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Illustration of the CNN-LSTM architecture of the Tiberius model for gene structure classification at each base position. The HMM layer computes posterior probabilities or complete gene structures (Viterbi sequences). The model has approximately 8 million trainable parameters, and it was trained with sequences of length T = 9999 and a length of T = 500,004 was used for inference.
