arXiv Machine Learning By Agnideep Aich, Sameera Hewage, Md Monzur Murshed

Copula Based Fusion of Clinical and Genomic Machine Learning Risk Scores for Breast Cancer Risk Stratification

Read the original on arXiv Machine Learning →

arXiv:2511. 17605v2 Announce Type: replace Abstract: Clinical and gene-expression models predict breast cancer outcomes, but simple linear fusion ignores dependence between their risk scores.

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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
arXiv Machine Learning
Sep 16

Copula Adapted Directed Acyclic Graph for Cluster Representation of Biomedical Data

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 Computation and Language
3d ago

Large Language Models are Approximate Survival Estimators

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