Popular Reports / computational-biology

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This report is written by MaltSci based on the latest literature and research findings

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What are the applications of graph neural networks in biology?

Abstract

The rapid advancement of high-throughput technologies has led to an overwhelming accumulation of molecular data in biology, necessitating sophisticated computational tools for data analysis. Among these, Graph Neural Networks (GNNs) have emerged as a transformative approach, adept at modeling complex biological networks and elucidating the relationships among biological entities such as genes, proteins, and metabolic pathways. GNNs excel in capturing both local and global structural information, making them particularly suitable for applications in genomics, drug discovery, protein-protein interaction prediction, and systems biology. This report reviews the diverse applications of GNNs in biology, highlighting their role in enhancing predictive modeling and providing insights into biological processes. In genomics, GNNs have been employed to analyze single-cell RNA sequencing data, predict unobserved node features, and identify disease subnetworks. In drug discovery, they facilitate the prediction of drug-target and drug-drug interactions, streamlining the identification of therapeutic candidates. GNNs also contribute significantly to protein-protein interaction networks and systems biology, offering a comprehensive understanding of cellular processes and disease mechanisms. Furthermore, the methodologies employed in GNN research, including various architectures and data preprocessing techniques, are discussed, along with the challenges and limitations faced in biological applications. The report concludes with a perspective on future directions for GNN integration in biological research, emphasizing the potential for synergistic approaches that combine GNNs with other machine learning techniques and the expansion of applications into emerging biological fields.

Outline

This report will discuss the following questions.

• 1 Introduction
• 2 Applications of Graph Neural Networks in Biology
  • 2.1 Genomics and Genetic Interaction Networks
  • 2.2 Drug Discovery and Molecular Interaction Predictions
  • 2.3 Protein-Protein Interaction Networks
  • 2.4 Systems Biology and Pathway Analysis
  • 2.5 Disease Prediction and Patient Stratification
• 3 Methodologies and Techniques
  • 3.1 Overview of Graph Neural Network Architectures
  • 3.2 Data Preprocessing and Graph Construction
  • 3.3 Training and Evaluation Metrics
• 4 Challenges and Limitations
  • 4.1 Data Quality and Availability
  • 4.2 Scalability Issues
  • 4.3 Interpretability of GNN Models
• 5 Future Directions
  • 5.1 Integrating GNNs with Other Machine Learning Techniques
  • 5.2 Expanding Applications to Emerging Biological Fields
  • 5.3 Enhancing Model Interpretability and Usability
• 6 Conclusion

1 Introduction

The advent of high-throughput technologies has revolutionized the field of biology, leading to an unprecedented accumulation of molecular data. This surge in data has necessitated the development of advanced computational tools capable of analyzing complex biological networks. Among these tools, Graph Neural Networks (GNNs) have emerged as a transformative approach, enabling researchers to model intricate biological systems and elucidate the relationships among biological entities such as genes, proteins, and metabolic pathways. The relational nature of biological data makes GNNs particularly suitable for applications in this domain, as they can effectively capture both local and global structural information, thereby enhancing predictive modeling and insights into biological processes[1].

The significance of GNNs in biology cannot be overstated. As biological research increasingly shifts from examining isolated components to understanding holistic systems, GNNs provide a framework that mirrors the interconnectedness of biological networks. This capability is crucial for various applications, including genomics, drug discovery, protein-protein interaction prediction, and systems biology. For instance, GNNs have been successfully employed in predicting gene interactions and drug-target interactions, facilitating advancements in personalized medicine and therapeutic strategies[2][3]. Moreover, GNNs enable the integration of diverse data types, such as genomic sequences and protein structures, thereby enriching the analysis and interpretation of biological phenomena[4].

Current research in GNNs within the biological context is rapidly expanding, with numerous studies exploring innovative architectures and methodologies. The applications of GNNs span various biological domains, each addressing specific challenges inherent in biological data. In genomics, GNNs have been utilized to model genetic interaction networks, providing insights into the regulatory mechanisms governing gene expression. In drug discovery, GNNs facilitate the prediction of molecular interactions, significantly accelerating the identification of potential therapeutic candidates[5]. Furthermore, in systems biology, GNNs contribute to the analysis of complex pathways, offering a comprehensive understanding of cellular processes and disease mechanisms[6].

This report is structured to provide a comprehensive overview of the applications of GNNs in biology. The subsequent sections will delve into specific applications, including genomics and genetic interaction networks, drug discovery and molecular interaction predictions, protein-protein interaction networks, systems biology and pathway analysis, and disease prediction and patient stratification. Following this, we will discuss the methodologies and techniques employed in GNN research, including an overview of various GNN architectures, data preprocessing, and evaluation metrics. We will also address the challenges and limitations faced by GNNs in biological applications, such as data quality, scalability, and model interpretability. Finally, we will explore future directions for GNN integration in biological research, emphasizing the potential for synergistic approaches that combine GNNs with other machine learning techniques, as well as the expansion of applications into emerging biological fields.

