Database

Understanding protein mechanisms in health and disease requires characterizing the functional roles of individual amino acid residues. To explore the role of residues and their mutations, we have developed Atlantis, a database that integrates structural and functional information at the human proteome residue level. A graph database enables complex queries and the retrieval of integrated information for multiple functional analysis of protein systems. A Model Context Protocol (MCP) connector allows the interrogation of the resource through Large Language Models (LLMs) or agentic frameworks for biomedical research. Atlantis annotates over 11M residues across 20k human proteins, identifying hundreds thousands intra- and inter-protein contacts in PDB as well as AlphaFoldDB structures. We also provide the possibility to analyze and integrate predicted 3D complexes inputted by the user, and we showcased these features on hundreds of AlphaFold-multimer complexes of GPCRs and LRRK2 interaction networks. The tool is freely accessible at https://atlantis.bioinfolab.sns.it/.

GPCRs make up the largest family of human membrane proteins and of drug targets. Decades of experimental and structural work have revealed how these receptors operate at the molecular level, but this work has covered only a fraction of the superfamily, leaving the majority of receptors underexplored. To address this problem, we developed the GPCR Evolution Database, gpcrevolution.org, an open-access resource that makes high-quality evolutionary analysis of GPCRs available to every laboratory. Through our database, each human receptor can be examined against its own orthologs, where per-residue conservation reveals the sites that evolution has protected throughout that receptor's history. These orthologous lineages can then be compared with their paralogs within the same GPCR family, to distinguish residues shared across paralogous lineages, which likely support ancestral functions, from the lineage-specific residues that underlie functional differentiation between receptor subtypes. The database provides ortholog sets, multiple sequence alignments, phylogenetic trees and per-residue conservation scores for over 800 human GPCRs. The resource presents these through interactive conservation plots, sequence logos and snake plots, and provides tools for comparing conservation across two or more paralogous lineages. Overall, the GPCR Evolution Database provides the data and tools for researchers to examine GPCR function through an evolutionary lens, allowing molecular insights from well-studied receptors to be extended to their underexplored relatives.

AO_SCPLOWBSTRACTC_SCPLOWGetting clinical data from different sources to "talk" to each other within the OMOP Common Data Model (CDM) is arguably the most tedious part of multi-center research. While this integration is essential, the transformation process is frequently a manual grind, requiring a rare overlap of deep clinical knowledge and technical expertise. In this paper, we present a framework designed to alleviate some of the burden on the researcher by automating data harmonization through two distinct steps: structural schema mapping and terminological standardization. For the structural piece, we moved away from "black box" logic in favor of a stateful workflow managed by large language models (LLMs) and directed acyclic graphs. By profiling EHR data at the source, our system generates context-aware dictionaries that offer ranked mapping suggestions alongside confidence scores. While our benchmarking showed a 97.5% agreement rate at the schema level and an 84% agreement rate at the value level when compared with human experts, the system appears most effective when treated as a "co-pilot" rather than a total replacement for human oversight. To handle value-level standardization, we implemented a hybrid search strategy that pairs the semantic depth of SapBERT embeddings with the literal precision of fuzzy string matching. By using FAISS for rapid similarity retrieval, the engine attempts to resolve messy or "noisy" clinical descriptions to standard OMOP concepts. This approach seems particularly promising for handling the non-standardized labels that often plague smaller, local datasets. Ultimately, our results suggest that this guided approach can shift the timeline for OHDSI-compliant warehousing from weeks of manual curation to a more manageable and scalable pipeline, potentially lowering the barrier to entry for smaller research teams.

