Biology subjects

Simmons, J. A.

Publications and source records attributed to Simmons, J. A..

10 recordsLinked to original sources

bioRxiv2026

ddkg.skill: A Compositional Agent Skill for Translating Biomedical and Bioinformatics Questions into Cypher for the Data Distillery Knowledge Graph

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.

bioinformatics↗

bioRxiv2026

MOSAIC: Learning Graph Node Embeddings from Spectrally Isolated Dominant Subspaces of Accumulated Diffusion Operators

Graph embedding transforms complex networks into low-dimensional representations for analysis and downstream machine learning. Existing methods often rely on eigenvalue ordering, random-walk sampling, or neural optimization. We introduce MOSAIC, Multistep Operator Spectral Accumulation with Isolated Components, an unsupervised framework that constructs an accumulated diffusion operator from successive powers of a graph transition matrix. MOSAIC uses Krylov subspace analysis to identify dominant spectral components according to their convergence behavior. This approach emphasizes persistent and spectrally separated diffusion modes rather than selecting embedding coordinates solely by eigenvalue magnitude. We evaluated MOSAIC using a biomedical graph projection derived from the Data Distillery Knowledge Graph that contained 5,171 human genes and 10,147 phenotypes. Across embedding dimensions from 16 to 128, MOSAIC achieved node-type classification AUROCs from 0.9863 to 0.9926 and exceeded FastRP, Node2Vec, HashGNN, and GraphSAGE. For gene-phenotype link prediction, validation and test associations were withheld before embedding generation. Under progressive removal of all edges or specific edge classes, MOSAIC retained more node-label information than FastRP, including after extreme graph sparsification. In a focused DICER1 analysis, 106 of 137 documented phenotype associations received scores above 0.90, demonstrating concordance with known pleiotropic biology and prioritizing additional relationships for independent evaluation. These results support MOSAIC as a fully unsupervised framework for learning informative graph representations. Although demonstrated using a biomedical graph projection, MOSAIC is domain independent and can be applied to other complex networks

bioinformatics↗

bioRxiv2026

BIFO: A Biological Information Flow Ontology for Directed Propagation in Heterogeneous Biomedical Knowledge Graphs

Biomedical knowledge graphs integrate heterogeneous data by connecting many entity types through many relationship types. Computational analyses that propagate signal across these graphs (random walks, diffusion, and message passing) implicitly assume that every traversable edge can carry a biological signal. In a heterogeneous KG this is rarely true: hierarchical, lexical, and purely statistical edges do not, by themselves, define an admissible directed state transformation, and traversing them propagates signal along paths that are not biologically meaningful. We present the Biological Information Flow Ontology (BIFO), a graph-agnostic specification of which directed transformations are biologically admissible for computable information flow. BIFO defines fourteen entity classes, a taxonomy of flow classes organized around the backbone G+CH [->]RNA [->]P [->]PW [->]C [->]PH [->]DS, a set of admissibility constraints, and a two-level CURIE mapping that can be applied without schema-specific code to any graph whose identifiers and predicates are resolvable through, or extendable to, the BIFO mapping tables. A four-step conditioning protocol converts a raw property graph into a conditioned propagation graph in which only admissible, direction-aware edges remain. We provide a reference implementation on the Data Distillery Knowledge Graph (DDKG); conditioning a cohort-independent, gene-anchored subgraph as a BIFO substrate of 33.6 million edges retained 23.7 million (70.7%) as BIFO-classified relationships, cleanly separating 13.3 million propagating mechanistic edges from 10.5 million retained-but-non-propagating observational associations, and confirming that pathway concepts are configured as scoring accumulation endpoints for BIFO-PPR pathway scoring. BIFO is an admissibility specification for computable propagation of signal over knowledge graphs. It is released as an open specification with versioned mapping tables and tooling, providing a reusable substrate for biologically interpretable, direction-aware analysis of biomedical knowledge graphs.

bioinformatics↗

bioRxiv2026

The Common Fund Data Ecosystem (CFDE)

The NIH Common Fund Data Ecosystem (CFDE) integrates data resources from 18 NIH Common Fund programs for discovery and integrative analysis. These programs generate valuable but heterogeneous datasets that can be difficult to discover, access, and reuse. CFDE aims to provide a collaborative, community-built infrastructure that links and enriches Common Fund programs. We describe the evolution, structure, and core technologies of CFDE, including practical approaches that support submission, integration, visualization, and public release of multimodal data. Training programs and workforce initiatives lower barriers to adoption. CFDE has devised solutions to critical issues facing cross-program initiatives, including data scale and heterogeneity, dataset integration, and long-term sustainability. We demonstrate the utility of linking Common Fund resources through integrative tools and cross-dataset queries to yield insights that would otherwise be infeasible. Collectively, CFDE shows that a standards-driven, federated approach enhances and unifies cross-disciplinary resources, fostering collaboration and data-driven discovery.

scientific communication and education↗

bioRxiv2025

Echolocation calls of some bat species in western Uganda

With the rise of accessible recording technology, passive acoustic monitoring can be an affordable and rapid way to assess species richness, even when individual animals cannot be captured due to regulatory or practical obstacles. Motivated by the relative lack of data and in partnership with the local populace, we recorded echolocation calls of freely-flying bats across six locations in rural western Uganda using opportunistic passive acoustic recordings. Frequency-modulated echolocation calls were recorded at all six locations, while constant-frequency calls were recorded only at sites near entrances to caves. Preliminary species identifications were made using Kaleidoscope Pro, habitat distribution maps for Uganda, and by reference to published work. We make our acoustic recordings publicly available to serve as a resource for further explorations of the richness of bat species in Uganda.

