One of the major challenges faced in treating cancer is the constantly changing nature of tumours hence proposed treatment at a particular time may not be effective because tumours become resistant to drugs or proposed treatment.

Artificial Intelligence in a new development has come in useful in predicting how cancers will progress and evolve, and this will go a long way in helping doctors propose the most effective treatment for each patient.

A team led by the Institute of Cancer Research London[ICR] and the University of Edinburgh developed a new technique known as Revolver [Repeated Evolution of Cancer]

This technique notes and selects the system in DNA mutation within cancers and uses the information to predict future genetic changes.

Cancer evolves unpredictably but if doctors can predict how the tumours evolve, they could step in and stop it before it advances or develops and increase the patient’s chances of survival.

The team also discovered a link between a certain pattern of recurrent tumour mutations and survival outcomes.
This indicates that repeating patterns of DNA mutations could be used as a means of prognosis, helping to build future treatment.

The research team developed a new machine learning technique which identifies patterns in genetic mutations that happen in tumours and applying the patterns to predict another.

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Two developers have created a new artificial intelligence system that aims to make managing push notifications easier.

Taiwan-based developers, TonTon Hsien-de Huang and Hung-Yu Kao, have figured out a way to input user data and habits into a neural network that’s able to learn what notifications users care about and what ones they would rather not show up on their device

They call this process of machine learning ‘Clicksequence-aware deep neural network (DNN)-based Pop-uPs recOmmendation’ or C-3PO. It works by combining what notifications users interact with and what ones they don’t. From there, the algorithm shows you notifications that you constantly find useful and hides those that you rarely click on.

The hope for this project is to better facilitate positive relationships between apps and users by showing them what they want and not bombarding them with intrusive, useless notifications.

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