Existing general-purpose biomedical knowledge graphs tend to focus on disease mechanisms and drug repurposing, leaving multiomic and wellness-relevant content underrepresented. KRAKEN (Knowledge Research & Analysis Kit for Evidence Networks) addresses this gap by integrating existing graphs (including Translator KG Open, RTX-KG2, and ROBOKOP) with specialized sources such as RefMet, LIPID MAPS, NIH Common Data Elements, Polygenic Score Catalog, and derived wellness measures including biological age and biological BMI. The resulting graph spans ~15M nodes and ~113M edges across 62 entity types. KRAKEN adopts the Biolink Model as its semantic layer, ensuring compatibility with standardized resources emerging from the NIH NCATS Biomedical Data Translator program. A lightweight, modular build system rebuilds the full graph (including entity resolution), with peak memory consumption under 48 GB, and supports flexible inclusion or exclusion of sources, allowing the user to scope the graph to a domain of interest. Built-in analytical tools include multi-hop reasoning, subgraph extraction, text, vector and hybrid entity search, and enrichment analyses, all accessible through an interactive web interface, a REST API, and a Model Context Protocol server, the last enabling direct consumption by agentic and LLM-based systems. KRAKEN is freely available at https://app.krakenkg.com.

Tuberculosis remains a major global public health threat. While whole-genome sequencing has transformed our understanding of the causative agent, Mycobacterium tuberculosis (MTB), existing genomic databases are highly fragmented and often underrepresent structural variations (SVs). Furthermore, critical population-genetic statistics are rarely integrated with phylogenetic and geographic context, forcing researchers to reconcile separate datasets manually. To address this gap, we developed TBpop (https://tbpop.chinacdc.cn), an open-access, integrated population genomics portal. TBpop is built from 420 clinical MTB isolates selected from the first national drug resistance baseline survey in China. The portal integrates isolate metadata, pangenome categories, SNPs, SVs, IS6110 insertion sites, strain phylogeny, and gene-level statistics, and provides three interactive explorer modules: the Population Explorer, the Statistics Explorer, and the Variation Explorer. Additionally, a User Analysis module allows researchers to run population genetic workflows on their own alignments. TBpop provides an integrated platform for exploring genome plasticity, signatures of positive selection, and conservation patterns of functionally important genes in MTB.

Bacterial pathogens ensure their successful dissemination by employing specialized secretion systems to deliver a wide range of virulence proteins that interact with specific host proteins to modulate the response during infection. These interactions are often mediated by host-like elements, like short linear motifs, which can interfere with host interaction interfaces, enabling bacterial secreted proteins to compete and displace native partners of the targeted proteins. Several resources provide access to bacteria-host protein-protein interaction data. However, some are neither fully functional or up to date. Moreover, most of these databases lack virulence protein annotations, thus hampering hypothesis formulation based on protein interaction analyses. In this context, we have developed BactMentha, a unified resource that integrates bacterial-host protein-protein interactions for three organisms (human, mouse, and rat) with virulence protein annotations and bacterial strain clinical relevance information. BactMentha provides easy access to available experimentally identified binding regions of the interaction partners, complemented with putative interaction interfaces based on predicted structure complexes and on the detection of host-like elements on bacterial protein sequences interacting with human proteins. BactMentha currently contains 11 776 unique protein interactions between 2970 bacterial proteins, of which 684 have at least one virulence annotation, and 5088 host proteins. Experimentally identified binding regions are available for 532 bacteria-host interactions, most of which involve human proteins (440 interactions). Predicted interaction interfaces enabled a 3.7-fold increase in known and putative binding regions in the bacteria-human protein interaction data. By providing easy access to functionally annotated and clinically relevant protein interaction data, enriched with experimentally and predicted interaction interfaces, BactMentha can be instrumental in formulating novel hypotheses and speeding up wet lab research on the molecular mechanisms underlying pathogenic bacterial infections.

