Jul 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 34 references
Computer Science
TL;DR
A multimodal Markov Blanket (MB)-based feature selection framework that performs local structure learning across seven data modalities and introduces a composite dependency-ranking fallback when sparse neighborhoods lead to data starvation is developed.
A multi-tier, explainable AI framework designed to risk-stratify patients and predict overall survival using clinical and genomic covariates is developed and demonstrates that explainable machine learning models can robustly predict survivability and highlight actionable features for oncology dashboards.
MoESurv, a zero-sample survival prediction framework that leverages a mix-ture-of-experts architecture to extract generalizable prognostic patterns from pan-cancer data, is proposed, demonstrating both clinical utility and biological interpretability.
This work proposes MultiSigBERT, a unified framework for multimodal sequential survival modeling in oncology based on path signature representations that achieves a concordance index of 0.743 on an independent test set, demonstrating the benefit of jointly modeling multimodal temporal dynamics together with patient-lev...
Paul Minchella, Stéphane Chrétien, Guillaume Metzler et al.· 0 citations
An Explainable Hybrid Machine Learning (XML) framework that integrates advanced feature extraction with interpretable classification techniques for early breast cancer detection and staging and offers a robust, transparent, and clinically auditable solution for personalized breast cancer diagnosis and treatment plannin...
Shubhangi, Sanjeev Sharma, Akhtar Husain· International journal of com...· 0 citations
It is demonstrated that assigning complementary roles to stacking multimodal information integration and bagging for prediction stabilization provides an effective and robust framework for multi-omics breast cancer prognosis, supporting the use of complementary ensemble paradigms for multimodal prediction in precision...
Reza Bozorgpour, Mohammadreza Soltany Sadrabadi· Clinical Cancer Bulletin· 0 citations
The proposed explainable ensemble learning framework provides accurate, robust, and interpretable lung cancer stage prediction through the integration of leakage-free model development, optimized ensemble learning, explainable artificial intelligence, statistical validation, and component-wise ablation analysis.
M. Alfuraydan, Shahid Mohammad Ganie, Ehab Seedahmed et al.· Discover Computing· 0 citations
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