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.
Related stories
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
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.
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 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.
arXiv Machine Learning
Aug 6
The Cost of Binarizing Survival Outcomes in Clinical Prognostic Modeling
arXiv:2608. 04046v1 Announce Type: cross Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training.
arXiv AI
Aug 7
CASCADE: An Agentic Regulatory Network Framework for Patient-Data-Validated Downstream Perturbation Prediction
arXiv:2608. 05359v1 Announce Type: new Abstract: CASCADE is an agentic framework that predicts downstream transcriptional effects of gene perturbation from precomputed ARACNe regulatory networks, exposed via MCP.
arXiv AI
Jul 21
GigaPath-Flash and GigaTIME-Flash: Efficient Pathology Foundation Models for Whole-Slide and Tumor Microenvironment Analysis
arXiv:2607. 18218v1 Announce Type: cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.
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 Machine Learning
6d ago
Large-scale AI-Ready Data for Anti-Cancer Drug Response Modeling
arXiv:2608. 11444v1 Announce Type: cross Abstract: Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective anticancer drugs.