Patient-reported and clinically documented narratives often contain informal, fragmented and linguistically heterogeneous expressions, complicating medical concept normalization (MCN) and frequently necessitating pre-processing before terminology mapping. Despite rapid advances in biomedical language modeling, the comparative utility of representation models for MCN and their integration with instruction-tuned LLMs in automated normalization workflows remain underexplored. In this study, we systematically benchmarked 15 general, biomedical and clinical transformer-based representation models together with 12 instruction-tuned LLMs across 12,713 instances from five established datasets: TAC2017_ADR, TwADR-L, TwiMed, CADEC and SMM4H2017. Representation models were evaluated using embedding-based semantic retrieval, whereas instruction-tuned LLMs were assessed as upstream text-correction modules. SapBERT achieved the highest Top-5 accuracy among representation models, reaching 63.8% for SNOMED CT and 58.0% for MedDRA without correction. Qwen 2 Instruct was selected as the preferred corrector on the basis of its favorable balance between Top-1 normalization performance and computational efficiency relative to substantially larger models, including Llama 3.1 Instruct (70B). Incorporation of Qwen 2 Instruct increased Top-5 accuracy to 69.4% for SNOMED CT and 63.2% for MedDRA. The resulting framework accepts heterogeneous short medical expressions without manual input pre-processing and automatically performs text refinement, semantic retrieval and terminology mapping to standardized concepts and vocabulary codes. These findings establish a benchmark-guided, scalable framework for automated medical terminology standardization.

BackgroundReuse of archived transcriptomic data underpins a large and growing share of published genomics. Because differences in upstream processing confound cross-study comparison, uniform reprocessing compendia -- recount3, ARCHS4, DEE2, refine.bio, Expression Atlas -- are widely treated as the remedy, and their availability is routinely assumed at the point of study design. Whether that remedy is actually obtainable for the population of published disease RNA-seq has not been measured. Prior audits have characterised metadata completeness and deposition rates, but none has quantified, across the published population, what fraction of studies can be uniformly reprocessed or where in the path from publication to comparable counts that capability is lost. ResultsWe enumerated 1,124 MeSH disease descriptors exhaustively, retrieved 16,820 human RNA-seq series from the Gene Expression Omnibus, and audited the 3,631 bulk, Illumina-platform series of at least 25 samples under three independently pre-registered, tool-enforced analysis plans. Raw reads were publicly available for 94.1% of series under a dual-route evidence standard, but only 46.7% appeared in any uniform reprocessing compendium (bounds 46.7-57.2%) and only 27.3% were usably covered at a 90% run threshold (bounds 27.3-38.8%). Of the 3,418 series whose reads are public, 991 were usably covered, leaving 71.0% of read-public series reprocessed by nothing usable. Presence overstated usability: DEE2 was present for 30.2% of series but usable for 3.4%. Design attrition was independent and severe -- 24.7% met bulk primary-tissue case-control criteria, 7.4% additionally reached a minimum replication threshold counted on sample accessions, and 4.1% did so counted on distinct donors. Among the 199 series where donor identity resolves, 4.1% pass the replication criterion on donors against 15.1% on accessions, a 3.73-fold difference; across the census frame, accessions exceeded distinct donors by 2.65-fold (Manski bounds 1.09-10.77, Imbens-Manski 95% CI 1.07-11.88). Independently, 36.4% of series-to-disease attributions produced by a conventional keyword query were refuted by the curated MeSH headings of the series own linked publication. ConclusionsUniform reanalysis of published human disease RNA-seq is unavailable for most studies in the population audited, and the binding constraint is usable coverage rather than deposition of raw reads. The loss occurs at several independent layers with different remedies, and the coverage layer -- unlike the others -- is one that resource maintainers can act on. Automated retrieval further over-counts eligible studies, both by admitting designs outside scope and by assigning studies to diseases their publications do not support.

