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Machine learning models benchmarked for breast cancer prediction using multi-omics data

By PulseAugur Editorial · [1 sources] ·

Researchers have benchmarked several machine learning models for predicting Estrogen Receptor (ER) status in breast cancer using multi-omics data. The study found that RNA expression data provided the strongest predictive signal, with multi-omic integration offering modest but consistent improvements. Among the models tested, Random Forest performed best when integrating transcriptomic, genomic, and proteomic data, achieving a balanced accuracy of 90.3% and an ROC-AUC of 97.1%. The models also highlighted the importance of genes like ESR1 and PGR, reinforcing their biological relevance in ER status prediction. AI

IMPACT This research demonstrates the continued effectiveness of classical machine learning for complex biological data, potentially guiding future diagnostic tool development.

RANK_REASON Academic paper detailing a benchmarking study of machine learning models for a specific biological prediction task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Machine learning models benchmarked for breast cancer prediction using multi-omics data

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Academic paper detailing a benchmarking study of machine learning models for a specific biological prediction task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Priyanka Paudel, Madan Baduwal ·

    Benchmarking Machine Learning Models for Multi-Omics-Based Breast Cancer Prediction

    arXiv:2607.16250v1 Announce Type: cross Abstract: Estrogen Receptor (ER) status is a critical biomarker in breast cancer diagnosis, prognosis, and treatment selection. Recent advances in high-throughput sequencing technologies have enabled the generation of multi-omics datasets t…

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