Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

La, Mary; Sedykh, Alexander; Fourches, Denis; Muratov, Eugene; Tropsha, Alexander

doi:10.1007/s40264-018-0688-5

Cited by 5 publications

(3 citation statements)

References 64 publications

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“…For instance, Kim et al reviewed existing statistical and machine-learning methods to detect drug-related side effects in humans [ 59 ]. La et al integrated theoretical biological data into machine-learning models to predict Active Pharmaceutical Ingredient (API) side effects, validating their approach against real-world clinical outcomes [ 63 ]. This underscores the multifaceted nature of data used in predicting drug-related side effects, reflecting the inherent challenges in directly comparing machine learning techniques used across these two distinct groups of studies.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review

Toni,

Ayatollahi,

Abbaszadeh

et al. 2024

Pharmaceuticals

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Background: Drug safety relies on advanced methods for timely and accurate prediction of side effects. To tackle this requirement, this scoping review examines machine-learning approaches for predicting drug-related side effects with a particular focus on chemical, biological, and phenotypical features. Methods: This was a scoping review in which a comprehensive search was conducted in various databases from 1 January 2013 to 31 December 2023. Results: The results showed the widespread use of Random Forest, k-nearest neighbor, and support vector machine algorithms. Ensemble methods, particularly random forest, emphasized the significance of integrating chemical and biological features in predicting drug-related side effects. Conclusions: This review article emphasized the significance of considering a variety of features, datasets, and machine learning algorithms for predicting drug-related side effects. Ensemble methods and Random Forest showed the best performance and combining chemical and biological features improved prediction. The results suggested that machine learning techniques have some potential to improve drug development and trials. Future work should focus on specific feature types, selection techniques, and graph-based methods for even better prediction.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review

Toni,

Ayatollahi,

Abbaszadeh

et al. 2024

Pharmaceuticals

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“…These approaches have been extensively employed to discover drugs against multiple diseases including recent gargantuan efforts to repurpose drugs against COVID-19 (reviewed recently by Muratov et al [3]). In recent years, literature-based discovery (LBD) has been added to the arsenal of tools employed specially to identify novel or poorly known uses of existing drugs (also known as ‘ drug repurposing ’ [4]) or identify potential side effects of the existing drugs [5]. Commonly, LBD approaches have been used to analyze textual sources such as abstracts of full texts of the scientific papers [6,7]) although in some cases social media mining has been explored as well [8].…”

Section: Background and Significancementioning

confidence: 99%

Text Mining of the People’s Pharmacy Radio Show Transcripts Can Identify Novel Drug Repurposing Hypotheses

Yedida¹,

Beasley

Korn

et al. 2022

Preprint

Self Cite

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Objective: Social media mining may provide surprising information about unknown effects of drugs. We endeavored to uncover such unknown drug-disease relationships by text mining of audio record transcripts from the popular NPR show, The People's Pharmacy. Materials and Methods: We used Google Cloud to transcribe episodes of the NPR podcast into textual documents. We then built a pipeline for systematically pre-processing the text to ensure quality input to the core classification model. Finally, results of the model were filtered by a series of post-processing steps. Our classification model itself uses the FLAIR language model pre-trained on PubMed abstracts. The modular nature of our pipeline allows for ease of future developments in this area by substituting higher quality components at each stage of the pipeline. To validate the drug-disease relating assertions extracted from the podcast, we utilized the DrugCentral database and ROBOKOP biomedical knowledge graph, which capture drug-disease relationships for FDA approved medications. Results: Our model identified 128 drug-disease pairs that were found in DrugCentral and 112 novel candidate pairs requiring expert review. To demonstrate the expert review process, we found literature evidence supporting the assertions for novel drug-disease pairs. Discussion and Conclusion: Text mining of social media is increasingly used to uncover novel relationships between semantic concepts corresponding to biomedical concepts. However, mining audio transcripts of specialized podcast shows has not been explored previously for this purpose. Using this approach, we have identified several unknown drug-disease relationships with support in biomedical literature. The proposed approach can extend beyond radio podcasts and could be applied to any source of audio and textual data.

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“…Independent input variables are sets of quantitative characteristics of molecules included in the studies and called descriptors. They represent topological properties of molecular graphs, describing three‐dimensional molecular fields and interactions with target proteins, just physicochemical properties, etc. Development of QSAR modeling and its using in adjacent fields of research is discussed in a recent publication by D.A.…”

Section: Introductionmentioning

confidence: 99%

Value of p‐Value

Gelbukh

2019

Molecular Informatics

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The goal of this manuscript is to discuss important aspects of external validation of classification and category Quantitative Structure -Activity/Property/Toxicity Relationship QS/A/P/T/R models that to the best of author's knowledge are not addressed in publications. Statistical significance (in terms of p-value) and accuracy of prediction (in terms of Correct Classification Rate (CCR)) of external validation set compounds are among most important characteristics of the models. We assert that in most cases the models built for classification or category response variable should be statistically significant and predictive for each class or category. We show that three thresholds of the number of compounds in each class or category of the external validation sets should be satisfied. 1) The p-value criterion can never be satisfied, if the number of compounds is below the first threshold. 2) If the number of compounds is between the first and the second thresholds, p-value criterion should be used. 3) If it is higher than the third threshold, classification or category accuracy criterion should be used. 4) If the number of compounds is between second and third thresholds, either one or the other criterion should be used depending on the value of p-value. 5) When the number of compounds in the class approaches infinity, the maximum relative error of prediction approaches the relative expected error. The results are of interest in other areas of multidimensional data analysis.Keywords: external validation of QSAR models · classification and category response variables · p-values and correct classification rate as alternative criteria of prediction accuracy[a] A. Golbraikh

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Predicting Adverse Drug Effects from Literature- and Database-Mined Assertions

Cited by 5 publications

References 64 publications

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review

Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review

Text Mining of the People’s Pharmacy Radio Show Transcripts Can Identify Novel Drug Repurposing Hypotheses

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