Assessment of Methods for Predicting Eukaryotic Promoter Sequences

Identifying promoters is challenging due to their short sequences, low conservation, and complex regulation. His-torically, this was done through slow and expensive experimental methods. Efficient pattern recognition and statistical approaches have revolutionized this process, offering a faster and more cost-effective solution. Accurate promoter identification is vital for experimental biologists and biotech applications, enabling precise gene expression regulation. This document evaluates tra-ditional machine learning methods (SVM, MLP, LDA, PSFN and denseNet) for promoter recognition, confirming their suitability. Our methodology consisted of using a partition of 80 to 20 percent for data training and analysis, respectively. The former was used to optimize the parameters at a k-fold with k = 10 by cross-validation; after optimization, the parameters were used to analyze the 20 percent of data. The F1 metric score is a positive predictive value that calculates the precision and sensitivity. Therefore, our findings align with [2] as the F1 metric score was above 85 % in SVM and both PSFN methods, affirming them as the most reliable options for promoter prediction. The objective was to find a method that accurately validates the prediction of promoters and non-promoters by a comparison of methods. To achieve this, we incorporated sequences from humans, which are validated with these characteristics, to apply this workflow to sequences from other organisms in the future.