Join Classifier of Type and Index Mutation on Lung Cancer DNA Using Sequential Labeling Model
Journal
IEEE Access
Journal Volume
10
Pages
9004-9021
Date Issued
2022
Author(s)
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 EGFR gene, BiLSTM and Bi-GRU displayed better performance and were more stable than 1D-CNN. Further tests were carried out on the TP53, KRAS, CTNNB1, SMARCA4, CDKN2A, PTPRD, BRAF, ERBB2, and PTPRT gene. 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. This will greatly facilitate the user in detecting type and index mutations faster by entering only the DNA sequence. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
Subjects
Bioinformatics
Data models
DNA
Hidden Markov models
Indexes
Labeling
Lung cancer
Brain
Convolution
Diseases
Genes
Long short-term memory
Bidirectional gated recurrent unit
Bidirectional long short-term memory
Convolutional neural network
Hidden-Markov models
Index
Labelings
Lung Cancer
Mutation detection
One dimensional convolutional neural network
One-dimensional
Sequential labeling
DNA sequences
SDGs
Type
journal article