Proves that checking global identifiability of parametric linear ODE models over reals is NP-hard via equivalence to the injectivity problem.
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WebProtÃľgÃľ: a collaborative Web-based platform for editing biomedical ontologies
20 Pith papers cite this work. Polarity classification is still indexing.
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CHR improves medical question answering retrieval by explicitly promoting evidence aligned with a correct hypothesis while penalizing content aligned with a plausible incorrect alternative.
An efficient enumeration algorithm is developed from sufficient conditions on subgraphs in the bipartite König representation to identify autocatalytic subnetworks and minimal cores in full metabolic networks.
cuSBF delivers a GPU-accelerated Super Bloom Filter for genomic sequences with up to 9.1x insertion and 7.7x query speedups over GPU baselines and high SM utilization via sequence-aware tiling.
HADES adapts knowledge distillation for hypergraph neural networks by using quantified node heterophily as a proxy for teacher reliability, yielding student models that often outperform teachers with up to 12.3x faster inference.
Decentralized AI agent teams self-organize around hypotheses, critique proposals, and share knowledge to outperform single-agent baselines on biomedical ML, language-model optimization, and protein fitness tasks.
MSB is a late-fusion stacking framework for multimodal survival prediction under blockwise missingness that improves C-index over baselines on the PIONeeR lung cancer immunotherapy dataset.
Pluot enables a single Rust visualization rendering function to execute reproducibly across languages and output formats via generated bindings.
A new tree-conditioned edit-flow model for ancestral sequence reconstruction achieves reasonable accuracy on substitution-only evolved sequences and superior localization of changes on natural indel-rich sequences.
EncFormer reduces online MPC communication by 1.4x-30.4x and end-to-end latency by 1.3x-9.8x versus prior hybrid FHE-MPC systems for private GPT- and BERT-style inference while preserving accuracy.
RAG-GNN augments GNNs with retrieved literature knowledge via gated fusion to improve functional clustering of 379 proteins in cancer signaling networks, raising silhouette score by 0.093.
PepMorph generates morphology-targeted peptides via a Transformer conditional VAE and reports 83% success under CG-MD validation.
Presents an optimal transport framework for simulating particle systems with arbitrary cell shapes and volumes that automatically handles exclusion constraints.
TriMod-DTI uses contrastive learning across 1D sequences, 2D graphs, and 3D structures to outperform prior DTI methods on three benchmarks.
PLM embeddings improve antibody monomer CDR-H3 accuracy but fail on complexes without co-evolution signals, while MSA refinement and convergence-aware recycling yield gains over AlphaFold3 on held-out antibody-antigen data without retraining.
scHelix uses explicit gene-level partitioning into Anchors and Variants plus an asymmetric Align-Refine-Fuse dual-stream architecture to improve batch correction in scRNA-seq without over-correcting biological signals.
Hyformer jointly models molecule generation and property prediction via alternating attention and joint pre-training, showing synergistic gains in conditional sampling, OOD prediction, and a drug design case for antimicrobial peptides.
Large-scale neutral benchmark of survival models on low-dimensional right-censored data finds Cox PH performs comparably to more complex methods across discrimination, calibration, and predictive metrics.
MathQA retrieves Wikidata formulas for natural language questions in English or Hindi, enables SymPy-based computation with user inputs and Wikidata constants, and outperformed a commercial engine by 13% in a user study while aiding formula imports with an 80% accurate heuristic.
Fine-tuned LLaMA3 with LoRA reaches 81.24% F1 on 18-category fine-grained medical entity recognition, beating zero-shot by 63.11% and few-shot by 35.63%.
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Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition
Fine-tuned LLaMA3 with LoRA reaches 81.24% F1 on 18-category fine-grained medical entity recognition, beating zero-shot by 63.11% and few-shot by 35.63%.