Gene Ontology
PulseAugur coverage of Gene Ontology — every cluster mentioning Gene Ontology across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New Moose method enhances neuro-symbolic learning for OWL 2 EL ontologies
Researchers have developed Moose, a novel neuro-symbolic learning method designed for the OWL 2 EL profile, which is utilized in large-scale ontologies like Gene Ontology and SNOMED CT. Unlike previous methods that hand…
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GeneGeoFlow model predicts cell responses using gene geometry
Researchers have developed GeneGeoFlow, a novel method for predicting cellular responses to genetic and drug perturbations. This approach utilizes gene geometry derived from biological networks, such as Gene Ontology an…
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SciReasoner model enables interpretable scientific reasoning across disciplines · 5 sources tracked
Researchers have introduced SciReasoner, a multimodal scientific foundation model designed for native structural reasoning across proteins, small molecules, and inorganic crystals. This model discretizes structural info…
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Stable-Shift method predicts gene responses using biological context
Researchers have developed Stable-Shift, a novel method for predicting how gene expression will change in response to genetic perturbations, even for genes not seen during training. The approach integrates various biolo…
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New deep learning framework KEPLA enhances protein-ligand binding prediction
Researchers have developed KEPLA, a new deep learning framework designed to improve the accuracy of predicting protein-ligand binding affinity, a crucial step in drug discovery. Unlike previous models that relied solely…
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New estimator for sub-Gaussian parameter achieves root-n rate
Researchers have developed a new method for estimating the sub-Gaussian parameter of a random variable, a measure related to its variance. The proposed estimator is shown to be consistent and achieves convergence rates …
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New deep learning model accurately classifies multi-omics cancer data
Researchers have developed a novel deep learning framework called MOGKAN to classify multi-omics data for cancer diagnostics. This framework integrates messenger-RNA, micro-RNA, and DNA methylation samples with protein-…
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DOGMA framework weaves biological structure into single-cell transcriptomics AI
Researchers have introduced DOGMA, a novel data-centric AI framework for single-cell transcriptomics analysis. This framework integrates multi-level biological prior knowledge, moving beyond purely data-driven heuristic…