In conclusion, GNNs represent a significant advancement in the analysis of biological data, offering new avenues for research and discovery. By harnessing the power of GNNs, researchers can gain deeper insights into the complexities of biological systems, ultimately paving the way for innovative solutions in healthcare and biotechnology.

2 Applications of Graph Neural Networks in Biology

2.1 Genomics and Genetic Interaction Networks

Graph Neural Networks (GNNs) have emerged as a powerful computational tool in the field of biology, particularly in genomics and genetic interaction networks. Their ability to model complex relationships and interactions within biological data has led to significant advancements in various applications.

One notable application of GNNs is in the analysis of single-cell RNA sequencing (scRNA-seq) data. In this context, GNNs can effectively capture the intricate relationships among cells by constructing cell-cell association networks based on ligand-receptor interactions. For instance, research by Duan et al. (2025) demonstrates that GNNs enhance the biological relevance of cellular communication pathways by integrating varying edge homophily ratios in these networks, thereby improving cell type prediction from scRNA-seq data[2].

Furthermore, GNNs have been applied to predict unobserved node features in biological networks, as shown in the work by Hasibi and Michoel (2021). Their study utilized a Graph Feature Auto-Encoder framework to integrate gene expression data with molecular interaction networks, demonstrating that GNNs can effectively reconstruct biological networks and predict missing data, particularly in single-cell RNA-seq contexts[7].

Another significant application lies in disease subnetwork detection. GNNs are utilized to identify disease-related subnetworks by analyzing the topology of protein-protein interaction networks enriched with multi-omics features, such as gene expression and DNA methylation data. Pfeifer et al. (2022) introduced an explainable GNN framework, GNN-SubNet, which enhances the detection of these subnetworks, thus aiding in understanding the underlying mechanisms of diseases[8].

Moreover, GNNs are employed in link prediction tasks within biomedical networks, where they predict potential interactions between entities like proteins and drugs. Long et al. (2022) developed a Pre-Training Graph Neural Networks-based framework (PT-GNN) that integrates multiple data sources for improved link prediction, showcasing GNNs' capability to refine node features and enhance predictive performance[4].

Additionally, GNNs are instrumental in modeling gene expression connectivity by incorporating both regulatory and spatial structural elements of gene networks. Yan et al. (2024) introduced a novel spatial GNN approach that outperforms traditional models in predicting gene expression, thereby highlighting the effectiveness of GNNs in capturing complex gene interactions[9].

In summary, the applications of GNNs in biology, particularly in genomics and genetic interaction networks, are vast and varied. They include cell type prediction from scRNA-seq data, unobserved feature prediction in biological networks, disease subnetwork detection, link prediction in biomedical networks, and modeling gene expression connectivity. These applications underscore the transformative potential of GNNs in advancing our understanding of complex biological systems and their underlying mechanisms.

2.2 Drug Discovery and Molecular Interaction Predictions

Graph neural networks (GNNs) have emerged as powerful tools in the field of biology, particularly in drug discovery and molecular interaction predictions. Their ability to model complex relationships in data structured as graphs makes them particularly suitable for biological applications, where interactions between molecules, proteins, and genes can be represented in a network format.

One significant application of GNNs is in predicting drug-target interactions (DTIs). The representation of drugs and their target proteins as graphs allows GNNs to effectively learn from the non-Euclidean data inherent in molecular structures. Research indicates that GNNs have been rapidly adopted for DTI prediction due to their effectiveness in identifying repositioning drugs and accelerating drug discovery processes. They can process features derived from molecular graphs, allowing for more accurate predictions than traditional methods [10].

Furthermore, GNNs have been utilized in predicting drug-drug interactions (DDIs). This is crucial as DDIs can significantly impact drug efficacy and safety. Traditional methods of predicting DDIs often rely on time-consuming clinical trials; however, GNNs can leverage existing molecular interaction networks to predict interactions efficiently. A dual graph neural network model has been developed specifically for this purpose, focusing on both molecular structures and interaction data to enhance the accuracy of DDI predictions [11].

In addition to DTI and DDI predictions, GNNs are being applied in various other domains within bioinformatics. For instance, they are used in protein function prediction, where the interactions among proteins are analyzed to infer their biological roles. GNNs have also shown promise in in silico drug discovery, aiding in the identification of potential drug candidates by modeling the relationships between chemical compounds [1].

Moreover, GNNs are increasingly being applied to gene regulatory networks, allowing for predictions regarding gene interactions and automatic disease diagnosis from data. This capability is particularly relevant in understanding complex diseases and developing personalized medicine approaches [1].

Overall, the applications of GNNs in biology extend beyond mere prediction tasks; they are integral to advancing our understanding of molecular interactions, enhancing drug discovery processes, and potentially leading to innovative therapeutic strategies. As research continues to evolve, the integration of GNNs into biological studies is expected to yield significant insights and improvements in various biomedical fields [12][13].

