Demographic Causes and Predictive Models of Population Fluctuations in Red Grouse

10.2307/4541 ◽  
1984 ◽  
Vol 53 (2) ◽  
pp. 639 ◽  
Author(s):  
Adam Watson ◽  
Robert Moss ◽  
Peter Rothery ◽  
Raymond Parr
Keyword(s):  
Predictive Models ◽  
Population Fluctuations ◽  
Red Grouse

Population Fluctuations in Red Grouse: Analysis of Bag Records and a Simulation Model

10.2307/4339 ◽  
1984 ◽  
Vol 53 (1) ◽  
pp. 21 ◽  
Author(s):  
G. R. Potts ◽  
S. C. Tapper ◽  
P. J. Hudson
Keyword(s):  
Simulation Model ◽  
Population Fluctuations ◽  
Red Grouse

Population Fluctuations in the Red Grouse Lagopus lagopus scoticus

10.2307/3017 ◽  
1967 ◽  
Vol 36 (1) ◽  
pp. 97 ◽  
Author(s):  
David Jenkins ◽  
Adam Watson ◽  
G. R. Miller
Keyword(s):  
Population Fluctuations ◽  
Red Grouse ◽  
Lagopus Lagopus ◽  
Lagopus Lagopus Scoticus

Kin Landownership, Differential Aggression between Kin and Non-Kin, and Population Fluctuations in Red Grouse

10.2307/5581 ◽  
1994 ◽  
Vol 63 (1) ◽  
pp. 39 ◽  
Author(s):  
A. Watson ◽  
R. Moss ◽  
R. Parr ◽  
M.D. Mountford ◽  
P. Rothery
Keyword(s):  
Population Fluctuations ◽  
Red Grouse

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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