SysBio FAIRplex is a Common Fund Venture Program that catalogs and indexes data from the Accelerating Medicines Partnership(R) (AMP(R)) Program through a federated model in which data hosts retain custody of their datasets. The central piece of this work is the SysBio Common Data Model (SysBio CDM). AMP is a precompetitive public-private partnership started in 2014 that unites the resources of NIH and private partners to improve our understanding of disease pathways and transform current models for developing new treatments by: - identifying new targets, biomarkers, and development paradigms; - developing leading-edge tools and technologies; - collecting large-scale datasets and supporting analytics for open analysis by the public; and - generating consensus platforms and procedures. A multidisciplinary Task Force was chartered to design the SysBio CDM by extending the Observational Medical Outcomes Partnership (OMOP) Common Data Model into the -omics domain. The Task Force produced a Minimum Viable Product comprising nine OMOP tables; four extension tables for assay and file metadata; and a Common Data Element (CDE) Registry to specify field semantics. This manuscript describes the deliverable: the underlying design choices, the criteria applied in selecting and constructing the extension tables, how the extended model supports multimodal data integration across AMP projects, and what further work to support additional -omics modalities would entail. As an auxiliary methodology, the paper also describes the AI-assisted CDE harmonization workflow used to populate the model.

The UK Biobank (UKB) is a cornerstone of modern biomedical research, providing unparalleled data to advance the understanding, prediction, and treatment of diseases. Its contributions span genetics, genomics, disease prediction, and long-term follow-up studies, driving transformative advancements in public health and precision medicine. However, the fragmentation of research outcomes across numerous publications limits analytic efficiency and cross-study integration. To address this, we developed UKB Knowledge Graph (UKB-KG), a high-quality medical knowledge graph constructed using large language models (LLMs) with 88.8% precision as assessed by GPT-5.4. Integrating data from approximately 9,200 UKB-related publications. UKB-KG comprises 292,328 triples enriched with contextual attributes such as source information and demographic details. It reveals intricate relationships among diseases, genes, chemicals, lifestyle factors, and other biomedical entities, while a dynamic scoring mechanism enhances triple retrieval accuracy. Evaluations highlight UKB-KG's transformative potential. (i) Embedding UKB-KG into multi-disease prediction models improves AUROC, AUPRC, and F1 scores by 8.1%, 6.2%, and 2.7%, respectively, for rare diseases. (ii) A tailored retrieval-augmented generation (RAG) approach boosted LLM accuracy by 13.2% on PubMedQA. and (iii) A user-friendly platform enhances accessibility for researchers. By unifying fragmented research and enabling robust data exploration, UKB-KG emerges as a powerful tool for advancing biomedical research and driving innovative healthcare applications.

Bacteriophage lytic enzymes and depolymerases are relevant to phage biology, antimicrobial development, and protein engineering, but their sequence and annotation data remain dispersed across general databases, specialized resources, genome-centred collections, and prediction-oriented datasets. We present PhageLysData, an evidence-aware and AI-ready resource constructed through reproducible multisource integration, provenance tracking, and exact-sequence consolidation. The release integrates 807,366 source observations from seven primary resources into 759,105 unique exact-sequence entities, comprising an evidence-supported Core of 11,867 entities, a Prediction Extension of 745,092 prediction-only candidates, and 2,146 Context entities retained for provenance and reference. This architecture preserves broad sequence-space coverage while maintaining a clear distinction between non-predictive and prediction-derived support. Core entities are enriched with harmonized biological annotations, physicochemical properties, independent InterProScan-derived functional annotations, mapped PDB and AlphaFold DB structural assets, and reusable numerical representations. For 11,259 eligible Core sequences, PhageLysData provides embeddings from 11 protein language models together with one-hot encoding under a common representation contract. Release-facing examples demonstrate latent-space exploration, unsupervised clustering, supervised classification, and evidence-aware candidate retrieval without defining a universal predictive benchmark. PhageLysData provides a traceable, versioned, and computationally accessible foundation for protein retrieval, comparative analysis, task-specific dataset construction, and machine-learning applications involving phage lytic enzymes and depolymerases.

