The sequential labeling model is commonly used for time series or sequence data where each instance label is classified using previous instance label. In this work, a sequential labeling model… Click to show full abstract
The sequential labeling model is commonly used for time series or sequence data where each instance label is classified using previous instance label. In this work, a sequential labeling model is proposed as a new approach to detect the type and index mutations simultaneously, using DNA sequences from lung cancer study cases. The methods used are One Dimensional Convolutional Neural Network (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU). Each nucleotide in the patient’s DNA sequence is classified as either normal or with a certain type of mutation in which case, its index mutation is predicted. The mutation types detected are either substitution, insertion, deletion, or delins (deletion insertion) mutations. Based on the experiments that were conducted using
               
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