Clearly, while the museum continues to be a site for the production of evolutionary information and dissemination of that information through exhibition, it is also a site for increasingly important critical research into evolutionary education.
The use of evolutionary information for input encoding has been shown to improve classifier performance.
The use of evolutionary information for classifier construction results in a large number of input variables.
Interestingly, the most accurate classifier was obtained by combining PSSM with the new descriptors of evolutionary information for input encoding.
Interestingly, the best classifiers were obtained by combining the new descriptors and PSSM, suggesting that they captured different aspects of evolutionary information for DNA and RNA-binding site prediction.
This study aimed to examine different descriptors of evolutionary information for DNA and RNA-binding site prediction, and to improve classifier performance by combining relevant sequence features.
The results obtained in this study indicate that DNA-binding site prediction can be significantly improved by using the RF-based approach with biochemical features and several new descriptors of evolutionary information for input encoding.
Since random forests can handle a large number of input variables and avoid model overfitting, accurate classifiers have been constructed by combining biochemical features with several descriptors of evolutionary information for input encoding.
There are always tradeoffs between incorporating more features into models and avoiding over-parameterization for more accurate captures of evolutionary information [ 19- 21].
However, RFs have the major advantage in handling a large number of input variables should various descriptors of evolutionary information be used for input encoding.
