Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Open Access

Peer-reviewed

Research Article

Kyle A. McQuisten,

Affiliation Department of Bioinformatics, Integrated DNA Technologies, Inc., Coralville, Iowa, United States of America
⨯
Andrew S. Peek

* E-mail: andrew.peek@roche.com

Current address: Department of Bioinformatics, Roche Molecular Systems, Inc., Pleasanton, California, United States of America

Affiliation Department of Bioinformatics, Integrated DNA Technologies, Inc., Coralville, Iowa, United States of America
⨯

x

Viewed

Powered by Altmetric

Questions or concerns about usage data? Please let us know.

Print

Small interfering RNA
Is the Subject Area "Small interfering RNA" applicable to this article?

Thanks for your feedback.
Machine learning
Is the Subject Area "Machine learning" applicable to this article?

Thanks for your feedback.
Support vector machines
Is the Subject Area "Support vector machines" applicable to this article?

Thanks for your feedback.
Forecasting
Is the Subject Area "Forecasting" applicable to this article?

Thanks for your feedback.
Artificial neural networks
Is the Subject Area "Artificial neural networks" applicable to this article?

Thanks for your feedback.
Analysis of variance
Is the Subject Area "Analysis of variance" applicable to this article?

Thanks for your feedback.
Statistical data
Is the Subject Area "Statistical data" applicable to this article?

Thanks for your feedback.
Linear regression analysis
Is the Subject Area "Linear regression analysis" applicable to this article?

Thanks for your feedback.