arXiv Machine Learning
Jun 10
TRAPS: Therapeutic Response Analysis via Pathway-informed Stratification
arXiv:2606. 09898v1 Announce Type: new Abstract: Cancer treatment planning requires decisions across multiple clinical dimensions at once.
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
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arXiv Machine Learning
Jul 24
Evaluation and Prognostic Validation of Deep Regression Models for WSI-Based Gene-Expression Prediction
arXiv:2410. 00945v2 Announce Type: replace-cross Abstract: Gene-expression profiling is widely used in research and central to many areas of precision oncology, but remains costly and not universally accessible.
arXiv AI
Sep 15
Causal multi-modal AI for personalized chemosensitivity prediction
A causal multi-modal AI model was developed to predict personalized chemosensitivity in breast cancer patients using routine pathology and clinical data. Trained on 9,141 patients from nine countries and validated on 1,994 patients from three countries, the model produced treatment-specific recurrence probabilities with near-perfect calibration and strong prognostic discrimination over 5- and 10-year horizons. It outperformed existing recurrence-score tests and could reduce chemotherapy prescriptions by 30% while maintaining recurrence-free rates, with predictive performance also transferring to non-breast cancers.
arXiv Machine Learning
Aug 11
TRAPS: Treatment-Assignment Prediction via Pathway-informed Stratification
arXiv:2606. 09898v2 Announce Type: replace Abstract: Cancer treatment involves decisions across multiple clinical outcomes, yet pathway-informed deep learning models are typically evaluated in isolation, making their relative benefits unclear.
arXiv Machine Learning
Aug 26
A Multimodal Foundation Model for Longitudinal Patient Representation and Scalable Insight Generation in Oncology
arXiv:2608.24688v1 Announce Type: new Abstract: Precision oncology necessitates a longitudinal model of patient state that captures cancer evolution and treatment over time, integrating multimodal ob...
arXiv Machine Learning
Jul 7
Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
arXiv:2607. 05306v1 Announce Type: new Abstract: Integrating complex, multi-omics data presents significant challenges.
arXiv AI
Jul 7
Graph Representation Learning of Longitudinal Medical Imaging Trajectories for Treatment Response Prediction
arXiv:2607. 04912v1 Announce Type: cross Abstract: In patients with breast cancer, pathological complete response (pCR) has been established as a clinically meaningful surrogate marker for long-term outcomes.
arXiv Machine Learning
1d ago
GeneICL: A Tabular Foundation Model for Bulk Transcriptomics
arXiv:2610.08694v1 Announce Type: new Abstract: Gene expression is widely measured in biomedicine, yet clinical outcome prediction remains challenging due to high dimensionality, strong feature corre...
Hugging Face Trending Papers
Jul 6
Biologically Informed Deep Neural Networks for Multi-Omic Integration, Pathway Activity Inference and Risk Stratification in Cancer
Integrating complex, multi-omics data presents significant challenges. Existing approaches often face a trade-off between model interpretability and representational capacity, with most either relying on post-hoc interpretation or use linear models that may overlook complex interactions.
arXiv AI
Jul 21
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.
Hugging Face Trending Papers
Jul 6
Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations
Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard to interpret and unstable, whereas knowledge-graph methods provide mechanistic context yet remain static and fail to capture drug-induced transcriptomic perturbation dynamics.
arXiv AI
Jul 7
Predicting Therapeutic Outcome via Aligning Patient-Specific Knowledge Graph and Gene-Level Perturbation Representations
arXiv:2607. 04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles.