Join Classifier of Type and Index Mutation on Lung Cancer DNA Using Sequential Labeling Model

Wisesty, Untari Novia; Purwarianti, Ayu; Pancoro, Adi; Chattopadhyay, Amrita; Phan, Nam Nhut; Chuang, Eric Y.; Mengko, Tati L. R.

doi:10.1109/access.2022.3142925

Cited by 5 publications

(6 citation statements)

References 45 publications

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“…The hyperparameters for the TCN model included number of kernels 128, a kernel size of 16, a dilation factor of [ 1 , 2 , 4 , 8 , 16 , 32 ], a learning rate of 0.0005, and dropout rate of 0.1. The proposed model was also compared with the BiLSTM model that was previously used in the study [ 21 ]. The best hyperparameters for the BiLSTM model obtained in the study included integer mapping, a 2-layer BiLSTM, 256 LSTM units, a dropout rate of 0.2, and a learning rate of 0.0001.…”

Section: Resultsmentioning

confidence: 99%

“…Duplicate mutations are later combined with insertion mutations because they have an additional number of nucleotides, as in insertion mutation. Then, the data is processed by generating patient sequences (samples) based on mutation data that occurred in certain samples and corresponding reference sequences [ 21 ]. From the preprocessing process, a total of 81,272 samples were obtained with different types of genes and the number of mutations.…”

Section: Methodsmentioning

confidence: 99%

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Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

et al. 2023

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Early detection of breast cancer can be achieved through mutation detection in DNA sequences, which can be acquired through patient blood samples. Mutation detection can be performed using alignment and machine learning techniques. However, alignment techniques require reference sequences, and machine learning techniques still cannot predict index mutation and require supporting tools. Therefore, in this research, a Temporal Convolutional Network (TCN) model was proposed to detect the type and index mutation faster and without reference sequences and supporting tools. The architecture of the proposed TCN model is specifically designed for sequential labeling tasks on DNA sequence data. This allows for the detection of the mutation type of each nucleotide in the sequence, and if the nucleotide has a mutation, the index mutation can be obtained. The proposed model also uses 2-mers and 3-mers mapping techniques to improve detection performance. Based on the tests that have been carried out, the proposed TCN model can achieve the highest F1-score of 0.9443 for COSMIC dataset and 0.9629 for RSCM dataset, Additionally, the proposed TCN model can detect index mutation six times faster than BiLSTM model. Furthermore, the proposed model can detect type and index mutations based on the patient’s DNA sequence, without the need for reference sequences or other additional tools.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

et al. 2023

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“…Using a 1-D convolutional neural network (1D-CNN), a Bidirectional Long Short-Term Memory (BiLSTM), and Bidirectional Gated Recurrent Unit (Bi-GRU), UntariNoviaWisestyetal.presented the sequential labeling model in 2022 as a method to concurrently identify type and index alterations of DNA sequences. The suggested model uses Bi-GRU and BiLSTM to report F1 scores of 0.9596 [15].…”

Section: Related Workmentioning

confidence: 99%

A New Technique for Genetic Mutations Detection and Classification Using Deep Learning

2023

IJIES

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Numerous variables, such as changes in the environment, toxins, spontaneous mutations, and replication mistakes, can have an impact on DNA. It is known as a gene mutation when this occurrence permanently alters the DNA sequence that forms a gene, changing it from the sequence seen in most people. Alleles, which are small variations within the same gene, are produced as a result of mutations. Each person is different due to these minute variations in their DNA sequence. In particular, some mutations affect just the carriers, whilst others affect both all children and the carrier organism's progeny. Changes in the DNA sequence (genetic mutations) are one of the reasons that lead to life-threatening disorders such as cancer and other diseases, so it has become necessary to detect these mutations early and know their types and their impact on the DNA sequence The DNA modifications developing in the cells of the following generation are what bioinformatics is most concerned about. In this paper, an efficient approach based on the architecture of long-term memory (LSTM) recurrent neural networks is presented. The method involves locating mutations using the Needleman algorithm that compares the reference DNA sequence with the mutant sequence. The deep neural network is then trained on the Cancer Cell Lines portal (CCLE) dataset to classify the different types of genetic mutations that have been identified. The algorithm reports the mutation type and locus for each pair of DNA inputs. Finally, the simulation output demonstrates the effectiveness of the algorithm. By testing the method, it was found that it was able to identify the mutation type with 100% accuracy.

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“…This implies that 3-mer provides better representation for sequential labelling purposes. However, it is possible that model and training configuration mentioned in [27] does not suit the sequential labelling task for predicting splice sites. Based on validation performance, Baseline Kmer is selected for testing.…”

Section: A Training and Validationmentioning

confidence: 99%

“…This research uses the RNN model proposed to detect type and mutation index in human DNA [27] as a baseline model. The report indicates that both BiLSTM and BiGRU produce similar results.…”

Section: A Datamentioning

confidence: 99%

Sequential Labelling and DNABERT For Splice Site Prediction in Homo Sapiens DNA

Leksono

Purwarianti

2023

Preprint

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Background Gene prediction on DNA has been conducted using various deep learning architectures to discover splice sites to locate intron and exon regions. However, recent predictions are carried out with models trained with a sequence which has a splice site in the middle. This case eliminates the possibility of multiple splice sites in a single sequence. Results This research proposes a sequential labelling model to predict splice sites regardless of their position in a sequence. A sequential labelling model named DNABERT-SL is developed on pre-trained DNABERT-3. DNABERT-SL is benchmarked against the latest sequential labelling model for mutation type and location prediction based on BiLSTM and BiGRU. While achieving F1 scores above 0.8 on validation data, BiLSTM, BiGRU, and DNABERT-SL perform poorly on test data as indicated by their respective low F1 scores (0.498 ± 0.184, 0.6 ± 0.123, 0.532 ± 0.245). Conclusions DNABERT-SL model cannot distinguish nucleotides acting as splice sites from normal ones. Principal component analysis on token contextual representation produced by DNABERT-SL shows that the representation is not optimal for distinguishing splice site tokens from non-splice site tokens. Splice site motif observation conducted on test and training sequences shows that an arbitrary sequence with GT-AG motif can be both splice sites in some sequences and normal nucleotides in others.

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Join Classifier of Type and Index Mutation on Lung Cancer DNA Using Sequential Labeling Model

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 5 publications

References 45 publications

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

Temporal convolutional network for a Fast DNA mutation detection in breast cancer data

A New Technique for Genetic Mutations Detection and Classification Using Deep Learning

Sequential Labelling and DNABERT For Splice Site Prediction in Homo Sapiens DNA

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