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Machine Learning Predicts Antineoplastic Effectiveness-ScienceDaily

Machine Learning Predicts Antineoplastic Effectiveness-ScienceDaily

 


With the advent of pharmacogenomics, machine learning studies are well underway to predict individual patient drug responses from algorithms derived from previously collected drug response data. Entering high-quality learning data that reflects a person’s drug response as much as possible is the starting point for improving the accuracy of predictions. Previously, preclinical studies of animal models were used, which were relatively easily available compared to human clinical data.

In light of this, a research team led by Professor Sanguk Kim of POSTECH’s Faculty of Life Sciences succeeded in improving the accuracy of antineoplastic response prediction by using data that most closely resembles the response of real people. , Is attracting attention. The team developed this machine learning technique through an algorithm that learns transcriptome information from artificial organoids derived from real patients rather than animal models.The results of these studies were published in an international journal. Nature Communications October 30th.

Customized treatment is considered to be of paramount importance in treatment development because even patients with the same cancer respond differently to antineoplastic agents. However, current predictions are based on the genetic information of cancer cells, limiting their accuracy. Machine learning had the problem of learning based on false signals because of unnecessary biomarker information.

To improve prediction accuracy, the research team introduced a machine learning algorithm that uses a protein interaction network that can interact with the target protein, as well as the transcriptome of individual proteins that are directly associated with the target protein. It induces learning of transcriptome production of proteins that are functionally close to the target protein. This improves accuracy by allowing you to learn only selected biomarkers, rather than the false biomarkers that traditional machine learning had to learn.

In addition, we used data from patient-derived organoids rather than animal models to narrow the discrepancy in actual patient responses. With this method, colorectal cancer patients treated with 5-fluorouracil and bladder cancer patients treated with cisplatin were predicted to be comparable to actual clinical outcomes.

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material Provided by Pohang University of Science and Technology (POSTECH).. Note: The content can be edited in style and length.

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