Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach
Cancer prognostics using tumour transcriptomics is a promising precision medicine approach for helping decisions during cancer treatment. However, currently used cancer prognostic biomarkers still have low predictive power. This work tested the potential of applying machine learning (ML) algorithms...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Medical Sciences Forum |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-9992/22/1/6 |
| _version_ | 1797578507243487232 |
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| author | Ricardo Jorge Pais Filipa Lopes Inês Parreira Márcia Silva Mariana Silva Maria Guilhermina Moutinho |
| author_facet | Ricardo Jorge Pais Filipa Lopes Inês Parreira Márcia Silva Mariana Silva Maria Guilhermina Moutinho |
| author_sort | Ricardo Jorge Pais |
| collection | DOAJ |
| description | Cancer prognostics using tumour transcriptomics is a promising precision medicine approach for helping decisions during cancer treatment. However, currently used cancer prognostic biomarkers still have low predictive power. This work tested the potential of applying machine learning (ML) algorithms for generating patients’ survival prognostics on lung, breast, and kidney tumour transcriptomics datasets. We evaluated the performance of models generated by ML and reported their optimal sensitivity, specificity, accuracy, and computed ROC-AUC. The results support the potential for applying auto ML approaches for the future development of cancer prognostics tools based on transcriptomics data. |
| first_indexed | 2024-03-10T22:22:54Z |
| format | Article |
| id | doaj.art-140fb49158784d94a85fd898f1f97f49 |
| institution | Directory Open Access Journal |
| issn | 2673-9992 |
| language | English |
| last_indexed | 2024-03-10T22:22:54Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Medical Sciences Forum |
| spelling | doaj.art-140fb49158784d94a85fd898f1f97f492023-11-19T12:12:30ZengMDPI AGMedical Sciences Forum2673-99922023-08-01221610.3390/msf2023022006Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning ApproachRicardo Jorge Pais0Filipa Lopes1Inês Parreira2Márcia Silva3Mariana Silva4Maria Guilhermina Moutinho5Bioenhancer Systems, Office 63 182-184 High Street North, East Ham, London E6 2JA, UKEgas Moniz School of Health & Science, 2929-511 Almada, PortugalEgas Moniz School of Health & Science, 2929-511 Almada, PortugalEgas Moniz School of Health & Science, 2929-511 Almada, PortugalEgas Moniz School of Health & Science, 2929-511 Almada, PortugalEgas Moniz Center for Interdisciplinary Research, Egas Moniz School of Health & Science, 2829-511 Almada, PortugalCancer prognostics using tumour transcriptomics is a promising precision medicine approach for helping decisions during cancer treatment. However, currently used cancer prognostic biomarkers still have low predictive power. This work tested the potential of applying machine learning (ML) algorithms for generating patients’ survival prognostics on lung, breast, and kidney tumour transcriptomics datasets. We evaluated the performance of models generated by ML and reported their optimal sensitivity, specificity, accuracy, and computed ROC-AUC. The results support the potential for applying auto ML approaches for the future development of cancer prognostics tools based on transcriptomics data.https://www.mdpi.com/2673-9992/22/1/6bioinformaticscancer prognosticsmachine learningtranscriptomics |
| spellingShingle | Ricardo Jorge Pais Filipa Lopes Inês Parreira Márcia Silva Mariana Silva Maria Guilhermina Moutinho Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach Medical Sciences Forum bioinformatics cancer prognostics machine learning transcriptomics |
| title | Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach |
| title_full | Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach |
| title_fullStr | Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach |
| title_full_unstemmed | Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach |
| title_short | Predicting Cancer Prognostics from Tumour Transcriptomics Using an Auto Machine Learning Approach |
| title_sort | predicting cancer prognostics from tumour transcriptomics using an auto machine learning approach |
| topic | bioinformatics cancer prognostics machine learning transcriptomics |
| url | https://www.mdpi.com/2673-9992/22/1/6 |
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