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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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Evaluations on simulated and real-world proteomic datasets demonstrated ST-CS\u2019s superiority in feature selection capability and classification performance. In simulations, ST-CS achieved feature selection robustness with balanced sensitivity (&gt;\u200980%) and specificity (&gt;\u200999.8%), reducing false discovery rates (FDR) by 20\u201350% 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\u2019s classification accuracy (AUC\u2009=\u200997.47% 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\u2009\u00b1\u20095 selected biomarkers. These results highlight ST-CS\u2019s ability to rigorously automate feature selection while balancing classification efficacy, interpretability, and scalability for translational proteomics.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/s12859-025-06193-2","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T13:27:45Z","timestamp":1751376465000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering"],"prefix":"10.1186","volume":"26","author":[{"given":"FuDong","family":"Wen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dan","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YuPeng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"MeiNa","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,1]]},"reference":[{"issue":"1","key":"6193_CR1","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.cell.2024.10.045","volume":"188","author":"YT Deng","year":"2025","unstructured":"Deng YT, You J, He Y, Zhang Y, Li HY, Wu XR, Cheng JY, Guo Y, Long ZW, Chen YL, et al. 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