arXiv AI By Priyanka Paudel, Madan Baduwal

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

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arXiv:2607. 16250v1 Announce Type: cross Abstract: Estrogen Receptor (ER) status is a critical biomarker in breast cancer diagnosis, prognosis, and treatment selection.

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arXiv Machine Learning
Sep 24

Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics

The study benchmarks active spot selection methods against random sampling for spatial transcriptomics, focusing on cost‑efficient data acquisition. Using two public cohorts, the authors simulate multi‑round selection with uncertainty‑based (MC‑dropout, TOD) and diversity‑based (CoreSet, TypiClust) strategies, evaluating performance at 5%, 10%, 30%, and 50% of the spot pool. Results show that none of the active strategies consistently outperforms random sampling across all budgets or evaluation metrics, with performance varying by dataset and metric.

By Zheyu Zhu, Junchao Zhu, Fengbei Liu, Tianyuan Yao, Gelei Xu, John Cannon, Haichun Yang, Yuankai Huo, Mert R. Sabuncu, Ruining Deng