Begin typing your search above and press return to search.
proflie-avatar
Login
exit_to_app
Gaza
access_time 30 Nov 2023 12:20 PM GMT
Geert Wilders
access_time 28 Nov 2023 4:50 AM GMT
Cusat tragedy: Let experience be a lesson
access_time 27 Nov 2023 4:00 AM GMT
A Constitution always in the making
access_time 27 Nov 2023 11:43 AM GMT
How long will the ceasefire last?
access_time 25 Nov 2023 5:56 AM GMT
DEEP READ
Schools breeding hatred
access_time 14 Sep 2023 10:37 AM GMT
Ukraine
access_time 16 Aug 2023 5:46 AM GMT
A Constitution always in the making
access_time 27 Nov 2023 11:43 AM GMT
Debunking myth of Israel’s existence
access_time 23 Oct 2023 7:01 AM GMT
exit_to_app
Homechevron_rightLifestylechevron_rightHealthchevron_rightAlgorithm identifies...

Algorithm identifies 165 genes that cause cancer

text_fields
bookmark_border
Algorithm identifies 165 genes that cause cancer
cancel

Scientists from the Max Planck Institute for Molecular Genetics (MPIMG) in Berlin and the Institute of Computational Biology of Helmholtz Zentrum München developed an algorithm that identified 165 previously unknown genes responsible for cancer even if their DNA sequence has not changed. The research points out that disorganization of these genes can lead to cancer as all-new genes interact with known cancer genes.

Cancer is usually caused when abnormal cells go rampant and multiply to destroy organs and bodily functions. This unfettered growth is usually caused by the accumulation of DNA changes in cancer genes or oncogenes caused by mutations in these genes that direct cell development. However, only very few mutational genes are in cancers, which means that other factors cause the illness.

Named EMOGI (Explainable Multi-Omics Graph Integration), the algorithm explains the relationships in the cell mechanism that causes the gene to transform into oncogenes. The program integrates tens of thousands of data sets created from patient samples. It contains information about DNA methylation, the activity of individual genes and protein interactions within cellular pathways, and the sequence of data with mutations. With the already inserted data, the algorithm detects molecular patterns and principles that lead to the development of cancer.

"Ideally, we obtain a complete picture of all cancer genes at some point, which can have a different impact on cancer progression for different patients. This is the foundation for personalized cancer therapy," said Annalisa Marsico, lead researcher, in a press release.

Scientists stressed that EMOGI is not limited to cancer in theory and can be used to integrate various biological data and find similar complex pathogens such as metabolic diseases such as diabetes.

Show Full Article
TAGS:CancerGenes
Next Story