Feature Story | 3-Jun-2024

Understanding neural networks in machine learning

A brief look at how neural networks can help answer scientific questions

La Jolla Institute for Immunology

Scientists at La Jolla Institute for Immunology (LJI) are pioneering new methods in machine learning to better understand the inner workings of our cells. In a recent Genome Biology study, LJI scientists shared how their new neural network method can shed light on gene expression in different cell types.

But what are neural networks?

Scientists use neural networks to understand extremely complicated datasets. "Neural" networks got their name because they are inspired by how the human brain processes information.

Imagine you are driving on the highway, and you see a sign for your exit. The neurons in your eyes communicate the road sign information to other neurons in your brain, which signal other neurons, and so on. 

As the neurons process the information, they give each answer weight, or importance. Is the sign shaped like a road sign? Yes? Does the road sign include letters? Yes. What does it say? Let's see. Neurons route the information along the right path to quickly find an answer.

After what feels like a split second, your neurons tell you: YES. That's the sign for your exit.

You are aware of the input (the road sign) and your brain's conclusion. But your brain came up with its own way of processing the information.

Computational scientists build neural networks that come to conclusions in a similar way. Researchers can take complicated inputs, such as genomic data, and neural networks can process the information to spot hidden trends and make predictions.

In these artificial neural networks, information travels through artificial neurons, or nodes, that process the information and signal other artificial neurons. Every time the signal passes through a neural connection, it carries weight, which alters how the neural network routes the signal for further processing.

Scientists don't have to intervene to help the network come to conclusions. Instead, the network adapts and learns as it goes—which is why scientists call this process machine learning.

Learn more about LJI's work in computational biology

Learn more about the Genome Biology study

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