MMF-MCP: A Deep Transfer Learning Model Based on Multimodal Information Fusion for Molecular Feature Extraction and Carcinogenicity Prediction
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| Titel: | MMF-MCP: A Deep Transfer Learning Model Based on Multimodal Information Fusion for Molecular Feature Extraction and Carcinogenicity Prediction |
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| Autoren: | Liwei Liu, Qi Zhang, Yuxiao Wei |
| Publikationsjahr: | 2025 |
| Schlagwörter: | Medicine, Biotechnology, Cancer, Virology, Space Science, Biological Sciences not elsewhere classified, Mathematical Sciences not elsewhere classified, Chemical Sciences not elsewhere classified, Information Systems not elsewhere classified, significantly outperforming state, multimodal information fusion, https :// github, enabling knowledge transfer, convolutional neural networks, process molecular images, molecular images demonstrate, benchmark data sets, transfer learning strategy, providing significant assistance, deep residual network, predict molecular carcinogenicity, molecular feature extraction, carcinogenicity data set, molecular carcinogenicity, deep learning, data quality, significant improvement, feature richness, carcinogenicity prediction, visually observing |
| Beschreibung: | Molecular carcinogenicity is a crucial factor in the development of cancer, and accurate prediction of it is vital for cancer prevention, treatment, and drug development. In recent years, deep learning has been applied to predict molecular carcinogenicity, but due to limitations in data quality and feature richness, these methods still need improvement in terms of accuracy, robustness, and interpretability. In this article, we propose a deep transfer learning model based on multimodal information fusion, called MMF-MCP, for molecular feature extraction and carcinogenicity prediction. We extract molecular graph features and fingerprint features using graph attention networks and convolutional neural networks, respectively, and process molecular images through a deep residual network, SE-ResNet18, equipped with a squeeze-and-excitation module. To more effectively utilize limited carcinogenicity data and enhance the model’s predictive performance and generalization ability, we further apply a transfer learning strategy by pretraining the model on a molecular mutagenicity data set and then fine-tuning it on the carcinogenicity data set, enabling knowledge transfer and significant improvement in model performance. MMF-MCP achieves average ACC, AUC, SE, and SP scores of 0.8452, 0.8513, 0.8571, and 0.8333 on benchmark data sets for molecular carcinogenicity, significantly outperforming state-of-the-art molecular carcinogenicity prediction methods. Additionally, the visualization results of MMF-MCP on molecular images demonstrate its strong interpretability, providing significant assistance in visually observing and understanding the critical structures and features of molecular carcinogenicity. The source code for MMF-MCP is available at https://github.com/liuliwei1980/MCP. |
| Publikationsart: | article in journal/newspaper |
| Sprache: | unknown |
| DOI: | 10.1021/acs.jcim.5c01362.s001 |
| Verfügbarkeit: | https://doi.org/10.1021/acs.jcim.5c01362.s001 https://figshare.com/articles/journal_contribution/MMF-MCP_A_Deep_Transfer_Learning_Model_Based_on_Multimodal_Information_Fusion_for_Molecular_Feature_Extraction_and_Carcinogenicity_Prediction/29646590 |
| Rights: | CC BY-NC 4.0 |
| Dokumentencode: | edsbas.87FF753F |
| Datenbank: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Molecular carcinogenicity is a crucial factor in the development of cancer, and accurate prediction of it is vital for cancer prevention, treatment, and drug development. In recent years, deep learning has been applied to predict molecular carcinogenicity, but due to limitations in data quality and feature richness, these methods still need improvement in terms of accuracy, robustness, and interpretability. In this article, we propose a deep transfer learning model based on multimodal information fusion, called MMF-MCP, for molecular feature extraction and carcinogenicity prediction. We extract molecular graph features and fingerprint features using graph attention networks and convolutional neural networks, respectively, and process molecular images through a deep residual network, SE-ResNet18, equipped with a squeeze-and-excitation module. To more effectively utilize limited carcinogenicity data and enhance the model’s predictive performance and generalization ability, we further apply a transfer learning strategy by pretraining the model on a molecular mutagenicity data set and then fine-tuning it on the carcinogenicity data set, enabling knowledge transfer and significant improvement in model performance. MMF-MCP achieves average ACC, AUC, SE, and SP scores of 0.8452, 0.8513, 0.8571, and 0.8333 on benchmark data sets for molecular carcinogenicity, significantly outperforming state-of-the-art molecular carcinogenicity prediction methods. Additionally, the visualization results of MMF-MCP on molecular images demonstrate its strong interpretability, providing significant assistance in visually observing and understanding the critical structures and features of molecular carcinogenicity. The source code for MMF-MCP is available at https://github.com/liuliwei1980/MCP. |
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| DOI: | 10.1021/acs.jcim.5c01362.s001 |