A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction

Wu, Lianlian; Ye, Xiaona; Zhang, Yixin; Gao, Jie; Sui, Binsheng; Wen, Yao; Wu, Qingqiang; Liu, Kun-Hong; He, Song; Bo, Xiaochen

doi:10.1021/acs.jcim.3c00260

Cited by 3 publications

(2 citation statements)

References 63 publications

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“…In addition, the synergistic potentials are observed on the drug combinations with the highest score in the HT29 cell line and A375 cell line. Wu et al validated cellular experiments on the novel drug combination. Dose–response values of HT29 cells were obtained from the experiments.…”

Section: Results and Discussionmentioning

confidence: 99%

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations

Pang,

Chen,

Lin

et al. 2024

J. Chem. Inf. Model.

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Combination therapy is a promising strategy for the successful treatment of cancer. The large number of possible combinations, however, mean that it is laborious and expensive to screen for synergistic drug combinations in vitro. Nevertheless, because of the availability of high-throughput screening data and advances in computational techniques, deep learning (DL) can be a useful tool for the prediction of synergistic drug combinations. In this study, we proposed a multimodal DL framework, MMSyn, for the prediction of synergistic drug combinations. First, features embedded in the drug molecules were extracted: structure, fingerprint, and string encoding. Then, gene expression data, DNA copy number, and pathway activity were used to describe cancer cell lines. Finally, these processed features were integrated using an attention mechanism and an interaction module and then input into a multilayer perceptron to predict drug synergy. Experimental results showed that our method outperformed five state-of-the-art DL methods and three traditional machine learning models for drug combination prediction. We verified that MMSyn achieved superior performance in stratified cross-validation settings using both the drug combination and cell line data. Moreover, we performed a set of ablation experiments to illustrate the effectiveness of each component and the efficacy of our model. In addition, our visual representation and case studies further confirmed the effectiveness of our model. All results showed that MMSyn can be used as a powerful tool for the prediction of synergistic drug combinations.

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Section: Results and Discussionmentioning

confidence: 99%

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations

Pang,

Chen,

Lin

et al. 2024

J. Chem. Inf. Model.

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“…The framework included imbalanced data processing and a search for global optimal solutions. The results indicated that this method has a more accurate recommendation ability compared to the other 11 algorithms [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

E-commerce recommender system based on improved K-means commodity information management model

Zhang,

2024

Heliyon

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Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

Rakhshaninejad,

Fathian,

Shirkoohi

et al. 2024

BMC Bioinformatics

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Breast cancer remains a major public health challenge worldwide. The identification of accurate biomarkers is critical for the early detection and effective treatment of breast cancer. This study utilizes an integrative machine learning approach to analyze breast cancer gene expression data for superior biomarker and drug target discovery. Gene expression datasets, obtained from the GEO database, were merged post-preprocessing. From the merged dataset, differential expression analysis between breast cancer and normal samples revealed 164 differentially expressed genes. Meanwhile, a separate gene expression dataset revealed 350 differentially expressed genes. Additionally, the BGWO_SA_Ens algorithm, integrating binary grey wolf optimization and simulated annealing with an ensemble classifier, was employed on gene expression datasets to identify predictive genes including TOP2A, AKR1C3, EZH2, MMP1, EDNRB, S100B, and SPP1. From over 10,000 genes, BGWO_SA_Ens identified 1404 in the merged dataset (F1 score: 0.981, PR-AUC: 0.998, ROC-AUC: 0.995) and 1710 in the GSE45827 dataset (F1 score: 0.965, PR-AUC: 0.986, ROC-AUC: 0.972). The intersection of DEGs and BGWO_SA_Ens selected genes revealed 35 superior genes that were consistently significant across methods. Enrichment analyses uncovered the involvement of these superior genes in key pathways such as AMPK, Adipocytokine, and PPAR signaling. Protein-protein interaction network analysis highlighted subnetworks and central nodes. Finally, a drug-gene interaction investigation revealed connections between superior genes and anticancer drugs. Collectively, the machine learning workflow identified a robust gene signature for breast cancer, illuminated their biological roles, interactions and therapeutic associations, and underscored the potential of computational approaches in biomarker discovery and precision oncology.

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A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction

Cited by 3 publications

References 63 publications

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations

MMSyn: A New Multimodal Deep Learning Framework for Enhanced Prediction of Synergistic Drug Combinations

E-commerce recommender system based on improved K-means commodity information management model

Refining breast cancer biomarker discovery and drug targeting through an advanced data-driven approach

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