Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis

Bilgi, Eyüp; Karakus, Ceyda Oksel

doi:10.1007/s11051-023-05806-2

Cited by 4 publications

(3 citation statements)

References 43 publications

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“…For example, it has become possible to predict the cytotoxicity of silver nanoparticles, which are biosynthesized with anti-cancer and antibacterial activity [ 15 – 17 ]. Through a systematic review and statistical integration of silver nanoparticle cytotoxicity data, machine learning model training and development on these aggregated data pools can enhance the precision of risk prediction and avoid over- or underestimation of the actual risk of human exposure to nanotoxicity [ 18 ]. Although there is potential to guide precision therapies, improve efficiency, and achieve better outcomes, limited progress has been made to deal with decision-making in the clinical context.…”

Section: Introductionmentioning

confidence: 99%

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Qiao,

Chen,

Qian

et al. 2024

J Transl Med

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Clinical data mining of predictive models offers significant advantages for re-evaluating and leveraging large amounts of complex clinical real-world data and experimental comparison data for tasks such as risk stratification, diagnosis, classification, and survival prediction. However, its translational application is still limited. One challenge is that the proposed clinical requirements and data mining are not synchronized. Additionally, the exotic predictions of data mining are difficult to apply directly in local medical institutions. Hence, it is necessary to incisively review the translational application of clinical data mining, providing an analytical workflow for developing and validating prediction models to ensure the scientific validity of analytic workflows in response to clinical questions. This review systematically revisits the purpose, process, and principles of clinical data mining and discusses the key causes contributing to the detachment from practice and the misuse of model verification in developing predictive models for research. Based on this, we propose a niche-targeting framework of four principles: Clinical Contextual, Subgroup-Oriented, Confounder- and False Positive-Controlled (CSCF), to provide guidance for clinical data mining prior to the model's development in clinical settings. Eventually, it is hoped that this review can help guide future research and develop personalized predictive models to achieve the goal of discovering subgroups with varied remedial benefits or risks and ensuring that precision medicine can deliver its full potential.

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Section: Introductionmentioning

confidence: 99%

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Qiao,

Chen,

Qian

et al. 2024

J Transl Med

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“…Afshin Saadat et al studied machinelearning techniques to predict the antibacterial activity of AgNPs against Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Klebsiella pneumonia [39]. Eyup Bilgi et al employed two different machine learning approaches namely decision tree (DT) and artificial neural network (ANN) to predict the cytotoxic potential of nanosilver based on material and assay-related parameters [40]. Anjana S Desai et al utilized the Decision tree and Random Forest models to comprehend the relationship between the physical parameters of silver nanoparticles and their cytotoxicity [41].…”

Section: Introductionmentioning

confidence: 99%

A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles

Mary,

Mujeeb

2024

Nano Ex.

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The biocompatibility property has made silver nanoparticles powerful candidates for various nanomedical applications. Research interest in silver nanoparticles as a viable alternative to antibiotics is gaining more attention due to their enhanced antimicrobial activity, better antibacterial activity and low cytotoxicity. Machine Learning (ML) has become a state-of-the-art analytic and modelling tool in recent times, due to its prediction capabilities and increased accuracy of the results. In this work, we present machine-learning techniques to predict the antibacterial capacity of silver nanoparticles and extended the work on antifungal studies. In the first phase, we reviewed 50 articles and collected data points for training the model, which consists of features such as core size, shape of the nanoparticle, dosage, bacteria/fungi species and zone of inhibition (ZOI). Then, we trained the data using eight different machine-learning regression algorithms and validated the models' performance using four metrics such as RMSE, MSE, MAE and R2. Furthermore, the importance of features used in the prediction models has been evaluated. The feature importance revealed that the core size of silver nanoparticles is the prominent feature in the prediction of the antibacterial capacity. The optimum model for the prediction of antibacterial and antifungal activity has been identified. Finally, the model’s validation has also been demonstrated. This work enables researchers to utilize Machine Learning which in turn can address the challenges of time consumption, and cost in laboratory experiments while minimising the reliance on trial and error.

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“…ML has been also used to model adverse outcome pathways, identify critical pathways through which NMs exert adverse effects, and explore clustering and pattern recognition to identify distinct toxicity pathways and correlations with physicochemical properties. , ML also has the potential to enable cross-species toxicity extrapolation, bridging knowledge gaps between different organisms and facilitating risk assessment for humans and other species . Last, feature selection and importance analysis can be employed to identify key descriptors influencing NM toxicity and enhance model interpretability . One way to sort out the descriptors of toxicity of NMs from a data set using ML is to identify the descriptors closely associated with biological endpoints and then determine the strength of association of the identified descriptors with the respective biological endpoint .…”

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confidence: 99%

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study

Scott-Fordsmand,

Gomes,

Pokhrel

et al. 2024

ACS Appl. Mater. Interfaces

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The wide variation of nanomaterial (NM) characters (size, shape, and properties) and the related impacts on living organisms make it virtually impossible to assess their safety; the need for modeling has been urged for long. We here investigate the custom-designed 1−10% Fe-doped CuO NM library. Effects were assessed using the soil ecotoxicology model Enchytraeus crypticus (Oligochaeta) in the standard 21 days plus its extension (49 days). Results showed that 10%Fe-CuO was the most toxic (21 days reproduction EC50 = 650 mg NM/kg soil) and Fe 3 O 4 NM was the least toxic (no effects up to 3200 mg NM/kg soil). All other NMs caused similar effects to E. crypticus (21 days reproduction EC50 ranging from 875 to 1923 mg NM/kg soil, with overlapping confidence intervals). Aiming to identify the key NM characteristics responsible for the toxicity, machine learning (ML) modeling was used to analyze the large data set [9 NMs, 68 descriptors, 6 concentrations, 2 exposure times (21 and 49 days), 2 endpoints (survival and reproduction)]. ML allowed us to separate experimental related parameters (e.g., zeta potential) from particle-specific descriptors (e.g., force vectors) for the best identification of important descriptors. We observed that concentration-dependent descriptors (environmental parameters, e.g., zeta potential) were the most important under standard test duration (21 day) but not for longer exposure (closer representation of real-world conditions). In the longer exposure (49 days), the particle-specific descriptors were more important than the concentration-dependent parameters. The longer-term exposure showed that the steepness of the concentration−response decreased with an increased Fe content in the NMs. Longer-term exposure should be a requirement in the hazard assessment of NMs in addition to the standard in OECD guidelines for chemicals. The progress toward ML analysis is desirable given its need for such large data sets and significant power to link NM descriptors to effects in animals. This is beyond the current univariate and concentration−response modeling analysis.

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Machine learning-assisted prediction of the toxicity of silver nanoparticles: a meta-analysis

Cited by 4 publications

References 43 publications

Clinical data mining: challenges, opportunities, and recommendations for translational applications

Clinical data mining: challenges, opportunities, and recommendations for translational applications

A machine learning framework for the prediction of antibacterial capacity of silver nanoparticles

Machine Learning Allowed Interpreting Toxicity of a Fe-Doped CuO NM Library Large Data Set─An Environmental In Vivo Case Study

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