## 5 Discussion  
Translation site prediction is an important step for gene annotation. The accurate prediction of TISs in mRNA sequences facilitates understanding the gene regulating mechanism and plays an important role in studying the occurrence and development mechanisms of diseases. Many computational methods have been proposed to avoid expensive and time-consuming wet-lab experiments and achieve acceptable prediction accuracy by exploiting features such as consensus motifs around TISs, coding features around TISs, and the scanning model. However, how to fully exploit the aforementioned features so far is still an open problem. Most of the existing computational methods directly capture the above features from raw data, whereas it has been verified by a few works that it is more beneficial to design a series of more sophisticated submodels for feature extraction, e.g., NeuroTIS.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Coding Number**} & \multirow{2}{*}{**TIS Number**} & \multicolumn{2}{c}{**Time Cost (min)**} \\ \cline{3-4}  & & & **kmer+TCN** & **Frame-Specific CNN** \\ \hline Human & 9,545,915 & 32,780 & 20 & 0.8 \\ Mouse & 7,883,216 & 17,420 & 15 & 0.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Brief description of time cost on human and mouse datasets with regard to NeuroTIS+.  
This paper extends that idea and explores a method for TIS prediction by fully exploiting the primary structural information in mRNA sequences. Tests on transcriptome-wide human and mouse datasets demonstrate that our proposed method shows remarkable prediction performance.  
The superior performance of NeuroTIS+ compared to NeuroTIS can be primarily attributed to its comprehensive utilization of mRNA primary structural information in our implementation. First, regarding codon label dependency, NeuroTIS only realizes the process of message passing among three neighboring codon labels by using a skip connection in a BRNN, whereas NeuroTIS+ realizes the process of message passing among all the codon labels in a local region by using temporal convolution. Moreover, NeuroTIS+ has stronger expressive power by increasing the number of network layers and convolution kernels. Second, NeuroTIS+ considers the heterogeneity of negative TISs and built homogenous features for TISs by using an adaptive grouping strategy. Then, three frame-specific CNNs are trained for TIS prediction, leading to substantial enhancements in NeuroTIS's predictive performance.  
Although demonstrating enhanced predictive capability, NeuroTIS+ presents certain constraints. First, the generation of global features necessitates a full-length mRNA sequence. Fortunately, the availability of full-length mRNA sequences is increasing. Second, it only considers canonical downstream TISs, while numerous mRNAs contain alternative TISs that exhibit short CDSs, so NeuroTIS+ primarily trained on long CDSs may struggle to detect reliably. Despite this fact, NeuroTIS+ could provide indirect support for alternative TIS discovery through precise CDS boundary delineation. Third, NeuroTIS+ specializes in canonical TISs for its biological prevalence. However, considerable non-canonical TISs exist (e.g., CUG, AUU, and GUG). Fourth, its assumption of a directed TIS-CDS dependency enables efficient inference, but cyclic dependencies might offer additional benefits. Future studies on these points are warranted.