DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy

Liu, Hui; Zhang, Wenhao; Zou, Bo; Wang, Jinxian; Deng, Yuanyuan; Deng, Lei

doi:10.1093/nar/gkz1007

Cited by 108 publications

(105 citation statements)

References 34 publications

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“…Last but not least, single-drug treatments often fail to inhibit the carcinogenic pathways in cancer cells, due to the intrinsic compensatory mechanism and cross-talk among cellular pathways. Combination drugs have demonstrated high sensitivities and low side effects in cancer therapies, and thus have drawn intensive attention from both the academical and industrial community [33,34,35]. It is crucial to develop in-silico methods that can dissect the mechanism of drug action from the perspective of pathways in modeling drug sensitivity to cancer.…”

Section: Discussionmentioning

confidence: 99%

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

Liu

Deng

Cai

et al. 2020

Preprint

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Motivation: To efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in-silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of mechanism of drug action or limited performance in modeling drug sensitivity. Results: In this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To make advantage of both the excellent predictive ability of deep neural network and the biological knowledge of pathways, we reshape the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets, and demonstrate that pathDNN significantly outperformed canonical DNN model and seven other classical regression models. Most importantly, we observed remarkable activity decreases of disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments show that pathDNN achieves pharmacological interpretability and predictive ability in modeling drug sensitivity to cancer cells. Availability: The web server, as well as the processed data sets and source codes for reproducing our work, is available at

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Section: Discussionmentioning

confidence: 99%

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

Liu

Deng

Cai

et al. 2020

Preprint

Self Cite

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“…In this work, we developed a deep learning framework called 4mcDeep-CBI to identify the 4mC sites. Deep learning related methods are widely used in hot spots prediction of proteinprotein interfaces Wang et al, 2018;Deng et al, 2019;Liu et al, 2019), but we have not found any work with deep learning in 4mC sites prediction, and all previous studies have used SVM machine learning methods. This work is the first study of 4mC sites using deep learning.…”

Section: Introductionmentioning

confidence: 92%

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

2020

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Motivation: N4-methylcytosine (4mC) plays an important role in host defense and transcriptional regulation. Accurate identification of 4mc sites provides a more comprehensive understanding of its biological effects. At present, the traditional machine learning algorithms are used in the research on 4mC sites prediction, but the complexity of the algorithms is relatively high, which is not suitable for the processing of large data sets, and the accuracy of prediction needs to be improved. Therefore, it is necessary to develop a new and effective method to accurately identify 4mC sites.Results: In this work, we found a large number of 4mC sites and non 4mC sites of Caenorhabditis elegans (C. elegans) from the latest MethSMRT website, which greatly expanded the dataset of C. elegans, and developed a hybrid deep neural network framework named 4mcDeep-CBI, aiming to identify 4mC sites. In order to obtain the high latitude information of the feature, we input the preliminary extracted features into the Convolutional Neural Network (CNN) and Bidirectional Long Short Term Memory network (BLSTM) to generate advanced features. Taking the advanced features as algorithm input, we have proposed an integrated algorithm to improve feature representation. Experimental results on large new dataset show that the proposed predictor is able to achieve generally better performance in identifying 4mC sites as compared to the state-of-art predictor. Notably, this is the first study of identifying 4mC sites using deep neural network. Moreover, our model runs much faster than the state-of-art predictor.

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“…With the application of computing technology in the field of biology, more and more public biological databases have also been established, such as HMDD (Huang et al, 2018), miR2Disease (Jiang et al, 2008), DrugCombDB (Liu et al, 2020), and gutMDisorder (Cheng et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network

Wang

Kuang

et al. 2020

Front. Genet.

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Interactions between genetic factors and environmental factors (EFs) play an important role in many diseases. Many diseases result from the interaction between genetics and EFs. The long non-coding RNA (lncRNA) is an important non-coding RNA that regulates life processes. The ability to predict the associations between lncRNAs and EFs is of important practical significance. However, the recent methods for predicting lncRNA-EF associations rarely use the topological information of heterogenous biological networks or simply treat all objects as the same type without considering the different and subtle semantic meanings of various paths in the heterogeneous network. In order to address this issue, a method based on the Gradient Boosting Decision Tree (GBDT) to predict the association between lncRNAs and EFs (GBDTL2E) is proposed in this paper. The innovation of the GBDTL2E integrates the structural information and heterogenous networks, combines the Hetesim features and the diffusion features based on multi-feature fusion, and uses the machine learning algorithm GBDT to predict the association between lncRNAs and EFs based on heterogeneous networks. The experimental results demonstrate that the proposed algorithm achieves a high performance.

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DrugCombDB: a comprehensive database of drug combinations toward the discovery of combinatorial therapy

Cited by 108 publications

References 34 publications

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

GBDTL2E: Predicting lncRNA-EF Associations Using Diffusion and HeteSim Features Based on a Heterogeneous Network

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