hero image

According to researchers, the algorithm employed to monitor Netflix usage can be utilized to assist medical professionals in creating customized cancer treatments.

A Netflix-like algorithm could help in treating cancer 

Researchers utilize artificial intelligence (AI) to examine and classify the quantity and extent of DNA modifications across a genome, or the complete genetic code of the cell, as cancer arises and evolves. Scientists used this data to identify 21 common defects that develop as the condition worsens.

These faults, also called copy number signatures, are thought to help doctors choose treatments specific to the tumor’s characteristics.

In the future, doctors want to be able to evaluate a patient’s newly sequenced tumor and match its important attributes to the blueprints for genetic faults, enabling them to offer more specialized cancer treatment, according to Echo News.

When users access Netflix, information is gathered on the kind of content they consume, the number of times they watch it, or if they rate it or not. According to Study Finds, Netflix utilizes an algorithm to filter through this enormous amount of data, identify trends, and suggest upcoming films and TV episodes.

A machine learning software with a Netflix-like algorithm was developed by a team of academics led by Drs. Nischalan Pillay of University College London (UCL) and Ludmil Alexandrov of the University of California, San Diego (UC San Diego).

AI can analyze chromosome arrangement and organization 

The chromosome organization and arrangement can be analyzed by artificial intelligence (AI), which can sift through hundreds of rows of genomic information to find recurring patterns.

The system can then classify the patterns that emerge and help scientists identify the various types of cancer defects that can occur, claim academics funded by Cancer Research UK and Cancer Grand Challenges. However, studies have demonstrated that the changes to chromosomes that take place when it starts and how it develops are remarkably similar.

The researchers believe that by knowing this information, clinicians will be capable of treating for cancer patients more effectively and individually in the future.