# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 3 Results
### Ablation Experiments and Reliability Analysis  
To verify the design superiority of the proposed model, two simplified models were compared with the proposed model using ablation experiments: our model (no GC feature) and our model (no attention). Figure 8A,B and Table 1 present the performance results on the SNP dataset. Further analysis showed that the use of different modules resulted in different performance improvements. The GC content increased the amount of input information for the existing model and was related to the sequencing depth. The attention mechanism extracted the effect of different regions of the input sequence on the sequencing depth. Based on performance comparisons, the model using the attention mechanism with GC content achieved the best prediction performance.  
As shown in Table 1, there are 70 k trainable parameters in the proposed sequencing depth prediction model, which is significantly less than the DLM (300 k in total), with the reduction in parameters mainly stemming from optimization of the number of hidden nodes and the number of network layers in the RNN module. The reduction in the number of parameters did not have a negative impact on prediction accuracy. Experiments conducted on publicly available data illustrate that the proposed model outperformed the DLM, as described in Figure 7. However, due to the large number of model parameters, to verify the robustness of the model and avoid overfitting phenomena or non-reproducibility being caused by too many parameters, several rounds of independent experiments were set up, verifying that the prediction results were highly consistent. The proposed model training process was repeated 15 times on the SNP dataset. Each round of training started with random parameters and stopped after 450 rounds. Figure 8C shows the comparison results of the log sequencing depth predictions from two independent prediction rounds. As presented in Figure 8D, [PERSON]'s \(r\) values for all 105 pairwise comparisons exceeded 0.970, which demonstrates that despite the difference in parameter initialization across independent experiments, the proposed model still generated relatively consistent prediction results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Parameter** & **Memory** & **Batch Size** & **RMSE** \\ \hline DLM & 300 k & 2369 MB & 999 & 0.301 \\ Our model & 70 k & 4055 MB & 2560 & 0.295 \\ Our model (no GC feature) & 70 k & 4027 MB & 2560 & 0.298 \\ Our model (no attention) & 68 k & 3953 MB & 2560 & 0.299 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the performance of different neural networks and the number of parameters based on the SNP dataset.  
Figure 7: Performance comparison of our model and the competing models on different datasets. (**A,D**) Comparison of our model with DLM and the linear model on the SNP dataset under the F2 err, F3 err, and RMSE. (**B,E**) Comparison of our model with DLM on the lncRNA dataset under the F2 err, F3 err, and RMSE. (**C,F**) Comparison of our model with DLM and the linear model on the SynDNA dataset under the F2 err, F3 err, and RMSE.