Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy

Li, Jin; Huo, Yang; Wu, Xue; Liu, Enze; Zeng, Zhi; Tian, Zhen; Fan, Kunjie; Stover, Daniel G.; Cheng, Lijun; Li, Lang

doi:10.3390/biology9090278

Cited by 11 publications

(6 citation statements)

References 46 publications

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“…To evaluate the performance of DCE-DForest, DCE-DForest model is compared with some current state-of-the-art methods, including XGBOOST [ 14 ], logistic regression (LR) [ 15 ], DeepSynergy [ 2 ], and NN-XIA [ 3 ]. These models are excellent algorithms in the field of anticancer drug effect prediction in recent years.…”

Section: Resultsmentioning

confidence: 99%

DCE-DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects

Zhang

Xue

et al. 2022

Computational and Mathematical Methods in Medicine

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Drug combinations have recently been studied intensively due to their critical role in cancer treatment. Computational prediction of drug synergy has become a popular alternative strategy to experimental methods for anticancer drug synergy predictions. In this paper, a deep learning model called DCE-DForest is proposed to predict the synergistic effect of drug combinations. To sufficiently extract drug information, the paper leverages BERT (Bidirectional Encoder Representations from Transformers) to encode the drug and the deep forest to model the nonlinear relationship between the drugs and cell lines. The experimental results on the synergy datasets demonstrate that the proposed method consistently shows superior performance over the other machine learning models.

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Section: Resultsmentioning

confidence: 99%

DCE-DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects

Zhang

Xue

et al. 2022

Computational and Mathematical Methods in Medicine

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“…Strategically, the -omics change to some extent represents a dynamic profiling of compound–target interactions, containing useful information to assess anticancer effects for combinational components as well as other drug activities and responses ( Palmer et al, 2019 ). Advances in cancer system biology enabled the integration of signaling networks, pathway profiles, and -omics change of drug treatment, which can be utilized to model the combinational effects of compound actions ( Li et al, 2020 ) or infer cooperative partners for synergistic actions, as being demonstrated in this work.…”

Section: Discussionmentioning

confidence: 99%

Probing Synergistic Targets by Natural Compounds for Hepatocellular Carcinoma

Gao

Yin

et al. 2021

Front. Cell Dev. Biol.

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BackgroundDesigning combination drugs for malignant cancers has been restricted due to the scarcity of synergy-medicated targets, while some natural compounds have demonstrated potential to enhance anticancer effects.MethodsWe here explored the feasibility of probing synergy-mediated targets by Berberine (BER) and Evodiamine (EVO) in hepatocellular carcinoma (HCC). Using the genomics-derived HCC signaling networks of compound treatment, NF-κB and c-JUN were inferred as key responding elements with transcriptional activity coinhibited during the synergistic cytotoxicity induction in BEL-7402 cells. Then, selective coinhibitors of NF-κB and c-JUN were tested demonstrating similar synergistic antiproliferation activity.ResultsConsistent with in vivo experiments of zebrafish, coinhibitors were found to significantly reduce tumor growth by 79% and metastasis by 96% compared to blank control, accompanied by anti-angiogenic activity. In an analysis of 365 HCC individuals, the low expression group showed significantly lower malignancies and better prognosis, with the median survival time increased from 67 to 213%, compared to the rest of the groups.ConclusionTogether, NF-κB and c-JUN were identified as promising synergistic inducers in developing anti-HCC therapies. Also, our method may provide a feasible strategy to explore new targeting space from natural compounds, opening opportunities for the rational design of combinational formulations in combatting malignant cancers.

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“…Before 2018, some machine learning methods were used to predict drug synergy, such as Bayesian Network [17], Logistic Regression [18], Random Forest [19,20] and XGBoost [21][22][23]. In recent years, more and more deep learning methods have been developed [8,24].…”

Section: Introductionmentioning

confidence: 99%

Dual-view jointly learning improves personalized drug synergy prediction

Li,

shen,

Feng

et al. 2024

Preprint

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Background: Accurate and robust estimation of the synergistic drug combination is important for precision medicine. Although some computational methods have been developed, some predictions are still unreliable especially for the cross-dataset predictions, due to the complex mechanism of drug combinations and heterogeneity of cancer samples. Methods: We have proposed JointSyn that utilizes dual-view jointly learning to predict sample-specific effects of drug combination from drug and cell features. JointSyn capture the drug synergy related features from two views. One view is the embedding of drug combination on cancer cell lines, and the other view is the combination of two drugs' embeddings on cancer cell lines. Finally, the prediction net uses the features learned from the two views to predict the drug synergy of the drug combination on the cell line. In addition, we used the fine-tuning method to improve the JointSyn's performance on the unseen subset within a dataset or cross dataset. Results: JointSyn outperforms existing state-of-the-art methods in predictive accuracy and robustness across various benchmarks. Each view of JointSyn captures drug synergy-related characteristics and make complementary contributes to the final accurate prediction of drug combination. Moreover, JointSyn with fine-tuning improves its generalization ability to predict a novel drug combination or cancer sample only using a small number of experimental measurements. We also used JointSyn to generate an estimated atlas of drug synergy for pan-cancer and explored the differential pattern among cancers. Conclusions: These results demonstrate the potential of JointSyn to predict drug synergy, supporting the development of personalized combinatorial therapies. The source code is available on GitHub at https://github.com/LiHongCSBLab/JointSyn.

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Essentiality and Transcriptome-Enriched Pathway Scores Predict Drug-Combination Synergy

Cited by 11 publications

References 46 publications

DCE-DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects

DCE-DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects

Probing Synergistic Targets by Natural Compounds for Hepatocellular Carcinoma

Dual-view jointly learning improves personalized drug synergy prediction

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