Erratum to: Predictive Models: Foundation, Types, and Development

<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>

Download Full-text

Faculty Opinions recommendation of Comparison of predictive models for hepatitis C co-infection among HIV patients in Cambodia.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.737547793.793572444 ◽

2020 ◽

Author(s):

Philip Rosenthal

Keyword(s):

Hepatitis C ◽

Predictive Models ◽

Hiv Patients

Download Full-text

Study of the route correction system for the on-board navigation system of an unmanned aerial vehicle on the grounds of radar images of the terrain

Automation. Modern Techologies ◽

10.36652/0869-4931-2020-74-3-129-134 ◽

2020 ◽

pp. 129-134

Keyword(s):

Predictive Model ◽

Unmanned Aerial Vehicle ◽

Predictive Models ◽

Navigation System ◽

Error Compensation ◽

Compensation Scheme ◽

Radar Images ◽

Aerial Vehicle ◽

System Errors ◽

Correction System

The system of route correction of an unmanned aerial vehicle (UAV) is considered. For the route correction the on-board radar complex is used. In conditions of active interference, it is impossible to use radar images for the route correction so it is proposed to use the on-board navigation system with algorithmic correction. An error compensation scheme of the navigation system in the output signal using the algorithm for constructing a predictive model of the system errors is applied. The predictive model is building using the genetic algorithm and the method of group accounting of arguments. The quality comparison of the algorithms for constructing predictive models is carried out using mathematical modeling.

Download Full-text