecular biology at the Finnish Institute for Molecular Medicine (FIMM), wants to make cancer treatments more individualised.
With the help of microscopic imaging, researchers can inspect how the drugs influence the cancer cells. Machine learning models allow researchers to more effectively analyse the images of cancer cells. The artificial intelligence used by the research team has been trained with the Finnish ELIXIR node’s, CSC’s high-performance computing clusters.
“We call this phenotypic imaging. Microscopic imaging allow us to identify hundreds of different cell characteristics from images of drug-treated cancer cells. This information is important for further training the machine learning model. If we are able to clearly identify certain phenotypes, we can also teach the machine to do the same by showing it how certain cells have responded to a certain drug. After this, we can provide the machine with a new dataset, in which case it will be able to classify the cells by how they show up in the images. On the other hand, artificial intelligence, especially deep-learning solutions, can also help us to discover traits or phenotypes that we as humans are not able to either detect or classify.”
The iCAN-project utilises the SD Connect service provided by the Finnish ELIXIR node CSC for transferring sequencing data to the Academics environment.
The data is encrypted using Crypt4GH, a protected standard encryption method developed by the Global Alliance for Genomics & Health for sharing human genetic information.
“This ensures that the information can be used in all of the services included in CSC’s SD service suite, and may even be potentially shared with other service providers who possess similar information.”
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