Biomedical knowledge graphs can connect information across genes, phenotypes, tissues, pathways, experiments, and clinical resources, but they are difficult to query correctly without detailed knowledge of the graph. Large language models can help write Cypher code for knowledge graphs, yet a query focused on a bioinformatics task that looks reasonable may still use the wrong identifier, relationship direction, source, intermediate node, or output unit if the user is not completely trained on the schema. We developed ddkg.skill, an Agent Skill that can be loaded into a compatible LLM session to support querying of the NIH Common Fund Data Ecosystem Data Distillery Knowledge Graph (DDKG), a biomedical property graph integrating more than 180 ontologies, over 40 genomics datasets, and dozens of additional cross-domain biomedical datasets. Rather than utilizing a simple markdown prompt, ddkg.skill contains a controller, a compositional library of DDKG-specific references and structured tables, primary documentation, validated query patterns, a routing table, and a Python script that checks the internal links among these materials. The skill build for the December 2025 DDKG release contains 38 bundled files and 239 routing relationships and is identified by checksum so users can state the exact build used to compose a query. The evaluation reported here was performed separately on an earlier build with 38 files and 227 routing entries. In that orthogonal nine-test evaluation against a live December 2025 DDKG instance, five of seven tests targeting sources absent from the worked examples produced correct executed results. The evaluation also exposed a failed query, a cross-source comparison that was not biologically well posed, and errors in the skills own reference material that informed later revisions. The skill guides an AI through entity resolution, graph inspection, query construction, and validation for biomedical and bioinformatics queries, while keeping the DDKG itself as the source of returned results. This design provides a portable and versioned method for giving general-purpose AI systems practical knowledge of a complex biomedical knowledge graph to empower complex bioinformatics data integration. Three executed biomedical use cases further demonstrate cross-species phenotype-to-expression querying, genomic overlap between 4DN chromatin-loop anchors and GTEx eQTLs, and integration of congenital heart phenotype associations with ClinVar and GTEx heart evidence.

Manual clinical DNA variant classification is the bottleneck of every clinical and research rare disease workflow. The process typically requires a curator to assemble evidence from numerous databases, weigh 28 criteria, reconcile competing evidence, and produce a defensible case for the final classification. Additionally, the framework used to assess variants is not static and successive addenda have revised individual criteria. The most complete and current evidence aggregators available are commercial platforms, which can limit researcher access. We present HeartVar, an open-source web tool that automates the evidence-gathering and interpretation steps of variant classification associated with cardiovascular disease. Given a gene, a variant, and clinical context, HeartVar queries 20 public databases in parallel and assigns ACMG/AMP criteria through a hybrid rule-based/large language model (LLM) approach. Criteria that can be resolved from structured data are computed programmatically, and only those requiring interpretation of unstructured evidence are passed to the LLM. HeartVar returns a classification, point score, per-criterion breakdown, clinical-narrative summary, and database annotations. Benchmarking of 106 expert-curated ClinGen variants showed HeartVar outperformed other curation tools, assigning the correct ACMG tier in 72% of cases. HeartVar demonstrates that an LLM constrained by a domain-specific prompt and grounded in structured evidence can produce variant interpretations of first-pass quality for a cardiovascular disease cohort; however, it is not intended to replace manual assessment by a qualified variant curator. The tool is freely available to use and hosted at www.heartvar.victorchang.edu.au.

We combine GeneNetwork's 1TB structured database with its metadata as an RDF graph and an associated literature corpus. We benchmark frontier and local LLMs querying it through four architectures: document, graph and agentic retrieval-augmented generation (RAG), and a combination of the three. We evaluate them on 40 domain specific expert-curated questions with a new metric: the satisfaction frequency psi (psi, i.e, the share of questions answered with precision and recall of at least 70% or an f1 of at least 60%). psi ranks systems the same way precision, recall and f1 -- a machine learning metric built from the harmonic mean of precision and recall -- but is easier to read and assess for humans. System architecture had a large effect: with full use of Claude Opus 4.8 the plain document RAG of our earlier GNQA system had a psi of 36% against 86% for our new combined tools. Delegating most of the work from Claude Opus 4.8 to a local model halved query costs while still reaching a psi of 59%.

