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.
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.
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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 Machine Learning
4d ago
Benchmarking graph-based models for in-silico toxicity prediction in drug discovery
arXiv:2609.37555v1 Announce Type: new Abstract: Drug discovery is a costly and high-risk process, where toxicity-related failures remain a major cause of attrition in both preclinical and clinical st...
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 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...
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 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...
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.
arXiv Machine Learning
Aug 24
PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction
PerturbRx is a treatment‑conditioned representation learning framework that learns latent transitions induced by drug interventions. It trains a drug‑ and dose‑conditioned transition predictor using control and treated single‑cell populations, then applies this predictor to pretreatment patient profiles to generate response features without needing post‑treatment data. On TCGA and patient‑derived xenograft benchmarks, PerturbRx outperforms other methods, demonstrating the value of perturbation‑pretrained latent transitions for patient‑level drug‑response prediction.
arXiv Machine Learning
Aug 7
THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction
arXiv:2608. 05982v1 Announce Type: new Abstract: Inadequate target--disease linkage accounts for 40--50\% of Phase~II efficacy failures, so anticipating which programmes will advance would let sponsors back the hypotheses most likely to reach patients.
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.