AI Medical Compendium Journal:
Journal of hazardous materials

Showing 71 to 80 of 97 articles

Implementing comprehensive machine learning models of multispecies toxicity assessment to improve regulation of organic compounds.

Journal of hazardous materials
Machine learning has made significant progress in assessing the risk associated with hazardous chemicals. However, most models were constructed by randomly selecting one algorithm and one toxicity endpoint towards single species, which may cause bias...

Machine learning-based approach for efficient prediction of toxicity of chemical gases using feature selection.

Journal of hazardous materials
Toxic gases can be fatal as they damage many living tissues, especially the nervous and respiratory systems. They can cause permanent damage for many years by harming environmental tissue and living organisms. They can also cause mass deaths when use...

Deep learning for asbestos counting.

Journal of hazardous materials
The PCM (phase contrast microscopy) method for asbestos counting needs special sample treatments, hence it is time consuming and rather expensive. As an alternative, we implemented a deep learning procedure on images directly acquired from the untrea...

Machine learning-assisted data filtering and QSAR models for prediction of chemical acute toxicity on rat and mouse.

Journal of hazardous materials
Machine learning (ML) methods provide a new opportunity to build quantitative structure-activity relationship (QSAR) models for predicting chemicals' toxicity based on large toxicity data sets, but they are limited in insufficient model robustness du...

Machine learning in the identification, prediction and exploration of environmental toxicology: Challenges and perspectives.

Journal of hazardous materials
Over the past few decades, data-driven machine learning (ML) has distinguished itself from hypothesis-driven studies and has recently received much attention in environmental toxicology. However, the use of ML in environmental toxicology remains in t...

Deep learning approach identified a gene signature predictive of the severity of renal damage caused by chronic cadmium accumulation.

Journal of hazardous materials
Epidemiology studies have indicated that environmental cadmium exposure, even at low levels, will result in chronic cadmium accumulation in the kidney with profound adverse consequences and that the diabetic population is more susceptible. However, t...

Deep learning model based on urban multi-source data for predicting heavy metals (Cu, Zn, Ni, Cr) in industrial sewer networks.

Journal of hazardous materials
The high concentrations of heavy metals in municipal industrial sewer networks will seriously impact the microorganisms of the activated sludge in the wastewater treatment plant (WWTP), thus deteriorating the effluent quality and destroying the stabi...

TrendProbe: Time profile analysis of emerging contaminants by LC-HRMS non-target screening and deep learning convolutional neural network.

Journal of hazardous materials
Peak prioritization is one of the key steps in non-target screening of environmental samples to direct the identification efforts to relevant and important features. Occurrence of chemicals is sometimes a function of time and their presence in consec...

Structure analysis and non-invasive detection of cadmium-phytochelatin2 complexes in plant by deep learning Raman spectrum.

Journal of hazardous materials
Plants synthesize phytochelatins to chelate in vivo toxic heavy metal ions and produce nontoxic complexes for tolerating the stress. Detection of the complexes would simplify the identification of high phytoremediation cultivars, as well as assessmen...

Machine learning models on chemical inhibitors of mitochondrial electron transport chain.

Journal of hazardous materials
Chemicals can induce adverse effects in humans by inhibiting mitochondrial electron transport chain (ETC) such as disrupting mitochondrial membrane potential, enhancing oxidative stress and causing some diseases. Thus, identifying ETC inhibitors (ETC...