arXiv Machine Learning June 2, 2026 By Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Harshith Guduru, Mohanad Mohammed

Interpretable Graph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification using Multi-Omics Data

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arXiv:2503. 22939v4 Announce Type: replace Abstract: The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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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.

By Pedro Henrique da Costa Avelar, Le Ou-Yang, Min Wu, Sophia Tsoka

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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.

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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.

By Sujoy Banik, Sayantan Chakraborty, Boishakhi Das Toma, Zainab Ghafoor, Ushashi Bhattacharjee, Koushik Howlader, Tirtho Roy

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arXiv Machine Learning

Sep 23

An Accurate and Interpretable Hyper Graph Neural Network for GBM Survival Prediction

arXiv:2609.25088v1 Announce Type: cross Abstract: Survival prediction for glioblastoma multiforme (GBM) demands models that are both accurate and interpretable, yet existing approaches treat these ob...

By Mushahid Intesum

multimodal safety

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.

By Dongmin Bang, Sugyun An, Inyoung Sung, Ilho Yun, Sun Kim, Sangseon Lee

rag diffusion benchmarks

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.

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