arXiv:2606. 27413v1 Announce Type: cross Abstract: Understanding which genes control which traits in an organism remains one of the central challenges in biology.
arXiv:2607. 01627v1 Announce Type: cross Abstract: Accurate protein-protein interaction (PPI) prediction is central to functional genomics, disease mechanism discovery, and drug development.
arXiv:2606. 14734v1 Announce Type: cross Abstract: Motivation: Gene regulatory network inference from single-cell RNA sequencing (scRNA-seq) data is important for uncovering cell-state-specific transcriptional programs.
arXiv:2607. 13120v1 Announce Type: cross Abstract: Inferring gene regulatory networks (GRNs) from single-cell transcriptomic data is crucial for biological discovery, yet existing approaches suffer from a fundamental misalignment with real-world needs.
arXiv:2607. 06224v1 Announce Type: new Abstract: Designing microbial strains that produce high-value chemicals at commercially viable titers remains a central challenge in metabolic engineering.
OptimusKG is a multimodal biomedical labeled property graph that integrates structured and semi‑structured resources to preserve detailed, type‑specific metadata across molecular, anatomical, clinical, and environmental domains. The graph contains nearly 191,000 nodes, over 21.8 million edges, and more than 67 million property instances derived from 18 ontologies, with a top‑level schema that enforces node and edge constraints while retaining granular provenance. Validation using the PaperQA3 agent found that 70.0% of sampled edges are supported by literature evidence, and the graph offers a standardized resource for machine learning, knowledge‑grounded retrieval, and hypothesis generation in biomedical research.
arXiv:2608. 05928v1 Announce Type: new Abstract: Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes.
Single-cell transcriptomes are sparse observations of coordinated biological programmes, yet most self-supervised models learn by reconstructing individual genes. Here we present BioM-JEPA, a joint-embedding predictive architecture that instead predicts aggregate representations of graph-connected gene blocks defined by protein-association and corpus-derived coexpression evidence.
arXiv:2606. 07700v1 Announce Type: cross Abstract: Background: Prediction of essential genes (proteins), is a basic and challenging problem but at the same time very costly and time-consuming in wet-lab experiments.
arXiv:2606. 24940v1 Announce Type: cross Abstract: Predicting transcriptional responses to genetic perturbations could reduce the experimental burden of functional genomics, but extrapolation to genes that were never perturbed during training remains difficult.
arXiv:2407. 07357v3 Announce Type: replace Abstract: Predicting signed interactions in biological networks is crucial for understanding drug mechanisms and facilitating drug repurposing.
arXiv:2512.09365v2 Announce Type: replace Abstract: Predicting molecule-protein interactions (MPIs) is a fundamental task in computational biology, with crucial applications in drug discovery and mol...
arXiv:2605. 15511v2 Announce Type: replace Abstract: Graph Neural Networks (GNNs) have become the dominant framework for inductive graph-level learning.