2.3 Protein-Protein Interaction Networks

Graph neural networks (GNNs) have emerged as a powerful computational tool in the field of biology, particularly in the analysis and prediction of protein-protein interactions (PPIs). The applications of GNNs in this domain are extensive and have been explored through various studies, showcasing their effectiveness in understanding complex biological networks.

One significant application of GNNs is in the prediction of protein-protein interactions, which are essential for understanding cellular mechanisms, signaling networks, and disease processes. GNNs have demonstrated superior capabilities in identifying complex patterns within large and intricate datasets compared to traditional computational techniques. For instance, a study by Alme et al. (2025) highlighted the use of GNNs to analyze PPIs, emphasizing their ability to handle the multifaceted nature of protein interactions effectively [14]. This capability is particularly crucial given the challenges posed by the dynamic nature of PPIs.

Moreover, GNNs have been employed to improve the accuracy of PPI predictions through various models and frameworks. For example, Yang et al. (2024) introduced a Knowledge Graph Fused Graph Neural Network (KGF-GNN) that integrates multiple biological entities and relationships, significantly enhancing the prediction accuracy of PPIs [15]. This model utilizes an end-to-end learning approach, which allows for comprehensive feature extraction and fusion, resulting in improved predictive performance.

Another notable application is the development of GNN models that incorporate structural information and sequence features of proteins. Jha et al. (2022) proposed a graph-based approach that utilizes protein structural data from PDB files, effectively capturing the interactions between amino acids and enhancing the prediction of PPIs [16]. Similarly, Zhang et al. (2024) introduced the DSSGNN-PPI model, which combines distance graphs and sequence information to achieve a more nuanced understanding of protein interactions [17].

GNNs have also been utilized in multi-category prediction of PPIs. Zeng et al. (2024) developed the GNNGL-PPI model, which leverages global and local graph features to enhance the prediction of different types of protein interactions, demonstrating the flexibility and robustness of GNN approaches in tackling complex biological questions [18].

Furthermore, GNNs have been integrated with advanced techniques such as feature fusion and explainability methods to further improve the interpretability and accuracy of PPI predictions. For instance, the implementation of graph attention mechanisms in various GNN models has allowed for the integration of diverse features, leading to enhanced predictive performance across multiple biological datasets [19].

In summary, the applications of graph neural networks in the study of protein-protein interaction networks are diverse and impactful. GNNs not only provide advanced computational methods for predicting PPIs but also enhance our understanding of the underlying biological processes through their ability to model complex interactions and integrate various types of biological data. As research continues to evolve, GNNs are likely to play an increasingly vital role in bioinformatics and systems biology, contributing to significant advancements in our understanding of cellular functions and disease mechanisms.

2.4 Systems Biology and Pathway Analysis

Graph Neural Networks (GNNs) have emerged as a transformative tool in the field of biology, particularly within systems biology and pathway analysis. These networks leverage the inherent graph structures found in biological data, enabling more effective analysis of complex relationships and interactions among biological entities.

GNNs excel in capturing intricate dependencies that traditional neural networks may overlook, making them particularly suitable for applications involving structured biological data. One significant application area is single-cell RNA sequencing (scRNA-seq) data analysis. By extracting ligand-receptor associations and constructing cell-cell association networks, GNNs can enhance the biological relevance of cellular communication pathways. This is especially important given that standard GNN models often operate under the assumption of homophily (similar nodes being more likely to connect), which may not hold true in biological networks characterized by heterophily. Advanced GNN methods designed to handle such heterophilic data can provide more accurate insights into complex biological interactions, thus facilitating better understanding of cellular environments and functions (Duan et al. 2025) [2].

Moreover, GNNs have been utilized in the context of drug discovery and development, where they can model protein-protein interaction networks and predict potential drug functions. The ability to integrate multi-omics data, including gene expression profiles and ligand-receptor interactions, allows GNNs to decipher complex patterns within biological data, leading to more accurate analyses in computational biology (Pfeifer et al. 2022) [8].

Additionally, GNNs are increasingly applied to biological network analysis, where they facilitate tasks such as protein function prediction and the analysis of gene regulatory networks. By embedding biological graphs into low-dimensional spaces while preserving their topology and node properties, GNNs bridge the gap between biological networks and modern machine learning techniques, enhancing the predictive capabilities in bioinformatics (Yi et al. 2022) [20].

In systems biology, GNNs contribute to understanding the dynamics of biological systems by modeling the relationships among various bioentities. This modeling can reveal new insights into biological functions and processes, which are essential for advancing research in areas such as disease diagnosis and treatment (Mazein et al. 2024) [21].

In summary, the applications of GNNs in biology span across single-cell analysis, drug discovery, biological network analysis, and systems biology, showcasing their potential to unravel complex biological interactions and enhance our understanding of biological systems.

