ArticleScientific data2024
Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- From Machine Learning-Enhanced Proteomics to a Validated Diagnostic Model: A Pipeline for Breast Cancer Biomarker Discovery via Independent and Transcriptomic Corroboration.Bioengineering (Basel, Switzerland) · 2026Article
- Ensemble machine learning algorithms leveraged on serum proteomics for enhanced early detection of ovarian cancer.Scientific reports · 2026Article
- Integrative analysis of tumor-educated platelets for stage-specific diagnosis, prognosis, and therapy in ovarian cancer.Discover oncology · 2026Article
- Development of an interpretable machine learning model for lymphovascular space invasion prediction in patients with endometrioid endometrial carcinoma: A prospective study.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026Article
- Ovarian Tumor Biomarkers: Correlation Between Tumor Type and Marker Expression, and Their Role in Guiding Therapeutic Strategies.International journal of molecular sciences · 2025Review
- New Method Enhanced Extraction of Protein Signatures of Renal Cell Carcinoma from Proteomics Data.Journal of proteome research · 2025Article
- Identification of key candidate genes for ovarian cancer using integrated statistical and machine learning approaches.Briefings in bioinformatics · 2025Article
- Amino Acid Profile Alterations in the Mother-Fetus System in Gestational Diabetes Mellitus and Macrosomia.International journal of molecular sciences · 2025Article
- Circulating proteins and metabolites panel for noninvasive preoperative diagnosis of epithelial ovarian cancer.BMC medicine · 2025Article
- Machine Learning Framework for Ovarian Cancer Diagnostics Using Plasma Lipidomics and Metabolomics.International journal of molecular sciences · 2025Article
- Integrated Machine Learning Algorithms-Enhanced Predication for Cervical Cancer from Mass Spectrometry-Based Proteomics Data.Bioengineering (Basel, Switzerland) · 2025Article
- Are we ready to integrate advanced artificial intelligence models in clinical laboratory?Biochemia medica · 2025Review
- TNXB modulates radiosensitivity of esophageal cancer through the ATM/P53 pathway.American journal of translational research · 2025Article
- Unveiling drug resistance pathways in high-grade serous ovarian cancer(HGSOC): recent advances and future perspectives.Frontiers in immunology · 2025Review
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3 authors.
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Abstract
Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biological insights. Here, we report a strategic machine learning (ML)-based feature extraction workflow that was applied to unveil high-performing protein markers for high-grade serous ovarian carcinoma (HGSOC) from publicly available ovarian cancer tissue and serum proteomics datasets. Diagnosis of HGSOC, an aggressive form of ovarian cancer, currently relies on diagnostic methods based on tissue biopsy and/or non-specific biomarkers such as the cancer antigen 125 (CA125) and human epididymis protein 4 (HE4). Our newly developed ML-based approach enabled the identification of new serum proteomic biomarkers for HGSOC. The performance verification of these marker combinations using two independent cohorts affirmed their outperformance against known biomarkers for ovarian cancer including clinically used serum markers with >97% AUC. Our analysis also added novel biological insights such as enriched cancer-related processes associated with HGSOC.
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