Protein-peptide interactions are central to cellular signaling and to a growing class of peptide therapeutics, yet the datasets used to develop and benchmark computational methods for protein-peptide modeling remain poorly standardized. Available databases prioritize comprehensive coverage but require task-specific curation, while published benchmarks are typically distributed as static collections built with heterogeneous curation, quality-filtering, redundancy-reduction, and sampling strategies, limiting reproducibility and cross-study comparison. We present PepXPro, a modular framework that transforms publicly available protein-peptide structure-affinity resources into curated datasets and reproducible benchmark collections generated under user-defined criteria. PepXPro is organized into three components: Scrape, for deterministic curation of protein-peptide complex entries from public resources; GenSample, for constructing configurable subsets under explicit quality, redundancy, and sampling constraints; and Benchmark, for evaluating candidate subsets and selecting a nonredundant, representative, general-purpose benchmark for distribution. Starting from PDBbind and complementary resources, the curation pipeline yields a pool of proteinpeptide complex entries that retains chemically complex cases, including disulfide- linked cyclic peptides, which are commonly excluded from existing benchmarks. We release PepXPro Benchmark v1, a benchmark comprising 70 non-redundant protein- peptide complexes with experimentally determined structures and binding affinities. The underlying framework provides an extensible foundation for reproducible protein- peptide benchmark construction.

Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hybrid named entity recognition framework designed to automatically identify bioinformatics software and database (SW/DB) names from biomedical texts. SNAIL integrates complementary lexical and semantic modeling strategies. The lexical component captures orthographic patterns and contextual cues characteristic of SW/DB names, while the semantic component leverages contextual embeddings generated by transformer-based language models such as SciBERT, combined with an explicit token-masking strategy to enhance entity-focused representations. A large training corpus was constructed automatically through a hybrid pipeline that integrates citation-hinted extraction with large language model-assisted distillation. Evaluation on two independent benchmark datasets and real-world research articles demonstrates that SNAIL substantially outperforms existing approaches, including domain-specific methods such as bioNerDS2 and general-purpose large language models such as ChatGPT, Gemini, Grok and Claude. Applying SNAIL to large-scale literature analysis further reveals distinct journal-level preferences across bioinformatics subfields. These results demonstrate that SNAIL provides an accurate and scalable solution for identifying bioinformatics resources in scientific texts and enables systematic meta-analysis of tool usage and research trends.

The Big Fantastic Virus Database (BFVD) v3 is the most comprehensive resource for ColabFold-AlphaFold2-predicted viral protein structures. It holds 5,776,417 structures of nearly all viral sequences in UniProt 2025_03, a 16.4-fold increase compared to the representative-only catalogs of BFVD v1 and v2. Structure prediction quality has also improved, with high-confidence predictions accounting for 75.3% of BFVD v3 entries. This is due to Logan's enormous sequence assembly, now mined for the entire BFVD v3 instead of only for shallow alignments, alongside continued prediction quality improvements. BFVD v3 covers 72.7% of ICTV's virus species, 1.54-fold more than BFVD v2. With this version, the fraction of fully covered viral reference proteomes increases from 1.5% to 72.6%. BFVD v3 thus brings us closer to a structural catalog of the known virosphere and is freely available at bfvd.steineggerlab.workers.dev and bfvd.foldseek.com.