2.5 Disease Prediction and Patient Stratification

Graph Neural Networks (GNNs) have emerged as a powerful tool in the field of biology, particularly in the domains of disease prediction and patient stratification. The unique ability of GNNs to model complex relationships and interactions within biological data makes them particularly suitable for these applications.

In disease prediction, GNNs are utilized to represent patients and diseases as nodes within a graph, where edges represent the relationships or similarities between them. For instance, Zhenchao Sun et al. (2021) introduced a GNN-based model that enhances disease prediction by integrating external knowledge bases to supplement electronic medical records (EMRs) that may be sparse, particularly for rare diseases. This model allows for the effective generation of node embeddings that encapsulate the relevant information about patients, diseases, and symptoms, thus facilitating accurate predictions even for new patients lacking extensive historical data [22].

Additionally, Ruomei Wang et al. (2024) have explored hybrid multimodal approaches in disease prediction using GNNs. Their method constructs adaptive graphs from both latent representations and raw data, thereby improving the accuracy of predictions by incorporating a wider range of patient data, including demographic attributes and laboratory results. This approach demonstrates the versatility of GNNs in leveraging various data sources to enhance disease prediction models [23].

Furthermore, GNNs are instrumental in stratifying patients based on their disease profiles. The ability to analyze the latent embeddings of patients as nodes allows for the identification of subgroups within a population that may respond differently to treatments or have distinct disease progression patterns. For example, Liang Peng et al. (2023) discussed the use of federated graph learning frameworks to address challenges in population-based disease prediction, thereby enabling institutions to collaboratively enhance model accuracy without compromising patient data privacy [24].

The application of GNNs extends beyond disease prediction to include gene regulatory networks and automatic disease prediction from complex datasets. Giulia Muzio et al. (2021) highlighted the increasing use of GNNs in bioinformatics for tasks such as protein function prediction and drug discovery, underscoring their utility in understanding biological interactions and networks [1].

In summary, the applications of GNNs in biology are multifaceted, particularly in disease prediction and patient stratification. Their ability to effectively model complex interactions and integrate diverse data sources makes them a vital tool in advancing personalized medicine and improving patient outcomes.

3 Methodologies and Techniques

3.1 Overview of Graph Neural Network Architectures

Graph Neural Networks (GNNs) have emerged as powerful computational tools for analyzing complex biological data, particularly due to their ability to model intricate relationships and interactions among biological entities. These networks are especially relevant in fields such as bioinformatics, systems biology, and drug discovery. The following outlines key applications of GNNs in biology, highlighting their methodologies and architectures.

1. Protein Function Prediction and Protein-Protein Interaction Prediction: GNNs are extensively used for predicting the functions of proteins and their interactions. By representing proteins as nodes and interactions as edges in a graph, GNNs can leverage the structural information of biological networks to make predictions. This application is crucial for understanding cellular processes and developing therapeutic strategies [1].

2. In Silico Drug Discovery and Development: In drug discovery, GNNs facilitate the prediction of drug-target interactions, drug repurposing, and drug-drug interactions. They enable the integration of various data types, such as molecular structures and biological activity data, to enhance predictive accuracy. GNNs can also model complex biological networks that represent drug effects and mechanisms [25].

3. Single-Cell RNA Sequencing (scRNA-seq) Data Analysis: GNNs are utilized to analyze scRNA-seq data, which captures the gene expression profiles of individual cells. By constructing cell-cell association networks based on ligand-receptor interactions, GNNs improve the accuracy of cellular communication pathway representations. This approach allows for better understanding of heterogeneity in cell populations [2].

4. Gene Regulatory Networks: GNNs are applied to model gene regulatory networks, which depict the regulatory relationships between genes. This application is essential for understanding gene interactions and their implications in various biological processes and diseases [26].

5. Cancer Research: GNNs are increasingly being applied in cancer research to analyze multimodal data, including molecular structures, imaging data, and biological networks. They help in identifying biomarkers, understanding tumor heterogeneity, and predicting treatment responses [3].

6. Graph Representation Learning: This technique involves embedding biological graphs into lower-dimensional spaces while preserving their topological properties. GNNs are instrumental in this process, allowing for effective data analysis and interpretation in various biological contexts, from genomics to drug development [20].

7. Link Prediction in Biomedical Networks: GNNs are used for predicting potential interactions between entities in biomedical networks, such as proteins and drugs. This capability is crucial for understanding disease mechanisms and identifying new therapeutic targets [4].

8. Integration of Multi-Omics Data: GNNs can integrate various omics data types, including genomics, proteomics, and metabolomics, to provide a comprehensive view of biological systems. This integration is vital for elucidating complex biological interactions and pathways [27].

The methodologies underlying these applications often involve specific GNN architectures, such as Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE, which are designed to capture the dependencies and patterns inherent in graph-structured data. These architectures enable the effective processing of non-Euclidean data, making them particularly suitable for the analysis of biological networks characterized by complex interactions [5].

