TL;DR
This study demonstrates that multi-omics integration via machine learning enhances survival prediction and reveals actionable biomarkers in breast cancer and outperformed mutation-based models.
An interpretable three-stage machine learning framework integrating mRNA, microRNA, DNA methylation, copy number variation, and protein expression data from The Cancer Genome Atlas that couples improved prognostic estimation with biological interpretability supporting multi-omics biomarker discovery in ovarian cancer.
A novel PCD-based prognostic signature for breast cancer is established and PDIA4 is identified as a functionally important oncogene, which effectively stratified patients into distinct risk groups and robustly predicted overall survival.
Considering the accessibility and stability of ct-miRNAs, this signature might improve the recurrence risk prediction identifying who, despite the early diagnosis, might need a more intensive screening or secondary prevention strategies.
An attention-based multi-omics framework that integrates pretreatment genomics, transcriptomics, proteomics, epigenomics, and clinical variables to predict pCR in early-stage breast cancer improves prediction of NAC response while producing interpretable outputs.
Accurate prediction of drug sensitivity in cancer cell lines is vital for precision oncology and patient-specific therapies. However, many computational approaches fail to integrate multi-modal biological and chemical features and often struggle with high-dimensional, imbalanced pharmacogenomic data, limiting predictiv...
A robust machine learning framework integrating genomic mutations and copy number alterations to predict survival in BM patients demonstrates stable performance and effective risk stratification in an independent cohort, supporting its potential clinical utility for prognostic assessment and precision oncology applicat...
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