Identification of informative features for predicting proinflammatory potentials of engine exhausts

A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example

Data scarcity is one of the most critical issues impeding the development of prediction models for chemical effects. Multitask learning algorithms leveraging knowledge from relevant tasks showed potential for dealing with tasks with limited data. However, current multitask methods mainly focus on learning from datasets whose task labels are available for most of the training samples. Since datasets were generated for different purposes with distinct chemical spaces, the conventional multitask learning methods may not be suitable. This study presents a novel multitask learning method MTForestNet that can deal with data scarcity problems and learn from tasks with distinct chemical space. The MTForestNet consists of nodes of random forest classifiers organized in the form of a progressive network, where each node represents a random forest model learned from a specific task. To demonstrate the effectiveness of the MTForestNet, 48 zebrafish toxicity datasets were collected and utilized as an example. Among them, two tasks are very different from other tasks with only 1.3% common chemicals shared with other tasks. In an independent test, MTForestNet with a high area under the receiver operating characteristic curve (AUC) value of 0.911 provided superior performance over compared single-task and multitask methods. The overall toxicity derived from the developed models of zebrafish toxicity is well correlated with the experimentally determined overall toxicity. In addition, the outputs from the developed models of zebrafish toxicity can be utilized as features to boost the prediction of developmental toxicity. The developed models are effective for predicting zebrafish toxicity and the proposed MTForestNet is expected to be useful for tasks with distinct chemical space that can be applied in other tasks.Scieific contributionA novel multitask learning algorithm MTForestNet was proposed to address the challenges of developing models using datasets with distinct chemical space that is a common issue of cheminformatics tasks. As an example, zebrafish toxicity prediction models were developed using the proposed MTForestNet which provide superior performance over conventional single-task and multitask learning methods. In addition, the developed zebrafish toxicity prediction models can reduce animal testing.

Ensemble learning for predicting ex vivo human placental barrier permeability

Background

The placental barrier protects the fetus from exposure to some toxicants and is vital for drug development and risk assessment of environmental chemicals. However, in vivo experiments for assessing the placental barrier permeability of chemicals is not ethically acceptable. Although ex vivo placental perfusion methods provide good alternatives for the assessment of placental barrier permeability, the application to a large number of test chemicals could be time- and resource-consuming. Computational prediction models for ex vivo placental barrier permeability are therefore desirable.

Methods

A total of 87 chemicals and corresponding 1444 physicochemical properties were divided into training and test datasets. Three types of algorithms including linear regression, random forest, and ensemble models were applied to develop prediction models for ex vivo placental barrier permeability.

Results

Among the tested models, the ensemble model integrating the previous two methods performed best for predicting ex vivo human placental barrier permeability with correlation coefficients of 0.887 and 0.825 when considering the applicability domain. An additional test on seven newly curated chemicals from the literature showed a good correlation coefficient of 0.879 which was further improved to 0.921 by considering the variation of experiments.

Conclusion

In this study, the first valid predicting model for ex vivo human placental barrier permeability was developed following the OECD guideline. The model is expected to be useful for assessing the human placental barrier permeability and can be integrated with developmental toxicity prediction models for investigating the toxic effects of chemicals on the fetus.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04937-y.

Assessment of the temporal variability and health risk of atmospheric particle-phase polycyclic aromatic hydrocarbons in a northeastern city in China

In this study, we examined the sources and temporal variability of 16 polycyclic aromatic hydrocarbons (PAHs) found in fine particulate matter (PM_2.5) in a typical industrial city in northern China. We also evaluated the incremental lifetime cancer risk (ILCR) from the inhalation of these PAHs. Atmospheric PM_2.5 samples were collected for 7 consecutive days each month from 2014 to 2019, and the 16 PAHs were measured using multiplex gas chromatography-tandem mass spectrometry. The carcinogenic risk of PAH exposure was assessed using the inhalation unit risk (IUR) and cancer slope factor (CSF) methods. The annual average concentrations of PM_2.5 for each year from 2014 to 2019 were 102.87±55.25, 86.92±60.43, 69.17±37.74, 58.20±59.15, 56.01±34.52, and 52.54±58.15 µg m^-3, and the annual average ΣPAH concentrations were 56.03±81.09, 47.99±79.30, 40.41±57.31, 33.57±51.79, 43.23±74.80, and 25.20±50.91 ng m^-3, respectively. Source identification, using diagnostic ratio analysis, indicated that the major PAH sources were coal/biomass combustion, fuel combustion, and traffic emissions. A health risk assessment showed that the ILCR from PAH inhalation decreased throughout the study period and varied with age. The IUR and CSF methods both showed that the adult ILCR exceeded 1.0×10^-6. These findings demonstrate the importance of addressing the carcinogenic risk of PM_2.5-bound PAHs, particularly in adults.