Background: In sequencing human and model organism DNA, the development of efficient
computational techniques for the rapid prediction of short exons in eukaryotes is a major challenge.
Objective: This paper presents a multiscale products-based method in B-spline wavelet domain for short
exon detection. In our analysis, we find out the wavelet coefficients associated to introns are less
correlated between consecutive scales than coefficients related to exons. We reveal the explanation of
this investigation which results from the HMR195 dataset by calculating the histogram distributions of
the exon and intron coefficients. We employ these inter-scale correlation features to enhance exon
structures and weak background noise.
Method: The development of our method is outlined at two stages: (i) A new B-spline wavelet
transform is designed to extract the exon features in multiscale domain; so, setting the window length
parameter which affects the results is avoided, and this wavelet has higher degree of freedom for curve
design. (ii) Based on the significant difference of correlated features between the exon and intron
coefficients, we present a multiscale products-based method to discriminate significant exon features
Results: The BG570 and HMR195 datasets have been used in the evaluation of considered methods. By
comparison with eight other existing techniques, the detection results show that: the proposed method
reveals at least improvement of 26.8%, 9.5%, 8.2%, 3.5%, 10.2%, 4.5%, 7.8% and 6.4% on the exons
length of 0-24, 25-49, 50-74, 100-124, 125-149, 150-174, 175-199 and 200-299, respectively.
Conclusion: Experimental results demonstrate that our approach leads to better performance for short