# A Low-Complexity Deep Learning Model for Predicting Targeted Sequencing Depth from Probe Sequence
## 3 Results
### Parameter Optimization  
The model parameters were trained using a 20-fold cross-validation method to comprehensively assess the proposed model. The dataset was divided into 20 folds, with 19 folds serving as the training set and 1 fold serving as the prediction set. This process was repeated 20 times, and the results were aggregated after 20 rounds of cross-validated predictions to obtain the prediction accuracy of our network.  
To acquire the best-distributed representation of the sequences, the performance of the proposed model was tested with different step size (\(s\)) and \(k\) values. As depicted in Figure 5A, the performance of the model was the best when \(k\) was 3. This result can be attributed to the fact that in biology, an amino acid comprises three bases, and the process of segmenting a sequence into word vectors with \(k=3\) shares a similar characteristic with this phenomenon. Then, \(k\) was set to 3, and the effect of different values of \(s\) on performance was tested. As Figure 5B indicates, the model's performance deteriorated as the step size increased. Longer step lengths could lead to a loss of sequence information. This might result in a degradation of prediction performance. Therefore, the values of \(k\) and \(s\) were set to 3 and 1, respectively, during the following experiments on model performance calculation. On an Intel(R) Xeon Gold 5220R CPU with a maximum frequency of 2.2 GHz, the CPU times required to generate sequence representation using DLM and the proposed model were compared. Based on the SNP dataset, the process of generating three-mer sequence representation took only 4 min of CPU time, significantly reducing the transformation processing time from sequence to distributed representation compared to the DLM (334 min of CPU time).  
The proposed model was completed with the PyTorch framework on an NVIDIA A6000 GPU server [33]. The model was trained with a batch size of 2560, and the initial learning rate was set to 0.0001. During the training process, RMSE was utilized to calculate the loss value between the predicted results and actual labels, and the adaptive moment estimation (Adam) algorithm was used to optimize the gradient descent [34]. The training process of this model is illustrated in Figure 5C. By observing the change in the loss value with the number of iterations, it was found that the loss value of the validation set started to increase at 450 rounds or more, indicating that the overfitting phenomenon had started to occur; therefore, the number of iterations was set to 450. Figure 5D,E illustrates the process of setting the number of hidden nodes and layers in a Bi-LSTM network, and the two-layer Bi-LSTM network with 32 hidden nodes is the optimal model. Setting more hidden nodes and deeper layers in the Bi-LSTM could lead to overfitting of the model, while a one-layer network or a smaller number of hidden nodes could lead to a lack of  
Figure 4: Visualization of features extracted by dna2 vec and one-hot for three-mer word vectors using \(t\)-SNE. (**A**) The word vector clustering result for three-mer encoding by dna2 vec. The different colors represent the different nucleotide species in the central position, where “*” represents nucleotide. (**B**) The clustering result of word vector encoding by one-hot for three-mer encoding.  
information acquisition. Compared to the four three-layer GRU networks with 128 hidden nodes used in the DLM, the number of parameters was reduced in the proposed sequencing depth prediction model because the sequence representation obtained through the _k_-mer and dna2 vec in the sequence-encoding module contained more sequence information.