The 21st century has been named The Century of Biology. This is where we now use life sciences to solve the world's biggest problems. Things like climate change, biodiversity loss, even human health. For a biologist like me this is super exciting. However, it uses data, lots and lots of data. So we biologists need people who are smart in maths and computers to help us out.

​Our project uses bioinformatics to analyse omics data sets using machine learning. That’s a lot of complex technical words all put together, so I'm going to walk you through the concepts.

Bioinformatics is a type of data science where we work with biological datasets called omics. Omics is where you take all the genes, proteins or other molecules within a sample and you measure them all at once, then you look at the networks or pathways that are going on between them. By taking all of the omics types together we can start to get a really good understanding of what’s going on inside a living creature. However, these data sets are really large and complicated and we can’t analyse them by hand, we need to use computers.

The main method that we use is called machine learning. Machine learning is where an algorithm or calculation can take in a whole lot of data, look at that data and then predict an outcome. So, for example, you can take a whole lot of different patient samples and look at them with machine learning and better predict which ones have got cancer.

​But the trick is, we don’t come up with the calculations, we let the computer do it, all on its own. So the computer tries a bunch of different things, it learns which ones works and which ones don’t, and it throws away the ones that don’t, and then it adapts and it evolves and it gets better, until it narrows down on the best. And then we come up with a much powerful and efficient calculation than a human could ever think up.

​So by using machine learning to analyse our omics data sets, we can get a better understanding of what’s going on inside a living system at a molecular level. And! When you can understand a system, you can design a better system. For example, if you understand everything that’s going on with a fish, you can come up with a better farming system for that fish, reduce your environmental impact and give the fish a better life.