Researchers from the School of Medicine in Ribeirão Preto (FMRP), at the University of São Paulo (USP), in collaboration with international groups, have developed indices that provide information about the prognosis of cancers, aid in the choice of the most appropriate therapy to be used and identify potential targets for the development of new drugs. The article reporting these results – Machine Learning Identity Stemness Features Associated with Oncogenic Dedifferentiation – will be published on April 5 in Cell.
To perform the study, researchers at the Omics laboratory from the Department of Genetics of the FMRP combined the use of artificial intelligence algorithms, genomic data from 12,000 samples from 33 different types of tumors, and an understanding of how progression of cancer occurs.
According to Houtan Noushmehr, senior author of the study, the methodologies used in this work are part of a new trend in biomedical sciences research, consequence of the large amount of molecular data currently available. “The present challenge is to manage, interpret and analyze different categories of data,” says Noushmehr, “which requires researchers to integrate knowledge in biology, computer science and statistics.” He considers the training of young scientists to manage coherently these massive data amounts
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