Integrating Multiple Gene Expression Datasets and Domain Knowledge Using Knowledge Graphs to Improve Diabetes Prediction

Core Concepts

Integrating multiple gene expression datasets and domain-specific knowledge using knowledge graphs can improve the performance of machine learning models in predicting diabetes.

Abstract

This work proposes a novel approach to address the challenges in using gene expression data for diabetes prediction. The key insights are:

Gene expression datasets often have limited sample sizes, making it difficult to train effective machine learning models. Combining multiple expression datasets can increase the sample pool, but integrating the information from diverse datasets is challenging due to differences in measured genes.

The authors build a knowledge graph (KG) that integrates gene expression data from multiple datasets as well as domain-specific knowledge about protein functions and interactions. This allows them to represent the heterogeneous data in a unified knowledge space.

They then employ KG embedding methods to generate vector representations of patients, which are used as input for a classifier to predict the likelihood of a patient having diabetes.

Experiments show that incorporating information from multiple gene expression datasets and domain knowledge into the KG improves the performance of diabetes prediction compared to using a single dataset or a naive combination of datasets.

Two strategies are explored for representing gene expression data in the KG - using blank nodes with binning, and linking patients directly to genes based on expression values. The latter approach, combined with representing patients as the weighted average of gene embeddings, achieves the best performance.

The results highlight the efficacy of integrating heterogeneous biomedical data sources using knowledge graphs to enhance the predictive power of machine learning models in healthcare applications.

Stats

Diabetes is a chronic health condition that has emerged as a worldwide health issue, impacting millions of people globally.
In 2019, diabetes directly contributed to 1.5 million deaths, with 48% occurring before the age of 70.
Diabetes is associated with the development of several comorbidities, such as blindness, kidney failure, heart attacks, strokes, and lower limb amputation.

Quotes

"Diabetes is a chronic health condition resulting from insufficient insulin production by the pancreas or the body's inability to utilize the insulin it generates effectively."
"While gene expression datasets are readily accessible in public databases, and gene expression analysis is a powerful tool for pinpointing genes associated with diseases, particularly in the context of diabetes prediction, a significant issue arises in handling this type of data. On the one hand, gene expression datasets often exhibit a limitation in sample size, with a relatively small number of included samples."

Key Insights Distilled From

Integrating Heterogeneous Gene Expression Data through Knowledge Graphs for Improving Diabetes Prediction

by Rita T. Sous... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2404.14970.pdf

Deeper Inquiries

How can the proposed knowledge graph-based approach be extended to incorporate other types of biomedical data, such as electronic health records or imaging data, to further improve diabetes prediction

The knowledge graph-based approach proposed in the study can be extended to incorporate other types of biomedical data, such as electronic health records (EHR) or imaging data, to further enhance diabetes prediction. By integrating EHR data, which includes patient demographics, medical history, lab results, and treatment information, the knowledge graph can capture a more comprehensive view of the patient's health status. This additional data can provide valuable insights into the patient's overall health, comorbidities, and risk factors associated with diabetes.
Similarly, incorporating imaging data, such as MRI or CT scans, can offer visual representations of organ health, particularly the pancreas, which plays a crucial role in diabetes. By linking imaging data to the knowledge graph, patterns or anomalies in organ structure or function can be identified, aiding in early detection and personalized treatment strategies.
Integrating diverse biomedical data types into the knowledge graph allows for a holistic approach to diabetes prediction, leveraging a wide range of information to improve the accuracy and reliability of predictive models. By combining gene expression data with EHR and imaging data, the knowledge graph can offer a comprehensive understanding of the complex factors influencing diabetes development and progression.

What are the potential limitations or biases in the gene expression datasets used in this study, and how might they impact the generalizability of the findings

The gene expression datasets used in the study may have potential limitations or biases that could impact the generalizability of the findings. Some of these limitations include:

Sample Size: Gene expression datasets often have limited sample sizes, which may not fully represent the genetic diversity within the population. This limitation can lead to overfitting or underfitting of machine learning models, affecting the predictive performance.

Dataset Heterogeneity: Different gene expression datasets may vary in terms of experimental protocols, platforms, and data preprocessing methods. This heterogeneity can introduce batch effects or technical biases, impacting the integration of data from multiple sources.

Missing Data: Gene expression datasets may have missing values or incomplete information, which can affect the quality of the analysis and model training. Imputation methods or handling missing data strategies need to be carefully considered to mitigate these issues.

Selection Bias: The selection of genes or biomarkers in the expression datasets may introduce bias towards known genes associated with diabetes, potentially overlooking novel biomarkers or pathways relevant to the disease.

To address these limitations, researchers should carefully evaluate and preprocess the gene expression datasets, consider data normalization techniques, account for batch effects, and validate the predictive models on independent datasets to ensure robustness and generalizability of the findings.

Could the knowledge graph-based representation be leveraged to gain deeper biological insights into the underlying mechanisms and pathways involved in the development of diabetes

The knowledge graph-based representation can be leveraged to gain deeper biological insights into the underlying mechanisms and pathways involved in the development of diabetes. By integrating gene expression data with domain-specific knowledge, such as Gene Ontology (GO) terms and protein-protein interaction (PPI) data, the knowledge graph can elucidate the molecular interactions, regulatory networks, and biological processes associated with diabetes.

Pathway Analysis: The knowledge graph can be used to identify enriched pathways or biological processes linked to diabetes by analyzing the relationships between genes, proteins, and GO terms. This analysis can reveal key pathways dysregulated in diabetes and potential therapeutic targets.

Network Analysis: By exploring the protein-protein interaction data within the knowledge graph, researchers can uncover protein networks and hubs that play crucial roles in diabetes pathogenesis. Network analysis can identify novel biomarkers or drug targets for diabetes treatment.

Functional Annotation: Integrating GO terms with gene expression data allows for functional annotation of genes associated with diabetes. This annotation provides insights into the molecular functions, biological processes, and cellular components relevant to diabetes development.

Predictive Modeling: The knowledge graph can enhance predictive modeling by incorporating biological knowledge and interactions between genes. Machine learning models trained on knowledge graph embeddings can capture complex relationships and improve the accuracy of diabetes prediction.

Overall, leveraging the knowledge graph-based representation enables a systems biology approach to studying diabetes, integrating multi-omics data and domain knowledge to unravel the intricate mechanisms underlying the disease.