Public sequencing repositories contain large amounts of gut microbiome data that could support cross-study comparison, reproducibility analysis, and microbiome foundation model development. However, the extent to which these data are structured, harmonized, and reusable at archive scale remains unclear. Here, we characterized publicly available gut microbiome sequencing metadata from the NCBI Sequence Read Archive using Google BigQuery, focusing on human gut metagenome, mouse gut metagenome, and broadly annotated gut metagenome records. We evaluated temporal growth, sequencing depth, BioSample and BioProject structure, platform and instrument use, metadata completeness, host attribution, publication linkage, and research themes from linked literature. Public gut microbiome data increased substantially over time and were dominated by human-associated datasets and Illumina sequencing platforms. Core technical metadata fields were highly complete, but biological context needed for reuse, including host identity, phenotype, study design, and disease status, was often inconsistently encoded or required recovery from BioSample attributes and linked publications. In the generic "gut metagenome" cohort, host identity could be assigned for only 13.00% of BioSamples, highlighting the limitations of broad organism annotations for automated cohort construction. Publication linkage was also incomplete at the archive level, although usable text was recovered for most linked publications. Topic modeling of SRA-linked literature showed persistent emphasis on core gut microbiota composition and increasing representation of human cohort and infant microbiome studies. Overall, these findings show that public gut microbiome data are extensive and technically rich but not uniformly analysis ready. Improved metadata harmonization, publication linkage, and biological context recovery will be necessary to support reliable large-scale reuse and AI-ready microbiome data resources.

We describe an automated software tool to accomplish data curation tasks previously performed by humans for the Gemma genomics data re-analysis resource. Gemma is a hand-curated database of reprocessed transcriptomic studies, currently covering over 23,000 human, mouse and rat data sets largely drawn from the Gene Expression Omnibus (GEO). We developed a pipeline that uses both traditional (mechanical) and large-language models to produce detailed ontology-anchored, sample- and experiment-level annotations in accordance with our established curation guidelines. In this report, we describe benchmarking the pipeline and investigations aimed at evaluating readiness of the v1.1 Gemma curation agent for production use. Overall, performance is near that of human curators, at approximately 1/20th the cost and at least 100 times the speed. We also present preliminary exploration of triage methods for identifying agent curations that are more likely to contain errors, and thus can be forwarded for human review. We discuss the potential place of such curation approaches in bioinformatics ecosystems. Besides the software, our deliverables include the benchmark set of 500 studies and an evaluation framework that can be used to further develop the pipeline or compare to other approaches.

When an ontology-based biomedical knowledge graph (a KG, containing information about genes, pathways, phenotypes, and molecular interactions) is analyzed using a computational reasoning system, the system can return unwanted generic concepts. Such generic concepts, in the context of a query about potential drug treatments for a disease, might include 'pharmaceutical preparations' or 'antibiotics'. Answers can be filtered based on hand-curated block-lists or ontology Information Content (IC), but to our knowledge, (1) neither filtering method has been validated against expert judgments of generality, and (2) IC is not available for the majority of nodes in the representative KG used in this work. In this study, three domain experts rated representative lists of KG concepts for generality. We built a semantic precision score (SPS) from a concept's name and description alone, using a biomedical language model (BioLORD). For the purpose of filtering generic answers from biomedical query results, we compared (using queries for 200 diseases) the SPS against a curated concept block-list and two IC thresholds. IC agreed moderately with the experts (Spearman's {rho} = 0.59), but correlations were negligible or negative within IC bands. The SPS was well-correlated with the experts ({rho} = 0.76) globally and within SPS bands, and remained informative on concepts without IC ({rho} = 0.59). Hard filtering of query results using the SPS removed 1,502 of 33,161 query answers, nearly three times the block-list's 513, while removing fewer of the query-specific DrugCentral indications. Hard filtering by IC removed either few answers or many approved drugs. We additionally compared the filtering methods' performance on an independent evaluation set of queries for 200 diseases. The SPS filter removed 5,135 of 91,203 answers compared with the block-list's 1,419 and lost none of 1,296 indications compared with 17. Further, SPS filtering removed approved drugs no more often from the half of DrugCentral not used in training than from the half used in training. Semantic meaning captures expert judgments of specificity more faithfully than ontology position, and semantic scoring can replace IC and manually maintained block-lists for filtering drug-repurposing results.