In conclusion, GNNs represent a transformative approach in biological research, providing novel methodologies for analyzing and interpreting complex biological data. Their applications span a wide range of areas, from basic biological research to advanced drug discovery, reflecting their growing significance in the life sciences.

3.2 Data Preprocessing and Graph Construction

Graph Neural Networks (GNNs) have emerged as a pivotal tool in the analysis of biological data, leveraging the inherent graph structure of biological networks to model complex interactions among various entities. The applications of GNNs in biology are vast and encompass several methodologies and techniques, particularly in the realms of data preprocessing and graph construction.

One primary application of GNNs is in the modeling of biological networks, where entities such as proteins, genes, and metabolites are represented as nodes, and their interactions or relationships are modeled as edges. This representation is particularly useful in understanding protein-protein interactions, gene regulatory networks, and cellular communication pathways. For instance, in the context of single-cell RNA sequencing (scRNA-seq) data, GNNs can enhance the prediction of cell types by utilizing ligand-receptor (L-R) dynamics to construct cell-cell association networks. This approach allows for the extraction of biologically relevant interactions that traditional methods might overlook (Duan et al. 2025) [2].

In addition to scRNA-seq, GNNs are frequently applied in domains such as drug discovery and development. They facilitate link prediction tasks in biomedical networks, where predicting potential interactions between drugs and targets is crucial for understanding disease mechanisms and identifying therapeutic candidates. The integration of various data sources—such as sequence, structure, and network data—through GNNs has proven effective in refining node features for link prediction, significantly improving the performance of models in tasks like synthetic lethality and drug-target interaction prediction (Long et al. 2022) [4].

Furthermore, GNNs have been utilized in graph representation learning, which focuses on embedding biological graphs into lower-dimensional spaces while preserving their topological and node properties. This technique aids in various applications, from molecular-level analysis to broader genomic and pharmaceutical contexts. Graph representation learning techniques allow for more efficient and insightful analyses of complex biological systems, thereby enhancing our understanding of underlying biological processes (Yi et al. 2022) [20].

The construction of graphs for biological data often involves preprocessing steps that ensure the quality and relevance of the input data. This includes the identification of nodes and edges based on biological relevance, as well as the incorporation of prior knowledge to guide the graph construction process. For example, graph embedding techniques have been increasingly employed to analyze mass spectrometry and sequencing-based data, facilitating the characterization of protein-protein interaction networks and enabling the prediction of novel drug functions (Alvarez-Mamani et al. 2024) [28].

In summary, the applications of GNNs in biology span various methodologies and techniques, particularly in data preprocessing and graph construction. By effectively modeling the intricate relationships within biological systems, GNNs enhance our capacity to analyze and interpret complex biological data, paving the way for advancements in fields such as genomics, drug discovery, and systems biology. The continuous evolution of GNN methodologies promises to further deepen our understanding of biological interactions and facilitate innovative solutions to longstanding challenges in biomedical research.

3.3 Training and Evaluation Metrics

Graph Neural Networks (GNNs) have emerged as a powerful tool in biological data analysis, leveraging their ability to model complex interactions within biological systems. The applications of GNNs in biology span various domains, each contributing to advancements in understanding biological processes and improving diagnostic and therapeutic strategies.

One significant application of GNNs is in the prediction of protein functions and protein-protein interactions. The intricate relationships among proteins can be effectively represented as graphs, where nodes correspond to proteins and edges represent interactions. GNNs utilize these representations to learn embeddings that capture the functional characteristics of proteins, thereby facilitating accurate predictions of their roles in biological pathways (Muzio et al. 2021) [1].

Additionally, GNNs are increasingly employed in drug discovery and development. They can model molecular structures as graphs, where atoms are nodes and chemical bonds are edges. This representation allows for the prediction of drug-target interactions, drug repurposing opportunities, and the identification of potential side effects. The ability of GNNs to integrate multimodal data enhances their predictive performance in these contexts (Yao et al. 2024) [25].

In the realm of medical diagnosis, GNNs are utilized to analyze healthcare data that is often irregular and unordered. Traditional machine learning approaches struggle with such data, but GNNs can effectively exploit the implicit relationships within biological systems, providing insights into disease prediction and progression (Ahmedt-Aristizabal et al. 2021) [29].

Moreover, GNNs play a crucial role in analyzing gene regulatory networks, where they can help predict gene interactions and understand regulatory mechanisms. This application is vital for elucidating the complexities of gene expression and its implications in various diseases (Gogoshin & Rodin 2023) [3].

Graph representation learning, a subset of GNN methodologies, aims to embed graph data into low-dimensional spaces while preserving topological information. This technique is particularly beneficial for bridging the gap between complex biological graphs and modern machine learning methods, enabling more efficient data analysis and interpretation (Yi et al. 2022) [20].

