Cancer-Drugs Treatment Market to Reach New Height with AI Predicting Drug Combination
A research team from Finland has successfully developed a machine learning model that can help us fight cancer more effectively. During the treatment of patients suffering from cancers, healthcare professionals usually require to use a unique combination of several therapies. The patients are generally treated, with medication, radiation therapy, or both, in addition to cancer surgery.
As different drugs act differently on various cancer cells, medication can often be combined. Combinatorial drug therapies are known to enhance cancer treatment's effectiveness and carry the potential to reduce the negative side-effects by lessening the dosage of each drug. However, the full advantages of combination therapy fail to get discovered as the experimental screening of various drug combinations is often very expensive and slow. The newly discovered machine learning technique can help identify useful combinations to kill cancer cells with particular functional or genetic makeup.
Researchers hailing from the University of Turku, University of Helsinki, and the Aalto University in Finland have developed a new machine learning method that correctly predicts the procedure. Different combinations of cancer drugs kill several types of cancer cells. A set of data collected from previous studies involving the association between cancer cells and drugs was used to create a new AI model. With the introduction of the new method, the cancer-drugs treatment market is expected to grow enormously.
The model correctly predicts how a drug combination could inhibit specific cancer cells before the testing of that drug combination on those cancer cells. This achievement can help the cancer researchers to select the most accurate drug combination among the thousand other options for further research, as stated by a researcher from the Institute for Molecular Medicine Finland (FIMM).
The newly discovered machine learning approach can be used for non-cancerous diseases as well. In such cases, the model needs to be re-trained with data associated with that disease. For instance, the model could also be used to study the effect of different combinations of antibiotics on bacterial infections.
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