- As researchers suggest, the science underlying Netflix watching patterns might someday be utilized to help doctors treat cancer effectively.
- Scientists have developed a machine learning technique that employs artificial intelligence systems to analyze the DNA alterations induced by cancer.
- When tumors begin and expand, the program classifies DNA changes across a cell’s complete genetic code.
Netflix uses a variety of Machine Learning applications, the most well-known of which is their content recommendation engine, which advises you on what you might like. They employ their recommendation engine, which is based on a machine-learning algorithm that considers your previous movie choices, the genres you enjoy, and what movies other users with similar preferences have viewed. This movie suggestion system is critical for Netflix since they have hundreds of different alternatives to choose from, and consumers are more likely to be confused about what to watch next than actually viewing anything. In this case, the movie suggestion system can give a clear direction and assistance in deciding what to watch.
Now, this Netflix’s monitoring algorithm, according to researchers, might be used to aid doctors in developing a tailored cancer therapy.
Dr. Nonchalant Pillay of University College London (UCL) and Dr. Ludmil Alexandrov of the University of California, San Diego (UC San Diego) lead a team of researchers that developed a machine learning tool using Netflix-like algorithms. Research co-author Ludmil Alexandrov of UC San Diego explained that just as Netflix can make predictions about your next watch, they’ll also be able to predict how your cancer is going to behave based on the modifications it’s DNA has already experienced.
Artificial intelligence (AI) can sift through hundreds of lines of genetic data to uncover common patterns in the organization and arrangement of chromosomes.
The system, according to specialists funded by Cancer Research UK and Cancer Grand Challenges, can then categories the patterns that emerge, assisting scientists in understanding the types of cancer defects that can occur.
Despite cancer being a complex disease, researchers have discovered that the chromosomal alterations that occur when it starts and how it grows are quite similar. Doctors will be able to deliver better and more customized cancer treatment in the future, according to the study.
“We want to get to the point where doctors can look at a patient’s fully sequenced tumour and match the key features of the tumour against our blueprint for genomic faults. Armed with that information, we believe that doctors will be able to offer better and more personalized cancer treatment in the future”, Said Alexandrov.
The scientists used the algorithm to seek for patterns in the genomes of 9,873 individuals with 33 distinct forms of cancer. They were able to identify 21 frequent flaws in the structure, order, and quantity of copies of DNA present when cancer begins and progresses. These common errors, known as copy number signatures, might help doctors find medicines that match the tumour’s features.
The program employs complicated mathematics to scan sequencing data from cancer patients and detect similar patterns in how the chromosomes are reconstructed in different forms of cancer using software called SigProfilerExtractor, which was developed by Dr. Alexandrov.
Dr. Nischalan Pillay, the co-lead author of the study and associate professor of sarcoma and genomics at UCL, said: “To stay one step ahead of cancer, we need to anticipate how it adapts and changes.
Mutations are the key drivers of cancer, but a lot of our understanding is focused on changes to individual genes in cancer. We’ve been missing the bigger picture of how vast swathes of genes can be copied, moved around, or deleted without catastrophic consequences for the tumor.
“Understanding how these events arise will help us regain an advantage over cancer. Thanks to advances in genome sequencing, we can now see these changes play out across different cancer types and figure out how to respond effectively to them.”