arXiv:2607. 16250v1 Announce Type: cross Abstract: Estrogen Receptor (ER) status is a critical biomarker in breast cancer diagnosis, prognosis, and treatment selection.
By Priyanka Paudel, Madan Baduwal
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
The paper presents Copula Adapted Directed Acyclic Graph (CopDAG), a framework that combines copula models with an ensemble of causal structure discovery methods based on Directed Acyclic Graphs to represent biomedical data. By capturing non‑Gaussian, non‑linear dependencies and stable causal relationships, CopDAG enables clustering of unlabeled biomedical data using K‑means. Across 16 biomedical datasets, CopDAG achieves the highest normalized clustering accuracy and adjusted Rand index among 12 evaluated methods, and it can predict class labels and provide explainable causal visualizations without relying on data annotations.
By Heranga K. Rathnasekara, Norou Diawara, Manar D. Samad
arXiv:2609.38181v1 Announce Type: new
Abstract: Survival analysis estimates time-to-event outcomes from patient covariates and is widely used for medical risk assessment. Patients seeking prognostic...
By Juan M Zambrano Chaves, Peniel Argaw, Risa Ueno, Carlo Bifulco, Kristina Young, Rom Leidner, Tristan Naumann, Hoifung Poon
arXiv:2608. 00271v1 Announce Type: cross Abstract: A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.
By Bego\~na B. Sierra, Colin McLean, Peter S. Hall, Sarah Friedrich-Welz, Catalina A. Vallejos
arXiv:2608. 04025v1 Announce Type: cross Abstract: Whole-slide survival models commonly provide risk rankings without calibrated statements about individual event times.
By Mingi Hong