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Pitkänen and his team are developing algorithms that identify short, repetitive snippets of DNA sequences. These algorithms can be used to find DNA sequences that mutate frequently in a particular type of cancer or to which certain proteins involved in gene regulation bind. Analysis of these sequences can be used for various purposes, such as charting the causes of cancer and developing medicines.

Algorithms have been used to identify cells in sectional images of tissue samples. For instance, if tissue cells appear atypical, the algorithm will spot this and determine if the cells are cancerous. DNA sequence data from tumours is now being used along with imaging data to identify cancers.

– We trained the algorithm to try to deduce the type of cancer from these sequence changes. Once the algorithm is given all the mutations of different tumours and their sequences, in the future it will be able to determine the kind of tumour that has been detected. This deduction process is based on the algorithm learning these connections, tells Pitkänen.

New computational methods

– Individualised treatments of the future, among them cancer treatments, will be based on a precise understanding of the patient and their illness. This will result from gathering a large volume of data of different types, such as tumour-related data and imaging data during cancer treatment. Many data collection methods produce a mass of data, and the new computational methods developed for analysing it requires a very large amount of computational resources, says Pitkänen.

For example, the three-year HPC/Exascale Centre of Excellence for Personalised Medicine in Europe (PerMedCoE) project is aimed at making effective use of cancer-related data in healthcare and speeding up the process of diagnosis. CSC – IT Center for Science, is one of the main partners in the project.

Read more form Elixir Finland’s website.