NDDSA: A network- and domain-based method for predicting drug-side effect associations

Shabani-Mashcool, Soheila; Marashi, Sayed-Amir; Gharaghani, Sajjad

doi:10.1016/j.ipm.2020.102357

Cited by 14 publications

(3 citation statements)

References 41 publications

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“…We measure the performance of our model using a number of metrics that are recognized by a wide range of work [ 51 ]. The prediction metrics used are precision, F-measure, accuracy and Area Under the Receiver Operating Characteristic (AUC).…”

Section: Discussionmentioning

confidence: 99%

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

et al. 2021

Diagnostics

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Increasingly, machine learning methods have been applied to aid in diagnosis with good results. However, some complex models can confuse physicians because they are difficult to understand, while data differences across diagnostic tasks and institutions can cause model performance fluctuations. To address this challenge, we combined the Deep Ensemble Model (DEM) and tree-structured Parzen Estimator (TPE) and proposed an adaptive deep ensemble learning method (TPE-DEM) for dynamic evolving diagnostic task scenarios. Different from previous research that focuses on achieving better performance with a fixed structure model, our proposed model uses TPE to efficiently aggregate simple models more easily understood by physicians and require less training data. In addition, our proposed model can choose the optimal number of layers for the model and the type and number of basic learners to achieve the best performance in different diagnostic task scenarios based on the data distribution and characteristics of the current diagnostic task. We tested our model on one dataset constructed with a partner hospital and five UCI public datasets with different characteristics and volumes based on various diagnostic tasks. Our performance evaluation results show that our proposed model outperforms other baseline models on different datasets. Our study provides a novel approach for simple and understandable machine learning models in tasks with variable datasets and feature sets, and the findings have important implications for the application of machine learning models in computer-aided diagnosis.

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Section: Discussionmentioning

confidence: 99%

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

et al. 2021

Diagnostics

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“…They first used Multinomial NB and SVM to classify reviews into ADR or non-ADR, and then performed text analysis to identify potentially dangerous drugs from the ADRs related mentions [17]. In addition, some studies proposed to infer the associations between the side effects of approved drugs to further predict potential side effects for new drugs, for example [18,19].…”

Section: Related Workmentioning

confidence: 99%

Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning

Huang

Lee

2022

Healthcare

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Social forums offer a lot of new channels for collecting patients’ opinions to construct predictive models of adverse drug reactions (ADRs) for post-marketing surveillance. However, due to the characteristics of social posts, there are many challenges still to be solved when deriving such models, mainly including problems caused by data sparseness, data features with a high-dimensionality, and term diversity in data. To tackle these crucial issues related to identifying ADRs from social posts, we perform data analytics from the perspectives of data balance, feature selection, and feature learning. Meanwhile, we design a comprehensive experimental analysis to investigate the performance of different data processing techniques and data modeling methods. Most importantly, we present a deep learning-based approach that adopts the BERT (Bidirectional Encoder Representations from Transformers) model with a new batch-wise adaptive strategy to enhance the predictive performance. A series of experiments have been conducted to evaluate the machine learning methods with both manual and automated feature engineering processes. The results prove that with their own advantages both types of methods are effective in ADR prediction. In contrast to the traditional machine learning methods, our feature learning approach can automatically achieve the required task to save the manual effort for the large number of experiments.

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“…Consequently, it is inevitable to identify and address protective flaws in drug development. Approximately two million people are affected by the adverse effects of drugs worldwide [3], [4]. Drugs are immediately withdrawn (e.g., Rofecoxib) from the market due to their unaccepted DSEs [5].…”

Section: Introductionmentioning

confidence: 99%

Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images

Arshed,

Ibrahim,

Mumtaz

et al. 2024

IEEE Access

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Drug Side Effects (DSE) are inconvenient and inadvertent retorts of the drugs. DSEs impact on public health and healthcare can prove costly. These DSEs can be an important factor in the failure/acceptance of drugs. Every approved drug should be either free from DSEs or these should be minor and reported properly. The drug discovery process should be capable of predicting and preventing these effects in advance. Previously proposed studies for the prediction/prevention of DSEs utilized the features of 1D drug chemical structures or Natural Language Processing (NLP). Both these techniques required a complex transformation process. In this research authors have proposed a deep learning model, specifically using a transfer learning approach to predict DSEs directly from 2D chemical structure images, eliminating the need for the hefty transformation process of the NLP domain. for this study, a unique dataset is prepared that associates each image (taken from PubChem) with its specific side effects (SIDER). The results are evaluated using Accuracy, Precision, Recall and F-measure. The proposed model showed its dominance with an Accuracy of 73%, Precision of 83%, Recall of 73%, and an F1 score of 75%. The achieved results of the proposed model are compared against established transfer learning models like VGG16, DenseNet121 and some previously used traditional machine learning models like SVM and KNN. The collected results indicate a significant advancement in predicting drug side effects and offer a promising avenue for streamlining the drug development process.

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NDDSA: A network- and domain-based method for predicting drug-side effect associations

Cited by 14 publications

References 41 publications

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

An Adaptive Deep Ensemble Learning Method for Dynamic Evolving Diagnostic Task Scenarios

Predicting Adverse Drug Reactions from Social Media Posts: Data Balance, Feature Selection and Deep Learning

Img2Side: A Transfer Learning Based Model for Predicting Drug Side Effects Using 2D Chemical Structural Images

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