Predicting crop root concentration factors of organic contaminants with machine learning models

2021 ◽  
pp. 127437
Author(s):  
Feng Gao ◽  
Yike Shen ◽  
J. Brett Sallach ◽  
Hui Li ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Contaminants ◽  
Learning Models ◽  
Crop Root ◽  
Concentration Factors ◽  
Machine Learning Models

scholarly journals Direct prediction of bioaccumulation of organic contaminants in plant roots from soils with machine learning models based on molecular structures

2021 ◽  
Author(s):  
Feng Gao ◽  
Yike Shen ◽  
J. Brett Sallach ◽  
Hui Li ◽  
Cun Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Contaminants ◽  
Plant Root ◽  
Ecological Impacts ◽  
Molecular Structures ◽  
Gradient Boosting ◽  
Learning Models ◽  
Root Interactions ◽  
Chemical Soil ◽  
Machine Learning Models

Abstract Root concentration factor is an important substance-specific characterization parameter for plant uptake of organic contaminants from soils in life cycle impact assessment (LCIA); however, the availability of a reliable dataset and building of robust predictive models remain challenging due to the complexity of chemical-soil-plant root interactions. Here we developed end-to-end machine learning models to devolve the interaction complexity by training on a unified dataset with 341 data points covering 72 chemicals. The gradient boosting regression tree (GBRT) model based on the extended connectivity fingerprints (ECFP) demonstrated a superior prediction performance with R-squared of 0.77 and Mean Absolute Error (MAE) of 0.22. In addition, partial dependence analysis was used to determine the nonlinear relationships in the chemical-soil-plant root system. Feature importance analysis revealed the relationship between and chemical topological structures. Stemming from its simplicity and universality, the GBRT-ECFP model provides a promising tool for LCIA to better characterize the human and ecological impacts of chemicals in the environment.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

Utilizing Blockchain Technology in Social Media Bot Identification

2020 ◽  
Author(s):  
Shreya Reddy ◽  
Lisa Ewen ◽  
Pankti Patel ◽  
Prerak Patel ◽  
Ankit Kundal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Gold Standard ◽  
The Internet ◽  
Learning Models ◽  
Current Time ◽  
Machine Learning Methods ◽  
Blockchain Technology ◽  
Modern Age ◽  
Machine Learning Models

<p>As bots become more prevalent and smarter in the modern age of the internet, it becomes ever more important that they be identified and removed. Recent research has dictated that machine learning methods are accurate and the gold standard of bot identification on social media. Unfortunately, machine learning models do not come without their negative aspects such as lengthy training times, difficult feature selection, and overwhelming pre-processing tasks. To overcome these difficulties, we are proposing a blockchain framework for bot identification. At the current time, it is unknown how this method will perform, but it serves to prove the existence of an overwhelming gap of research under this area.<i></i></p>

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