Prediction of Chemical Multi-target Profiles and Adverse Outcomes with Systems Toxicology

Wathieu, Henri; Ojo, Abiola; Dakshanamurthy, Sivanesan

doi:10.2174/0929867323666161214115540

Cited by 5 publications

(5 citation statements)

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“…It is a desperate need to efficiently evaluate potential carcinogenic compounds that humans are exposed to in preventing cancer incidence, progression, and high mortality. Several computational and machine learning models have been developed for the prediction of carcinogenic compounds [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. However, most or all of the models are developed as binary or regression models, not as categorical multiclassification models or comprehensive classification models.…”

Section: Resultsmentioning

confidence: 99%

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Limbu

Dakshanamurthy

2022

Sensors

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Determining environmental chemical carcinogenicity is urgently needed as humans are increasingly exposed to these chemicals. In this study, we developed a hybrid neural network (HNN) method called HNN-Cancer to predict potential carcinogens of real-life chemicals. The HNN-Cancer included a new SMILES feature representation method by modifying our previous 3D array representation of 1D SMILES simulated by the convolutional neural network (CNN). We developed binary classification, multiclass classification, and regression models based on diverse non-congeneric chemicals. Along with the HNN-Cancer model, we developed models based on the random forest (RF), bootstrap aggregating (Bagging), and adaptive boosting (AdaBoost) methods for binary and multiclass classification. We developed regression models using HNN-Cancer, RF, support vector regressor (SVR), gradient boosting (GB), kernel ridge (KR), decision tree with AdaBoost (DT), KNeighbors (KN), and a consensus method. The performance of the models for all classifications was assessed using various statistical metrics. The accuracy of the HNN-Cancer, RF, and Bagging models were 74%, and their AUC was ~0.81 for binary classification models developed with 7994 chemicals. The sensitivity was 79.5% and the specificity was 67.3% for the HNN-Cancer, which outperforms the other methods. In the case of multiclass classification models with 1618 chemicals, we obtained the optimal accuracy of 70% with an AUC 0.7 for HNN-Cancer, RF, Bagging, and AdaBoost, respectively. In the case of regression models, the correlation coefficient (R) was around 0.62 for HNN-Cancer and RF higher than the SVM, GB, KR, DTBoost, and NN machine learning methods. Overall, the HNN-Cancer performed better for the majority of the known carcinogen experimental datasets. Further, the predictive performance of HNN-Cancer on diverse chemicals is comparable to the literature-reported models that included similar and less diverse molecules. Our HNN-Cancer could be used in identifying potentially carcinogenic chemicals for a wide variety of chemical classes.

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Section: Resultsmentioning

confidence: 99%

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Limbu

Dakshanamurthy

2022

Sensors

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“…Mapping all the biological pathways that chemicals can interfere with could facilitate the discovery of novel mechanisms of action. Advanced literature and database search have been used to identify new mechanisms of action [52] and diverse omics and in silico approaches have been used to unravel possible interactions between chemicals and proteins [53,54]. A common strategy to identify the molecular network that can be altered by chemical exposures is to look into the changes in expression of transcripts combined with gene enrichment analysis [55].…”

Section: Risk Assessment Challengesmentioning

confidence: 99%

Toxicoproteomics, from finding molecular targets to evaluating the impact on human health

Fallas

2023

Linköping University Medical Dissertations

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“…For example, in [19], a generalized framework to predict cognitive and symptomatic scores for schizophrenia and healthy controls using magnetic resonance imaging (MRI) is proposed. Moreover, in [20], the authors describe some computational tools for the prediction of chemical multi-target profiles and adverse outcomes with systems toxicology. In addition, in [21], a multi-layer multi-target regression is proposed for the prediction of cognitive assessment from multiple neuroimaging biomarkers, allowing an early detection of Alzheimer's disease.…”

Section: Related Workmentioning

confidence: 99%

SOFIA: Selection of Medical Features by Induced Alterations in Numeric Labels

et al. 2020

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This work deals with the improvement of multi-target prediction models through a proposed optimization called Selection Of medical Features by Induced Alterations in numeric labels (SOFIA). This method performs a data transformation when: (1) weighting the features, (2) performing small perturbations on numeric labels and (3) selecting the features that are relevant in the trained multi-target prediction models. With the purpose of decreasing the computational cost in the SOFIA method, we consider those multi-objective optimization metaheuristics that support parallelization. In this sense, we propose an extension of the Natural Optimization (NO) approach for Simulated Annealing to support a multi-objective (MO) optimization. This proposed extension, called MONO, and some multiobjective evolutionary algorithms (MOEAs) are considered when performing the SOFIA method to improve prediction models in a multi-stage migraine treatment. This work also considers the adaptation of these metaheuristics to run on GPUs for accelerating the exploration of a larger space of solutions and improving results at the same time. The obtained results show that accuracies close to 88% are obtained with the MONO metaheuristic when employing eight threads and when running on a GPU. In addition, training times have been decreased from more than 8 h to less than 45 min when running the algorithms on a GPU. Besides, classification models trained with the SOFIA method only require 15 medical features or fewer to predict treatment responses. All in all, the methods proposed in this work remarkably improve the accuracy of multi-target prediction models for the OnabotulinumtoxinA (BoNT-A) treatment, while selecting those relevant features that allow us to know in advance the response to every stage of the treatment.

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Prediction of Chemical Multi-target Profiles and Adverse Outcomes with Systems Toxicology

Cited by 5 publications

References 0 publications

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Predicting Chemical Carcinogens Using a Hybrid Neural Network Deep Learning Method

Toxicoproteomics, from finding molecular targets to evaluating the impact on human health

SOFIA: Selection of Medical Features by Induced Alterations in Numeric Labels

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