A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application
Abstract Background Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-03-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-023-05235-x |
| _version_ | 1797859796745977856 |
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| author | Mpho Mokoatle Vukosi Marivate Darlington Mapiye Riana Bornman Vanessa. M. Hayes |
| author_facet | Mpho Mokoatle Vukosi Marivate Darlington Mapiye Riana Bornman Vanessa. M. Hayes |
| author_sort | Mpho Mokoatle |
| collection | DOAJ |
| description | Abstract Background Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer. Methods In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings. Results The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE’s sentence transformer only marginally improved the performance of machine learning models. |
| first_indexed | 2024-04-09T21:35:24Z |
| format | Article |
| id | doaj.art-07b74d23f28a472db760ff1b38bfeeac |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-09T21:35:24Z |
| publishDate | 2023-03-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-07b74d23f28a472db760ff1b38bfeeac2023-03-26T11:18:44ZengBMCBMC Bioinformatics1471-21052023-03-0124112510.1186/s12859-023-05235-xA review and comparative study of cancer detection using machine learning: SBERT and SimCSE applicationMpho Mokoatle0Vukosi Marivate1Darlington Mapiye2Riana Bornman3Vanessa. M. Hayes4Department of Computer Science, University of PretoriaDepartment of Computer Science, University of PretoriaCapeBio TM TechnologiesSchool of Health Systems and Public Health, University of PretoriaSchool of Medical Sciences, The University of SydneyAbstract Background Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer. Methods In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings. Results The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE’s sentence transformer only marginally improved the performance of machine learning models.https://doi.org/10.1186/s12859-023-05235-xCancer detectionDNAMachine learningSentenceBertSimCSE |
| spellingShingle | Mpho Mokoatle Vukosi Marivate Darlington Mapiye Riana Bornman Vanessa. M. Hayes A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application BMC Bioinformatics Cancer detection DNA Machine learning SentenceBert SimCSE |
| title | A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application |
| title_full | A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application |
| title_fullStr | A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application |
| title_full_unstemmed | A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application |
| title_short | A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application |
| title_sort | review and comparative study of cancer detection using machine learning sbert and simcse application |
| topic | Cancer detection DNA Machine learning SentenceBert SimCSE |
| url | https://doi.org/10.1186/s12859-023-05235-x |
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