Analysis of Machine Learning Techniques for Detection of Tumor Suppressor Genes for Early Detection of Cancer: A Systematic Literature Review

Din, Sami Ud; Shah, Muhammad Ayaz Farid; Shah, Asghar Ali

doi:10.1109/icic53490.2021.9691506

Cited by 1 publication

(2 citation statements)

References 51 publications

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“…Most of these studies uses MRI images for the detection of the gastric cancer. As shown in Table 1 , machine learning techniques have become widely used in recent years to provide timely identification models for efficient decision making [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ].…”

Section: Related Workmentioning

confidence: 99%

“…Most of these studies uses MRI images for the detection of the gastric cancer. As shown in Table 1, machine learning techniques have become widely used in recent years to provide timely identification models for efficient decision making [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Several studies have explored different machine learning techniques for the detection and classification of gastric cancer.…”

Section: Related Workmentioning

confidence: 99%

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A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer

Alotaibi

Khan

2023

Diagnostics

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Mutations in genes can alter their DNA patterns, and by recognizing these mutations, many carcinomas can be diagnosed in the progression stages. The human body contains many hidden and enigmatic features that humankind has not yet fully understood. A total of 7539 neoplasm cases were reported from 1 January 2021 to 31 December 2021. Of these, 3156 were seen in males (41.9%) and 4383 (58.1%) in female patients. Several machine learning and deep learning frameworks are already implemented to detect mutations, but these techniques lack generalized datasets and need to be optimized for better results. Deep learning-based neural networks provide the computational power to calculate the complex structures of gastric carcinoma-driven gene mutations. This study proposes deep learning approaches such as long and short-term memory, gated recurrent units and bi-LSTM to help in identifying the progression of gastric carcinoma in an optimized manner. This study includes 61 carcinogenic driver genes whose mutations can cause gastric cancer. The mutation information was downloaded from intOGen.org and normal gene sequences were downloaded from asia.ensembl.org, as explained in the data collection section. The proposed deep learning models are validated using the self-consistency test (SCT), 10-fold cross-validation test (FCVT), and independent set test (IST); the IST prediction metrics of accuracy, sensitivity, specificity, MCC and AUC of LSTM, Bi-LSTM, and GRU are 97.18%, 98.35%, 96.01%, 0.94, 0.98; 99.46%, 98.93%, 100%, 0.989, 1.00; 99.46%, 98.93%, 100%, 0.989 and 1.00, respectively.

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Section: Related Workmentioning

confidence: 99%

“…Most of these studies uses MRI images for the detection of the gastric cancer. As shown in Table 1, machine learning techniques have become widely used in recent years to provide timely identification models for efficient decision making [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. Several studies have explored different machine learning techniques for the detection and classification of gastric cancer.…”

Section: Related Workmentioning

confidence: 99%

A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer

Alotaibi

Khan

2023

Diagnostics

View full text Add to dashboard Cite

show abstract

Analysis of Machine Learning Techniques for Detection of Tumor Suppressor Genes for Early Detection of Cancer: A Systematic Literature Review

Cited by 1 publication

References 51 publications

A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer

A Framework for Prediction of Oncogenomic Progression Aiding Personalized Treatment of Gastric Cancer

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