Machine learning technologies are everywhere. They’re used by search engines, social media, and even in online banking. But one area that this technology is still emerging is medicine.
Machine learning technologies could be very promising in medicine, and could be used for many applications, such as detecting signs of disease in cells, or discovering new drugs for rare diseases. But in order for a machine learning approach to be able to do such things, it needs to be both accurate and able to understand how cells work.
Our team has developed an accurate machine learning approach that can predict cell growth in a way that researchers can easily understand. The machine learning technique makes its predictions by looking at how cells change and act under different conditions. This method could someday be used to diagnose cancer, or predict how certain drugs may interact with a patient.
Interpreting machine learning predictions
In essence, machine learning is a form of artificial intelligence (AI) in which data is used to teach computers to make decisions on their own, without a person needing to be there to do it for them.
But one of the main weaknesses of machine learning techniques in biology and medicine is the fact that they don’t incorporate biological knowledge—such as underlying cell biochemistry—in the learning process. In general, they also ignore this knowledge when making their predictions. This is because these systems treat biological information as data or numbers, so they don’t consider the actual biological meaning of these numbers.
Such systems are often referred to as “black box” systems. These are AI that are fed data, and provide users with a clear decision or prediction