Deep learning has transformed how we analyze complex data by using artificial neural networks with multiple layers. These networks, inspired by the human brain’s structure, can learn intricate patterns directly from raw data. Traditional deep learning architectures like Convolutional Neural Networks (CNNs) excel at processing structured data such as images, where spatial relationships follow a regular grid pattern. However, many real-world systems, particularly in biology, are better represented as networks or graphs, where relationships between elements don’t follow such regular patterns.
Consider protein interaction networks, where proteins are connected through physical or functional interactions, or metabolic networks that represent chemical reactions in cells. These networks capture complex relationships that can’t be effectively represented in a regular grid structure. This limitation led to the development of Graph Neural Networks (GNNs), a class of deep learning models specifically designed to work with network-structured data.
Biological systems are inherently interconnected. From molecular interactions to cellular pathways, biological processes often involve complex networks of relationships. These networks can represent various biological phenomena:
The network structure itself often contains valuable information. For instance, proteins that interact often have related functions, and genes that are close in regulatory networks tend to be involved in similar biological processes. This makes networks not just a way to visualize data, but a source of meaningful biological insights.
GNNs adapt the principles of deep learning to work with network-structured data. Unlike traditional neural networks that process individual data points independently, GNNs can learn from both the features of individual nodes (like protein properties) and the relationships between them (like protein interactions).
The key innovation of GNNs is their ability to process irregular network structures while maintaining important properties like permutation invariance - the model should give the same results regardless of how we order the nodes in our network. This is crucial for biological networks where there’s no natural ordering of elements.
One of the most powerful aspects of GNNs is their ability to create embeddings - learned representations of nodes that capture both their features and their network context. Think of an embedding as a sophisticated summary of a node’s role in the network, encoded as a vector of numbers.
For a protein in a PPI network, its embedding would combine:
These embeddings are not just extracted features but are learned representations that the GNN optimizes for specific tasks. For instance, if we’re predicting protein functions, the GNN will learn embeddings that place proteins with similar functions close together in the embedding space.
The learning process in GNNs differs fundamentally from traditional neural networks. In a standard neural network, information flows from input to output through a series of layers, with each neuron processing its inputs independently. GNNs, however, allow information to flow along the edges of the network, enabling nodes to learn from their neighbors.
This learning process is iterative:
At the heart of GNNs is the concept of message passing, where nodes communicate information along network edges. Unlike traditional neural networks where each layer applies the same transformation to all inputs, message passing in GNNs follows the structure of the input network.
During message passing, nodes send and receive “messages” containing information about their features and their network context. This enables each node to learn from both its direct neighbors and, through multiple layers, from more distant parts of the network. The learned parameters determine how these messages are constructed and combined, allowing the GNN to discover which patterns in the network are most relevant for the task at hand.
Consider a protein interaction network: during message passing, each protein receives information from its interaction partners. Through multiple rounds of message passing, proteins gather information not just about their direct interaction partners, but about their broader network neighborhood. This helps the GNN understand both local interaction patterns and global network structure.
Understanding GNNs this way reveals why they’re so powerful for biological data analysis: they can learn from the rich, complex relationships present in biological networks while respecting the fundamental network structure of the underlying system.
Let’s examine how GNNs handle real biological data through some concrete examples from current research. Consider protein function prediction, a fundamental challenge in molecular biology. Traditional approaches might look at a protein’s sequence or structure in isolation. However, as shown in the Muzio et al. paper, GNNs can simultaneously analyze:
The Muzio paper discusses several successful applications of GNNs to protein interaction networks. For instance, Liu et al. enhanced protein interaction prediction by combining sequence information with network structure. Their GNN learned representations that captured both the proteins’ molecular properties and their positions in the interaction network, outperforming methods that used sequence data alone.
More sophisticated approaches, like Zitnik and Leskovec’s work with OhmNet, demonstrate how GNNs can handle multi-scale biological networks. Their model learned from protein interactions across different tissues, showing how the same protein might behave differently in different cellular contexts. This kind of analysis would be difficult or impossible with traditional deep learning approaches.
GNNs have shown particular promise in pharmaceutical research. The Muzio paper describes several applications:
Researchers have used GNNs to predict how drugs will interact with protein targets. The models can learn from both the molecular structure of drugs and the broader context of protein interaction networks. This helps identify not just whether a drug will bind to a target, but also potential off-target effects.
One particularly innovative application is Decagon, which predicts side effects from drug combinations. The model works with a multi-modal graph that includes:
Through message passing, the model learns patterns that indicate which drug combinations might cause specific side effects, even when those combinations haven’t been tested experimentally.
GNNs have also been applied to disease analysis. For example, researchers have used GNNs to:
A key insight from these applications is that network structure itself contains valuable information. Even with limited feature data, GNNs can make meaningful predictions based on network topology. This is particularly valuable in biology, where detailed molecular data isn’t always available for every protein or gene.
When applying GNNs to biological problems, several technical considerations emerge:
While GNNs can work with minimal feature data, performance typically improves with relevant biological features. Researchers must carefully consider which features to include and how to represent them numerically.
The quality of predictions depends heavily on the quality of the input network. Researchers must decide:
Different biological problems may require different GNN architectures. For instance:
The field continues to evolve rapidly. Current trends include:
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).