The training and evaluation of GNNs typically involve various metrics tailored to the specific biological tasks at hand. For instance, metrics such as accuracy, precision, recall, and F1-score are commonly employed to assess the performance of GNNs in classification tasks, such as predicting protein functions or disease states. Additionally, evaluation metrics like area under the receiver operating characteristic curve (AUC-ROC) may be used to gauge the ability of GNNs to distinguish between positive and negative interactions in drug discovery applications (Li et al. 2023) [26].

In summary, GNNs are revolutionizing the analysis of biological data through their diverse applications in protein function prediction, drug discovery, medical diagnosis, and gene regulatory network analysis. The methodologies employed, along with tailored training and evaluation metrics, underscore the potential of GNNs to provide meaningful insights into complex biological systems and advance research in the life sciences.

4 Challenges and Limitations

4.1 Data Quality and Availability

Graph neural networks (GNNs) have emerged as a powerful tool in various biological applications, particularly due to their ability to model complex relationships within biological data. The applications of GNNs in biology are extensive and can be categorized into several domains.

Firstly, GNNs are frequently utilized in protein function prediction and protein-protein interaction prediction. These tasks are crucial for understanding the functional roles of proteins and their interactions within cellular processes. The inherent structure of biological networks allows GNNs to effectively capture the relational information necessary for accurate predictions [1].

In the realm of drug discovery and development, GNNs facilitate in silico approaches that predict potential drug candidates by analyzing molecular interactions and properties. This application is particularly significant given the high cost and time associated with traditional drug discovery methods [26].

Moreover, GNNs are applied in gene regulatory networks and disease diagnosis, where they assist in predicting gene interactions and automatically diagnosing diseases from data. These applications highlight the potential of GNNs to address classic biological questions through innovative computational techniques [1].

Despite the promising applications of GNNs in biology, there are notable challenges and limitations that researchers face. One primary challenge is the quality and availability of data. Biological data can often be noisy, incomplete, or biased, which can significantly impact the performance of GNNs. The effectiveness of these networks heavily relies on the quality of the input data; thus, any shortcomings in data quality can lead to inaccurate predictions and hinder the overall reliability of the results [30].

Furthermore, the complexity of biological systems presents additional hurdles. The intricate interactions among biological entities can be difficult to model accurately, and GNNs may struggle to capture the full scope of these interactions without sufficient data. The high dimensionality and sparsity of biological data can also pose challenges in training effective GNN models [20].

In summary, while GNNs hold great potential for advancing biological research through their applications in various domains such as protein function prediction, drug discovery, and disease diagnosis, the challenges related to data quality and availability must be addressed to fully leverage their capabilities. As research in this area progresses, efforts to improve data integration, quality control, and model interpretability will be crucial for overcoming these limitations and enhancing the applicability of GNNs in biological contexts.

4.2 Scalability Issues

Graph neural networks (GNNs) have emerged as powerful tools in the field of biology, leveraging the complex relationships and interactions inherent in biological data. Their applications span various domains, including protein function prediction, protein-protein interaction prediction, and in silico drug discovery and development. GNNs are particularly effective in analyzing biological networks due to their ability to model data observed across different levels of biological systems, from molecular graphs involving omics data to population graphs where patients are represented as nodes [1][26].

Despite their promising applications, the use of GNNs in biological contexts is not without challenges and limitations. One significant issue is scalability. As biological datasets grow in size and complexity, the computational demands of GNNs can become prohibitive. The high density of interactions and the often non-linear dynamics of biological networks require substantial computational resources, which can limit the applicability of GNNs to larger datasets. Furthermore, many existing GNN architectures have been primarily developed for smaller, structured datasets, which may not easily translate to the more irregular and unordered nature of biological data [27][29].

Additionally, the interpretability of GNN models poses another challenge. While GNNs can achieve high predictive performance, understanding the underlying mechanisms of how these models make decisions can be difficult. This lack of interpretability can hinder their adoption in critical areas such as clinical diagnostics and therapeutic development, where understanding the rationale behind predictions is essential [3][20].

Moreover, the integration of GNNs with existing biological data and computational frameworks remains a challenge. The diversity of biological data types and the specific requirements of different biological questions necessitate the development of tailored GNN architectures and training methodologies, which can be resource-intensive and time-consuming [6][27].

In summary, while GNNs hold significant promise for advancing biological research and applications, addressing scalability issues, enhancing interpretability, and improving integration with diverse biological datasets are critical areas that require further exploration and development to fully realize their potential in the biological sciences.

4.3 Interpretability of GNN Models

Graph Neural Networks (GNNs) have found diverse applications in biology, significantly enhancing the analysis of complex biological data through their ability to model relationships and interactions among biological entities. These applications include:

1. Single-Cell RNA Sequencing (scRNA-seq) Data Analysis: GNNs are employed to improve cell type prediction by leveraging ligand-receptor interactions and constructing cell-cell association networks, which enhance the biological relevance of cellular communication pathways (Duan et al., 2025) [2].

