#biomedical-ai News & Analysis

30 articles tagged with #biomedical-ai. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

30 articles

AINeutralarXiv – CS AI · May 126/10

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LLM-Guided Monte Carlo Tree Search over Knowledge Graphs: Composing Mechanistic Explanations for Drug-Disease Pairs

Researchers introduce TESSERA, a neuro-symbolic framework that combines Large Language Models with Monte Carlo Tree Search to extract multi-step explanations from knowledge graphs, specifically for drug-disease mechanism discovery. The system uses LLMs for local judgments rather than autonomous generation, enforcing structural constraints through knowledge graphs while employing MCTS for principled credit assignment across extended reasoning chains.

AINeutralarXiv – CS AI · May 126/10

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PrimeKG-CL: A Continual Graph Learning Benchmark on Evolving Biomedical Knowledge Graphs

Researchers introduced PrimeKG-CL, a benchmark dataset for continual graph learning built from nine biomedical databases with 129K+ nodes and 8.1M+ edges across two temporal snapshots (2021-2023). The work evaluates how different machine learning strategies handle evolving biomedical knowledge graphs, revealing that decoder choice and learning strategy interact significantly and that standard metrics fail to distinguish between retaining valid facts and forgetting outdated ones.

🏢 Hugging Face

AINeutralarXiv – CS AI · May 116/10

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PPI-Net connects molecular protein interactions to functional processes in disease

Researchers introduce PPI-Net, a hierarchical graph neural network that integrates protein-protein interaction networks with biological pathway data to predict cancer outcomes and mechanisms. Demonstrating over 90% balanced accuracy across ten cancer types, the model reveals how molecular changes propagate through biological systems to drive disease, offering both predictive power and mechanistic interpretability.

AIBullisharXiv – CS AI · Mar 266/10

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PLACID: Privacy-preserving Large language models for Acronym Clinical Inference and Disambiguation

Researchers developed PLACID, a privacy-preserving system using small on-device AI models (2B-10B parameters) for clinical acronym disambiguation in healthcare settings. The cascaded approach combines general-purpose models for detection with domain-specific biomedical models, achieving 81% expansion accuracy while keeping sensitive health data local.

AIBullisharXiv – CS AI · Mar 26/1010

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SHINE: Sequential Hierarchical Integration Network for EEG and MEG

Researchers developed SHINE, a Sequential Hierarchical Integration Network for analyzing brain signals (EEG/MEG) to detect speech from neural activity. The system achieved high F1-macro scores of 0.9155-0.9184 in the LibriBrain Competition 2025 by reconstructing speech-silence patterns from magnetoencephalography signals.

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