Comparative assessment of bivariate, multivariate and machine learning models for mapping flood proneness

2019 ◽  
Vol 100 (2) ◽  
pp. 461-491 ◽  
Author(s):  
Alaa M. Al-Abadi ◽  
Noor A. Al-Najar
Keyword(s):  
Machine Learning ◽  
Comparative Assessment ◽  
Learning Models ◽  
Machine Learning Models

A comparative assessment of gully erosion spatial predictive modeling using statistical and machine learning models

CATENA ◽  
2021 ◽  
Vol 207 ◽  
pp. 105679 ◽  
Author(s):  
Seyed Masoud Soleimanpour ◽  
Hamid Reza Pourghasemi ◽  
Maryam Zare
Keyword(s):  
Machine Learning ◽  
Predictive Modeling ◽  
Comparative Assessment ◽  
Gully Erosion ◽  
Learning Models ◽  
Machine Learning Models

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>

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