Pharmacometabolomics with a combination of PLS‐DA and random forest algorithm analyses reveal meloxicam alters feline plasma metabolite profiles

2020 ◽  
Vol 43 (6) ◽  
pp. 591-601 ◽  
Author(s):  
Liam E. Broughton‐Neiswanger ◽  
Sol M. Rivera‐Velez ◽  
Martin A. Suarez ◽  
Jennifer E. Slovak ◽  
Julianne K. Hwang ◽  
...  
Keyword(s):  
Random Forest ◽  
Random Forest Algorithm ◽  
Metabolite Profiles ◽  
Plasma Metabolite

METHOD SUGGESTING CITY WALKING ROUTES FOR PEDESTRIANS USING AN EXAMPLE OF SAINT-PETERSBURG

2019 ◽  
pp. 71-76
Author(s):  
A.E. Semenov
Keyword(s):  
Random Forest ◽  
Develop Model ◽  
Random Forest Algorithm ◽  
Saint Petersburg ◽  
Pedestrian Navigation ◽  
Factors Influencing ◽  
The City

The method of pedestrian navigation in the cities illustrated by the example of Saint-Petersburg was investigated. The factors influencing people when they choose a route for their walk were determined. Based on acquired factors corresponding data was collected and used to develop model determining attractiveness of a street in the city using Random Forest algorithm. The results obtained shows that routes provided by the method are 14% more attractive and just 6% longer compared with the shortest ones.

Classification and photometric redshift estimation of quasars in photometric surveys

2020 ◽  
Vol 15 (S359) ◽  
pp. 40-41
Author(s):  
L. M. Izuti Nakazono ◽  
C. Mendes de Oliveira ◽  
N. S. T. Hirata ◽  
S. Jeram ◽  
A. Gonzalez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbour ◽  
Random Forest Algorithm ◽  
Photometric Redshift ◽  
Using Data

AbstractWe present a machine learning methodology to separate quasars from galaxies and stars using data from S-PLUS in the Stripe-82 region. In terms of quasar classification, we achieved 95.49% for precision and 95.26% for recall using a Random Forest algorithm. For photometric redshift estimation, we obtained a precision of 6% using k-Nearest Neighbour.

scholarly journals Random forest classification for predicting lifespan-extending chemical compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sofia Kapsiani ◽  
Brendan J. Howlin
Keyword(s):  
Caenorhabditis Elegans ◽  
Random Forest ◽  
Molecular Descriptors ◽  
Area Under The Curve ◽  
Chemical Compounds ◽  
Research Area ◽  
Importance Measure ◽  
Random Forest Algorithm ◽  
Molecular Fingerprints ◽  
Age Related

AbstractAgeing is a major risk factor for many conditions including cancer, cardiovascular and neurodegenerative diseases. Pharmaceutical interventions that slow down ageing and delay the onset of age-related diseases are a growing research area. The aim of this study was to build a machine learning model based on the data of the DrugAge database to predict whether a chemical compound will extend the lifespan of Caenorhabditis elegans. Five predictive models were built using the random forest algorithm with molecular fingerprints and/or molecular descriptors as features. The best performing classifier, built using molecular descriptors, achieved an area under the curve score (AUC) of 0.815 for classifying the compounds in the test set. The features of the model were ranked using the Gini importance measure of the random forest algorithm. The top 30 features included descriptors related to atom and bond counts, topological and partial charge properties. The model was applied to predict the class of compounds in an external database, consisting of 1738 small-molecules. The chemical compounds of the screening database with a predictive probability of ≥ 0.80 for increasing the lifespan of Caenorhabditis elegans were broadly separated into (1) flavonoids, (2) fatty acids and conjugates, and (3) organooxygen compounds.

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