The sequential labeling model is commonly used for time series or sequence data where each instance label is classified using the previous instance label. In this study, Dr. Adi Pancoro and his colleagues propose a new approach to detect type and index mutation namely join classifier using a sequential labeling model 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. Based on the experiments conducted using EGFR gene, BiLSTM and Bi-GRU displayed better performance and were more stable than 1D-CNN. The proposed model reports F1-scores of 0.9596, and 0.9612 using Bi-GRU and BiLSTM, respectively. Based on the results the model can successfully detect the type and index mutations in the DNA sequence more accurately and faster without the need for other supporting data and tools, and does not require re-alignment to reference sequences.

Article Citation:
Wisesty, U.N., Purwarianti, A., Pancoro, A., Chattopadhyay, A., Phan, N.N., Chuang, E.Y., Mengko, A.T. (2022). Join Classifier of Type and Index Mutation on Lung Cancer DNA Using Sequential Labeling Model. IEEE Access. Vol. 10, pp. 9004-9021.3. https://doi.org/10.1109/ACCESS.2022.3142925

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