2. Biological Network Analysis: GNNs are utilized for various tasks such as protein function prediction, protein-protein interaction prediction, and in silico drug discovery. The structure of biological networks allows GNNs to capture intricate dependencies that are often overlooked by traditional neural networks (Muzio et al., 2021) [1].

3. Disease Subnetwork Detection: GNNs facilitate the identification of disease subnetworks by representing patients as nodes in a protein-protein interaction network enriched with multi-omics features. This approach enhances the interpretability and detection of relevant biological interactions associated with diseases (Pfeifer et al., 2022) [8].

4. Link Prediction in Biomedical Networks: GNNs are effective for predicting potential interactions in biomedical networks, such as drug-target interactions and synthetic lethality, by integrating various data sources to enhance prediction accuracy (Long et al., 2022) [4].

5. Cancer Research: GNNs are increasingly applied in cancer and oncology research to handle multimodal data types, improving predictive performance and providing insights into cancer biology (Gogoshin & Rodin, 2023) [3].

6. Monitoring Neurological Disorders: In the context of Parkinson's disease, GNNs have been used to analyze complex relationships among clinical and non-clinical factors, thereby contributing to better monitoring and alert systems (Zafeiropoulos et al., 2023) [31].

Despite the promising applications of GNNs in biology, several challenges and limitations persist:

1. Data Complexity: Biological data is often heterogeneous and high-dimensional, which complicates the integration of various data types and the extraction of meaningful features for GNNs (Li et al., 2023) [26].

2. Model Interpretability: A significant limitation of GNNs is their interpretability. While GNNs can model complex interactions effectively, understanding how these models arrive at specific predictions remains a challenge. The intricacies of the learned representations can obscure the underlying biological processes, making it difficult for researchers to validate and trust the results (Guo et al., 2022) [30].

3. Scalability: As the size of biological networks increases, scalability becomes an issue. Many GNN architectures struggle with large datasets, which can lead to increased computational demands and longer training times (Li et al., 2023) [26].

To address these challenges, ongoing research is focused on enhancing the interpretability of GNN models. For instance, explainable GNNs aim to provide insights into the decision-making processes of the models, thereby improving their utility in biological research (Pfeifer et al., 2022) [8]. Moreover, advancements in GNN architectures and training methodologies continue to evolve, potentially mitigating issues related to scalability and integration of diverse biological data sources.

5 Future Directions

5.1 Integrating GNNs with Other Machine Learning Techniques

Graph Neural Networks (GNNs) have emerged as powerful tools in the field of biology, leveraging their ability to model complex relationships and interactions among biological entities. Their applications span various domains, including molecular property prediction, drug discovery, protein-protein interaction prediction, and disease diagnosis.

In the context of drug discovery, GNNs are utilized to predict drug-target interactions and assess the efficacy of compounds. For instance, a novel framework called PT-GNN has been proposed for link prediction in biomedical networks, effectively integrating multiple data sources to enhance prediction performance for synthetic lethality and drug-target interactions [4]. Additionally, GNNs have been shown to outperform traditional methods in predicting siRNA efficacy in siRNA-mRNA interaction networks, highlighting their potential in therapeutic applications [6].

Furthermore, GNNs are instrumental in analyzing biological networks, such as gene regulatory networks and protein-protein interaction networks. They enable the extraction of insights from complex data structures, facilitating advancements in understanding cellular communication pathways and disease mechanisms [2]. The application of GNNs in multi-omics data integration further illustrates their versatility, providing a framework for personalized nutrition and optimizing health outcomes through tailored dietary interventions [32].

Looking towards future directions, the integration of GNNs with other machine learning techniques holds significant promise. For instance, federated learning approaches can be combined with GNNs to enhance disease prediction while maintaining data privacy across institutions [24]. Additionally, the incorporation of prior knowledge, such as gene regulatory networks and pathway information, can improve the accuracy of tumor risk predictions [33].

Moreover, advancements in GNN architectures, such as the multiphysical GNN model, indicate a trend towards more sophisticated representations of molecular interactions, which can be crucial for applications in drug design [34]. This model's ability to integrate multi-scale interactions and improve feature representation is a step towards addressing the complexities inherent in biological data.

In summary, GNNs are proving to be transformative in biological research, with applications that continue to expand. The integration of GNNs with other machine learning techniques, including federated learning and the incorporation of biological knowledge, presents a fertile ground for future exploration and innovation in the life sciences.

5.2 Expanding Applications to Emerging Biological Fields

Graph Neural Networks (GNNs) have become increasingly significant in various applications within the biological sciences, owing to their ability to effectively model complex relationships and interactions among biological entities. Their applications span several domains, each contributing to advancements in understanding biological processes and improving computational predictions.

One of the primary applications of GNNs is in the analysis of biological networks, where they are utilized for protein function prediction, protein-protein interaction prediction, and in silico drug discovery and development. The inherent structure of biological networks allows GNNs to capture complex dependencies that traditional neural networks may overlook. For instance, in the context of single-cell RNA sequencing (scRNA-seq) data, GNNs have been employed to enhance cell type prediction by utilizing ligand-receptor dynamics, thus improving the accuracy of cellular communication pathway modeling [2].

