DeepSide: A Deep Learning Approach for Drug Side Effect Prediction

Uner, Onur Can; Kuru, Halil İbrahim; Cinbiş, Ramazan Gökberk; Taştan, Öznur; Cicek, Ercument

doi:10.1109/tcbb.2022.3141103

Cited by 14 publications

(3 citation statements)

References 39 publications

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“…Landmark-1000 (L1000) gene set is known to be reproducible and capable of inferring expression levels of the majority of other genes ( Subramanian et al 2017 , Jeon et al 2022 ). This gene set is frequently used for characterizing biological samples ( Malta et al 2018 , Wan et al 2020 ) and machine learning-based drug response prediction ( Gardiner et al 2020 , Lu et al 2021 , Uner et al 2023 ). We included the L1000 gene-set as features in our analysis.…”

Section: Methodsmentioning

confidence: 99%

Text-mining-based feature selection for anticancer drug response prediction

Wu,

Zaker,

Ebrahimi

et al. 2024

Bioinformatics Advances

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Motivation Predicting anticancer treatment response from baseline genomic data is a critical obstacle in personalized medicine. Machine learning methods are commonly used for predicting drug response from gene expression data. In the process of constructing these machine learning models, one of the most significant challenges is identifying appropriate features among a massive number of genes. Results In this study, we utilize features (genes) extracted using the text-mining of scientific literatures. Using two independent cancer pharmacogenomic datasets, we demonstrate that text-mining-based features outperform traditional feature selection techniques in machine learning tasks. In addition, our analysis reveals that text-mining feature-based machine learning models trained on in vitro data also perform well when predicting the response of in vivo cancer models. Our results demonstrate that text-mining-based feature selection is an easy to implement approach that is suitable for building machine learning models for anticancer drug response prediction. Availability and implementation https://github.com/merlab/text_features.

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Section: Methodsmentioning

confidence: 99%

Text-mining-based feature selection for anticancer drug response prediction

Wu,

Zaker,

Ebrahimi

et al. 2024

Bioinformatics Advances

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“…There are several methods for predicting ADRs, mainly including multi-label methods [4,5] and methods based on drug-ADR association [6][7][8] . The former uses the characteristics of the drug itself to predict the probability of relevant ADRs and the latter uses the association information between drugs and ADRs to model and calculate the probability of occurrence of drug-ADR pairs.…”

Section: Introductionmentioning

confidence: 99%

A novel pseudo-labeling framework based on matrix integration for adverse drug reaction prediction

Chen,

Han,

et al. 2024

Preprint

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Background Adverse drug reactions (ADRs) are one of the global public health events that seriously endanger human life and cause high economic burdens. Therefore, predicting the possibility of their occurrence and taking early and effective response measures are of great significance. The construction of a correlation matrix between drugs and their adverse reactions, followed by effective correlation data mining, is one of the current strategies to predict ADRs using accessible public data. Since the number of known ADRs in real-world data is far less than the number of their unknown counterparts, the drug-ADR association matrix is very sparse, which greatly affects the classification performance of machine learning methods. Results To effectively solve the problem of sparsity, we propose a novel pseudo-labeled data augmentation framework that mines potential unknown drug-ADR pairs by integrating multiple weighted matrix factorization (MF) models and treating them as pseudo-labeled drug-ADR pairs. Pseudo-labeled data is added to the training set and the MF model is fine-tuned to improve the classification performance. Conclusions This paper reproduces the baselines under the same experimental conditions to evaluate the performance of the proposed method on sparse data from the Side Effect Resource (SIDER) database. Experimental results showed that our method outperformed other baselines in the Area Under Precision-Recall (AUPR) and F1 scores, and still maintained the best performance in sparser scenarios. Furthermore, we conducted a case study and the results showed that our proposed framework efficiently predicted ADRs in the real world.

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“…A feature-derived graph regularization matrix decomposition method was proposed to predict side effects not found based on accessible drug attributes and known drug–side effect connections in medications at present ( Dimitri and Lió, 2017 ). Decision trees and inductive logic methods were introduced by Bresso et al ( Uner et al, 2019 ). Zhang et al inferred potential side effect associations for drugs using a feature selection-based multi-label KNN method ( Xu et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects

Xuan,

Xu,

Cui

et al. 2023

Front. Pharmacol.

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Background: Inferring drug-related side effects is beneficial for reducing drug development cost and time. Current computational prediction methods have concentrated on graph reasoning over heterogeneous graphs comprising the drug and side effect nodes. However, the various topologies and node attributes within multiple drug–side effect heterogeneous graphs have not been completely exploited.Methods: We proposed a new drug-side effect association prediction method, GGSC, to deeply integrate the diverse topologies and attributes from multiple heterogeneous graphs and the self-calibration attributes of each drug-side effect node pair. First, we created two heterogeneous graphs comprising the drug and side effect nodes and their related similarity and association connections. Since each heterogeneous graph has its specific topology and node attributes, a node feature learning strategy was designed and the learning for each graph was enhanced from a graph generative and adversarial perspective. We constructed a generator based on a graph convolutional autoencoder to encode the topological structure and node attributes from the whole heterogeneous graph and then generate the node features embedding the graph topology. A discriminator based on multilayer perceptron was designed to distinguish the generated topological features from the original ones. We also designed representation-level attention to discriminate the contributions of topological representations from multiple heterogeneous graphs and adaptively fused them. Finally, we constructed a self-calibration module based on convolutional neural networks to guide pairwise attribute learning through the features of the small latent space.Results: The comparison experiment results showed that GGSC had higher prediction performance than several state-of-the-art prediction methods. The ablation experiments demonstrated the effectiveness of topological enhancement learning, representation-level attention, and self-calibrated pairwise attribute learning. In addition, case studies over five drugs demonstrated GGSC’s ability in discovering the potential drug-related side effect candidates.Conclusion: We proposed a drug-side effect association prediction method, and the method is beneficial for screening the reliable association candidates for the biologists to discover the actual associations.

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DeepSide: A Deep Learning Approach for Drug Side Effect Prediction

Cited by 14 publications

References 39 publications

Text-mining-based feature selection for anticancer drug response prediction

Text-mining-based feature selection for anticancer drug response prediction

A novel pseudo-labeling framework based on matrix integration for adverse drug reaction prediction

Graph generative and adversarial strategy-enhanced node feature learning and self-calibrated pairwise attribute encoding for prediction of drug-related side effects

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