Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering
Abstract Background High-dimensional proteomics data present significant challenges in biomarker discovery due to technical noise, feature redundancy, and multicollinearity. Current feature selection methods, including filter, wrapper, and embedded approaches, struggle with stability, sparsity, and...
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2025-07-01
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| Online Access: | https://doi.org/10.1186/s12859-025-06193-2 |
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| author | FuDong Wen Yue Su Dan Liu YuPeng Wang MeiNa Liu |
| author_facet | FuDong Wen Yue Su Dan Liu YuPeng Wang MeiNa Liu |
| author_sort | FuDong Wen |
| collection | DOAJ |
| description | Abstract Background High-dimensional proteomics data present significant challenges in biomarker discovery due to technical noise, feature redundancy, and multicollinearity. Current feature selection methods, including filter, wrapper, and embedded approaches, struggle with stability, sparsity, and computational efficiency. To address these limitations, we propose Soft-Thresholded Compressed Sensing (ST-CS), a hybrid framework integrating 1-bit compressed sensing with K-Medoids clustering. Unlike conventional methods relying on manual thresholds, ST-CS automates feature selection by dynamically partitioning coefficient magnitudes into discriminative biomarkers and noise. Results Evaluations on simulated and real-world proteomic datasets demonstrated ST-CS’s superiority in feature selection capability and classification performance. In simulations, ST-CS achieved feature selection robustness with balanced sensitivity (> 80%) and specificity (> 99.8%), reducing false discovery rates (FDR) by 20–50% compared to Hard-Thresholded Compressed Sensing (HT-CS). Additionally, it attained superior F1 scores and Matthews Correlation Coefficients (MCC), outperforming HT-CS, LASSO, and SPLSDA in identifying true biomarkers while suppressing noise. For classification performance, ST-CS surpassed all methods in the area under the receiver operating characteristic curve (AUC) across varying noise levels while maintaining sparsity. Applied to Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets, ST-CS matched HT-CS’s classification accuracy (AUC = 97.47% for intrahepatic cholangiocarcinoma) but with 57% fewer selected features (37 vs. 86), demonstrating its dual strength in precision biomarker discovery and predictive accuracy. For glioblastoma data, ST-CS achieved higher AUC (72.71%) than HT-CS (72.15%), LASSO (67.80%), and SPLSDA (71.38%) while retaining a parsimonious feature set (30 vs. 58 features for HT-CS). In ovarian serous cystadenocarcinoma, ST-CS further demonstrated its adaptability, attaining superior AUC (75.86%) over HT-CS (75.61%), LASSO (61.00%), and SPLSDA (70.75%) with only 24 ± 5 selected biomarkers. These results highlight ST-CS’s ability to rigorously automate feature selection while balancing classification efficacy, interpretability, and scalability for translational proteomics. |
| format | Article |
| id | doaj-art-9f4a7d7e69f74987afa305d80e2aaab5 |
| institution | DOAJ |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | BMC |
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| series | BMC Bioinformatics |
| spelling | doaj-art-9f4a7d7e69f74987afa305d80e2aaab52025-08-20T03:04:11ZengBMCBMC Bioinformatics1471-21052025-07-0126111610.1186/s12859-025-06193-2Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clusteringFuDong Wen0Yue Su1Dan Liu2YuPeng Wang3MeiNa Liu4Department of Biostatistics, Public Health College, Harbin Medical UniversityDepartment of Biostatistics, Public Health College, Harbin Medical UniversityDepartment of Biostatistics, Public Health College, Harbin Medical UniversityDepartment of Biostatistics, Public Health College, Harbin Medical UniversityDepartment of Biostatistics, Public Health College, Harbin Medical UniversityAbstract Background High-dimensional proteomics data present significant challenges in biomarker discovery due to technical noise, feature redundancy, and multicollinearity. Current feature selection methods, including filter, wrapper, and embedded approaches, struggle with stability, sparsity, and computational efficiency. To address these limitations, we propose Soft-Thresholded Compressed Sensing (ST-CS), a hybrid framework integrating 1-bit compressed sensing with K-Medoids clustering. Unlike conventional methods relying on manual thresholds, ST-CS automates feature selection by dynamically partitioning coefficient magnitudes into discriminative biomarkers and noise. Results Evaluations on simulated and real-world proteomic datasets demonstrated ST-CS’s superiority in feature selection capability and classification performance. In simulations, ST-CS achieved feature selection robustness with balanced sensitivity (> 80%) and specificity (> 99.8%), reducing false discovery rates (FDR) by 20–50% compared to Hard-Thresholded Compressed Sensing (HT-CS). Additionally, it attained superior F1 scores and Matthews Correlation Coefficients (MCC), outperforming HT-CS, LASSO, and SPLSDA in identifying true biomarkers while suppressing noise. For classification performance, ST-CS surpassed all methods in the area under the receiver operating characteristic curve (AUC) across varying noise levels while maintaining sparsity. Applied to Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets, ST-CS matched HT-CS’s classification accuracy (AUC = 97.47% for intrahepatic cholangiocarcinoma) but with 57% fewer selected features (37 vs. 86), demonstrating its dual strength in precision biomarker discovery and predictive accuracy. For glioblastoma data, ST-CS achieved higher AUC (72.71%) than HT-CS (72.15%), LASSO (67.80%), and SPLSDA (71.38%) while retaining a parsimonious feature set (30 vs. 58 features for HT-CS). In ovarian serous cystadenocarcinoma, ST-CS further demonstrated its adaptability, attaining superior AUC (75.86%) over HT-CS (75.61%), LASSO (61.00%), and SPLSDA (70.75%) with only 24 ± 5 selected biomarkers. These results highlight ST-CS’s ability to rigorously automate feature selection while balancing classification efficacy, interpretability, and scalability for translational proteomics.https://doi.org/10.1186/s12859-025-06193-2Feature selectionClassificationCompressed sensingK-Medoids clusteringProteomics |
| spellingShingle | FuDong Wen Yue Su Dan Liu YuPeng Wang MeiNa Liu Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering BMC Bioinformatics Feature selection Classification Compressed sensing K-Medoids clustering Proteomics |
| title | Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering |
| title_full | Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering |
| title_fullStr | Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering |
| title_full_unstemmed | Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering |
| title_short | Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering |
| title_sort | automated sparse feature selection in high dimensional proteomics data via 1 bit compressed sensing and k medoids clustering |
| topic | Feature selection Classification Compressed sensing K-Medoids clustering Proteomics |
| url | https://doi.org/10.1186/s12859-025-06193-2 |
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