MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction
Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrel...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-02-01
|
| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/25/5/2788 |
| _version_ | 1797264458492411904 |
|---|---|
| author | Raihanul Bari Tanvir Md Mezbahul Islam Masrur Sobhan Dongsheng Luo Ananda Mohan Mondal |
| author_facet | Raihanul Bari Tanvir Md Mezbahul Islam Masrur Sobhan Dongsheng Luo Ananda Mohan Mondal |
| author_sort | Raihanul Bari Tanvir |
| collection | DOAJ |
| description | Accurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients’ profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SUPREME, use a graph convolutional network (GCN), which fails to consider the level of importance of neighboring nodes on a particular node. To address this gap, we hypothesize that paying attention to each neighbor or providing appropriate weights to neighbors based on their importance might improve the cancer subtype prediction. The natural choice to determine the importance of each neighbor of a node in a graph is to explore the graph attention network (GAT). Here, we propose MOGAT, a novel multi-omics integration approach, leveraging GAT models that incorporate graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism to extract appropriate information for a specific sample by assigning unique attention coefficients to neighboring samples. Based on our knowledge, our group is the first to explore GAT in multi-omics integration for cancer subtype prediction. To evaluate the performance of MOGAT in predicting cancer subtypes, we explored two sets of breast cancer data from TCGA and METABRIC. Our proposed approach, MOGAT, outperforms MOGONET by 32% to 46% and SUPREME by 2% to 16% in cancer subtype prediction in different scenarios, supporting our hypothesis. Our results also showed that GAT embeddings provide a better prognosis in differentiating the high-risk group from the low-risk group than raw features. |
| first_indexed | 2024-04-25T00:29:13Z |
| format | Article |
| id | doaj.art-a19d3f65e013445f867727b4b607b566 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-04-25T00:29:13Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-a19d3f65e013445f867727b4b607b5662024-03-12T16:46:19ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672024-02-01255278810.3390/ijms25052788MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype PredictionRaihanul Bari Tanvir0Md Mezbahul Islam1Masrur Sobhan2Dongsheng Luo3Ananda Mohan Mondal4Knight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USAKnight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USAKnight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USAKnight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USAKnight Foundation School of Computing and Information Sciences, Florida International University, Miami, FL 33199, USAAccurate cancer subtype prediction is crucial for personalized medicine. Integrating multi-omics data represents a viable approach to comprehending the intricate pathophysiology of complex diseases like cancer. Conventional machine learning techniques are not ideal for analyzing the complex interrelationships among different categories of omics data. Numerous models have been suggested using graph-based learning to uncover veiled representations and network formations unique to distinct types of omics data to heighten predictions regarding cancers and characterize patients’ profiles, amongst other applications aimed at improving disease management in medical research. The existing graph-based state-of-the-art multi-omics integration approaches for cancer subtype prediction, MOGONET, and SUPREME, use a graph convolutional network (GCN), which fails to consider the level of importance of neighboring nodes on a particular node. To address this gap, we hypothesize that paying attention to each neighbor or providing appropriate weights to neighbors based on their importance might improve the cancer subtype prediction. The natural choice to determine the importance of each neighbor of a node in a graph is to explore the graph attention network (GAT). Here, we propose MOGAT, a novel multi-omics integration approach, leveraging GAT models that incorporate graph-based learning with an attention mechanism. MOGAT utilizes a multi-head attention mechanism to extract appropriate information for a specific sample by assigning unique attention coefficients to neighboring samples. Based on our knowledge, our group is the first to explore GAT in multi-omics integration for cancer subtype prediction. To evaluate the performance of MOGAT in predicting cancer subtypes, we explored two sets of breast cancer data from TCGA and METABRIC. Our proposed approach, MOGAT, outperforms MOGONET by 32% to 46% and SUPREME by 2% to 16% in cancer subtype prediction in different scenarios, supporting our hypothesis. Our results also showed that GAT embeddings provide a better prognosis in differentiating the high-risk group from the low-risk group than raw features.https://www.mdpi.com/1422-0067/25/5/2788cancer subtype predictiongraph neural networkgraph attention networkmulti-omics integration |
| spellingShingle | Raihanul Bari Tanvir Md Mezbahul Islam Masrur Sobhan Dongsheng Luo Ananda Mohan Mondal MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction International Journal of Molecular Sciences cancer subtype prediction graph neural network graph attention network multi-omics integration |
| title | MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction |
| title_full | MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction |
| title_fullStr | MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction |
| title_full_unstemmed | MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction |
| title_short | MOGAT: A Multi-Omics Integration Framework Using Graph Attention Networks for Cancer Subtype Prediction |
| title_sort | mogat a multi omics integration framework using graph attention networks for cancer subtype prediction |
| topic | cancer subtype prediction graph neural network graph attention network multi-omics integration |
| url | https://www.mdpi.com/1422-0067/25/5/2788 |
| work_keys_str_mv | AT raihanulbaritanvir mogatamultiomicsintegrationframeworkusinggraphattentionnetworksforcancersubtypeprediction AT mdmezbahulislam mogatamultiomicsintegrationframeworkusinggraphattentionnetworksforcancersubtypeprediction AT masrursobhan mogatamultiomicsintegrationframeworkusinggraphattentionnetworksforcancersubtypeprediction AT dongshengluo mogatamultiomicsintegrationframeworkusinggraphattentionnetworksforcancersubtypeprediction AT anandamohanmondal mogatamultiomicsintegrationframeworkusinggraphattentionnetworksforcancersubtypeprediction |