LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification
Breast cancer is the most common cancer type among females and is one of the leading causes of death worldwide. Being a heterogeneous disease, subtyping breast cancer plays a vital role in its treatment. In this regard, gene expression plays an important role. Thus, in this work gene expression data...
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IEEE
2025-01-01
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| author | Nimisha Ghosh Sankar Kumar Mridha Rourab Paul |
| author_facet | Nimisha Ghosh Sankar Kumar Mridha Rourab Paul |
| author_sort | Nimisha Ghosh |
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| description | Breast cancer is the most common cancer type among females and is one of the leading causes of death worldwide. Being a heterogeneous disease, subtyping breast cancer plays a vital role in its treatment. In this regard, gene expression plays an important role. Thus, in this work gene expression data is used to identify the most significant gene biomarkers. The identified biomarkers are highly associated with each breast cancer subtype such as Luminal A, Luminal B, HER2-Enriched and Basal-Like. To identify such biomarkers, initially LASSO in association with four machine learning models such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and Naive Bayes (NB) are applied on the dataset to find the initial reduced set of genes as well as the best learning model based on classification accuracy; SVM in this case. Thereafter, Modified Compact Genetic Algorithm (mCGA) is performed to identify the final set of genes as biomarkers for each specific subtype. Experimental results suggest that our proposed method assesses AUC-ROC values of 0.9878 and 0.97311 for LumA and LumB and 1 for Basal and HER2 subtypes. To validate the biological significance of the identified biomarkers, KEGG pathway and GO enrichment analysis are carried out. |
| format | Article |
| id | doaj-art-5e557918f468452db4b369487cfd6795 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-5e557918f468452db4b369487cfd67952025-01-31T00:01:23ZengIEEEIEEE Access2169-35362025-01-0113176731768210.1109/ACCESS.2025.353236110848104LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype ClassificationNimisha Ghosh0Sankar Kumar Mridha1Rourab Paul2https://orcid.org/0000-0001-5322-281XDepartment of Computer Science and Engineering, Centre of Internet of Things, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, IndiaDepartment of Computer Science and Information Technology, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, IndiaDepartment of Computer Science and Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, IndiaBreast cancer is the most common cancer type among females and is one of the leading causes of death worldwide. Being a heterogeneous disease, subtyping breast cancer plays a vital role in its treatment. In this regard, gene expression plays an important role. Thus, in this work gene expression data is used to identify the most significant gene biomarkers. The identified biomarkers are highly associated with each breast cancer subtype such as Luminal A, Luminal B, HER2-Enriched and Basal-Like. To identify such biomarkers, initially LASSO in association with four machine learning models such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbours (KNN) and Naive Bayes (NB) are applied on the dataset to find the initial reduced set of genes as well as the best learning model based on classification accuracy; SVM in this case. Thereafter, Modified Compact Genetic Algorithm (mCGA) is performed to identify the final set of genes as biomarkers for each specific subtype. Experimental results suggest that our proposed method assesses AUC-ROC values of 0.9878 and 0.97311 for LumA and LumB and 1 for Basal and HER2 subtypes. To validate the biological significance of the identified biomarkers, KEGG pathway and GO enrichment analysis are carried out.https://ieeexplore.ieee.org/document/10848104/Biomarkerbreast cancerfeature selectionLASSOmodified compact genetic algorithm |
| spellingShingle | Nimisha Ghosh Sankar Kumar Mridha Rourab Paul LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification IEEE Access Biomarker breast cancer feature selection LASSO modified compact genetic algorithm |
| title | LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification |
| title_full | LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification |
| title_fullStr | LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification |
| title_full_unstemmed | LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification |
| title_short | LASSO-mCGA: Machine Learning and Modified Compact Genetic Algorithm-Based Biomarker Selection for Breast Cancer Subtype Classification |
| title_sort | lasso mcga machine learning and modified compact genetic algorithm based biomarker selection for breast cancer subtype classification |
| topic | Biomarker breast cancer feature selection LASSO modified compact genetic algorithm |
| url | https://ieeexplore.ieee.org/document/10848104/ |
| work_keys_str_mv | AT nimishaghosh lassomcgamachinelearningandmodifiedcompactgeneticalgorithmbasedbiomarkerselectionforbreastcancersubtypeclassification AT sankarkumarmridha lassomcgamachinelearningandmodifiedcompactgeneticalgorithmbasedbiomarkerselectionforbreastcancersubtypeclassification AT rourabpaul lassomcgamachinelearningandmodifiedcompactgeneticalgorithmbasedbiomarkerselectionforbreastcancersubtypeclassification |