In the field of drug discovery, GNNs have shown promise in various tasks such as molecular property prediction, interaction prediction, and de novo drug design. They facilitate the integration of multimodal data types, ranging from molecular structures to digital pathology images, thereby enhancing predictive performance [3]. Specifically, GNNs are adept at modeling the relationships within drug-target interactions, which is crucial for developing new therapeutics [25].

Additionally, GNNs have applications in genomic studies, where they help in understanding gene regulatory networks and disease diagnosis. They provide tools for automatic disease prediction from data, enabling researchers to tackle classic questions such as gene interaction prediction [1]. The ability of GNNs to embed graphs into low-dimensional spaces while preserving topological and node properties allows for efficient representation of biological graphs, bridging them with modern machine learning techniques [20].

Moreover, GNNs are increasingly being applied to emerging biological fields such as systems biology, where they aid in modeling complex biological systems and their dynamics. They have been utilized to analyze mass spectrometry and sequencing data, assisting in tasks like node classification and community detection within biological networks [28]. The systematic review of GNNs in biomedical data highlights their versatility and the ongoing evolution of their applications across different biological contexts [26].

As research progresses, future directions for GNN applications in biology are likely to expand into areas such as personalized medicine, where GNNs could be used to tailor treatments based on individual genetic profiles. Furthermore, the integration of GNNs with other artificial intelligence methodologies could lead to novel approaches in understanding and manipulating biological systems [4]. The ongoing exploration of GNNs promises to unlock new insights into the complex interactions that govern biological processes, thus significantly impacting the fields of bioinformatics and computational biology.

5.3 Enhancing Model Interpretability and Usability

Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing complex biological data, facilitating a range of applications across various domains within biology. The utilization of GNNs in biological contexts has significantly advanced the understanding of intricate relationships and interactions among biological entities, such as proteins, genes, and cellular structures.

One of the prominent applications of GNNs is in the realm of protein-protein interaction (PPI) networks. GNNs have been effectively employed for drug target recognition, where they help identify potential therapeutic targets by analyzing the relationships between different proteins within these networks. This capability extends to cancer research, where GNNs contribute to cancer gene discovery, enabling researchers to uncover genetic factors associated with cancer progression and treatment response (Pfeifer et al., 2022) [8].

Moreover, GNNs have been utilized in single-cell RNA sequencing (scRNA-seq) data analysis. In this context, GNNs enhance the biological relevance of cellular communication pathways by extracting ligand-receptor associations and constructing cell-cell association networks. This approach allows for a more nuanced understanding of cellular interactions and heterogeneity within biological systems, addressing the limitations of traditional models that often assume homophily among connected nodes (Duan et al., 2025) [2].

The applications of GNNs also extend to link prediction in biomedical networks, which is crucial for understanding the pathological mechanisms underlying complex diseases. By integrating diverse data sources and employing advanced GNN architectures, researchers have developed frameworks that outperform traditional methods in predicting interactions among biological entities, such as in synthetic lethality and drug-target interaction scenarios (Long et al., 2022) [4].

Additionally, GNNs are being leveraged to enhance model interpretability and usability in clinical settings. The incorporation of explainable AI principles into GNN frameworks allows researchers and clinicians to gain insights into the decision-making processes of these models, thereby fostering trust and facilitating the integration of GNNs into clinical workflows. The use of knowledge graphs further aids in aligning model-driven insights with established medical knowledge, enhancing the interpretability of predictions made by GNNs (Johnson et al., 2024) [35].

In summary, GNNs have found diverse applications in biology, ranging from PPI network analysis and scRNA-seq data interpretation to link prediction in biomedical contexts. The ongoing development of GNN methodologies emphasizes not only the predictive power of these models but also their interpretability, which is crucial for translating computational findings into actionable biological insights. Future research directions will likely continue to focus on enhancing the usability and explainability of GNNs, ultimately leading to more robust applications in the biological sciences.

6 Conclusion

Graph Neural Networks (GNNs) have significantly transformed the landscape of biological research by providing advanced computational tools to analyze complex biological data. Their applications in genomics, drug discovery, protein-protein interactions, systems biology, and disease prediction illustrate their versatility and potential in addressing intricate biological questions. Current research highlights the ability of GNNs to capture the relational nature of biological data, thereby enhancing predictive modeling and insights into biological processes. However, challenges such as data quality, scalability, and model interpretability remain pertinent, necessitating ongoing efforts to refine methodologies and integrate GNNs with other machine learning techniques. Future directions include expanding GNN applications into emerging biological fields, enhancing model interpretability, and leveraging federated learning approaches to improve disease prediction while ensuring data privacy. As GNNs continue to evolve, they promise to unlock new avenues for understanding and manipulating biological systems, paving the way for innovative solutions in healthcare and biotechnology.

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