zoology↗

bioRxiv2025

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data

The Data Distillery Knowledge Graph (DDKG) is a framework for semantic integration and querying of biomedical data across domains. Built for the NIH Common Fund Data Ecosystem, it supports translational research by linking clinical and experimental datasets in a unified graph model. Clinical standards such as ICD-10, SNOMED, and DrugBank are integrated through UMLS, while genomics and basic science data are structured using ontologies and standards such as HPO, GENCODE, Ensembl, STRING, and ClinVar. The DDKG uses a property graph architecture based on the UBKG infrastructure and supports ontology-based ingestion, identifier normalization, and graph-native querying. The system is modular and can be extended with new datasets or schema modules. We demonstrate its utility for informatics queries across eight use cases, including regulatory variant analysis, tissue-specific expression, biomarker discovery, and cross-species variant prioritization. The DDKG is accessible via a public interface, a programmatic API, and downloadable builds for local use.

bioinformatics↗

bioRxiv2022

Non-invasive auditory brainstem responses to FM sweeps in awake big brown bats

We introduce two EEG techniques, one based on conventional monopolar electrodes and one based on a novel tripolar electrode, to record for the first time auditory brainstem responses (ABRs) from the scalp of unanesthetized, unrestrained big brown bats. Stimuli were frequency-modulated (FM) sweeps varying in sweep direction, sweep duration, and harmonic structure. As expected from previous invasive ABR recordings, upward-sweeping FM signals evoked larger amplitude responses (peak-to-trough amplitude in the latency range of 3-5 ms post-stimulus onset) than downward-sweeping FM signals. Scalp-recorded responses displayed amplitudelatency trading effects as expected from invasive recordings. These two findings validate the reliability of our noninvasive recording techniques. The feasibility of recording noninvasively in unanesthetized, unrestrained bats will energize future research uncovering electrophysological signatures of perceptual and cognitive processing of biosonar signals in these animals, and allows for better comparison with ABR data from echolocating cetaceans, where invasive experiments are heavily restricted. Because experiments can be repeated in the same animal over time without confounds of stress or anesthesia, our technique requires fewer captures of wild bats, thus helping to preserve natural populations and addressing the goal of reducing animal numbers used for research purposes.

neuroscience↗

bioRxiv2022

Connexin36 expression in the cochlear nucleus complex of the echolocating bat, Eptesicus fuscus

Gap junctions and electrical synapses in the central nervous system are associated with rapid temporal processing and coincidence detection. Using histology, immunohistochemistry, and in situ hybridization, we investigated the distribution of Connexin36 (Cx36), a protein that comprises neuronal gap junctions, throughout the cochlear nucleus complex of the echolocating big brown bat, Eptesicus fuscus, a species exhibiting extreme behavioral sensitivity to minute temporal changes in ultrasonic echoes. For comparison, we visualized Cx36 expression in the cochlear nucleus of transgenic Cx36 reporter mice, species that hear ultrasound but do not echolocate. We observed Cx36 expression in the anteroventral and dorsal cochlear nucleus, with more limited expression in the posteroventral cochlear nucleus, of both species. Several different morphological cell types were labeled, including globular and spherical bushy, octopus, stellate, and fusiform cells. Labeled Cx36 puncta were also observed. Cx36 expression in the bat was spread throughout a relatively smaller area of the cochlear nucleus than in the mouse, even though the bat cochlear nucleus is hypertrophied. In the bat, the anteroventral cochlear nucleus showed higher percent area label than the dorsal cochlear nucleus, with a trend towards the opposite result in the mouse. The presence of gap junctions appears to be a conserved feature of the mammalian cochlear nucleus and thus not uniquely tied to the temporal hyperacuity of echolocation.

neuroscience↗

bioRxiv2021

Amplitude discrimination is predictably affected by echo frequency filtering in wideband echolocating bats

Big brown bats emit wideband frequency modulated (FM) ultrasonic pulses for echolocation. They perceive target range from echo delay and target size from echo amplitude. Their sounds contain two prominent down-sweeping harmonic sweeps (FM1, [~]55-22 kHz; FM2, [~]100-55 kHz), which are affected differently by propagation out to the target and back to the bat. FM2 is attenuated more than FM1 during propagation. Bats anchor target ranging asymmetrically on the low frequencies in FM1, while FM2 only contributes if FM1 is present as well. These experiments tested whether the bats ability to discriminate target size from the amplitude of echoes is affected by selectively attenuating upper or lower frequencies. Bats were trained to perform an echo amplitude discrimination task with virtual echo targets 83 cm away. While echo delay was held constant and echo amplitude was varied to estimate threshold, either lower FM1 frequencies or higher FM2 frequencies were attenuated. The results parallel effects seen in echo delay experiments; bats performance was significantly poorer when the lower frequencies in echoes were attenuated, compared to higher frequencies. The bats ability to distinguish between virtual targets at the same simulated range from echoes arriving at the same delay indicates a high level of focused attention for perceptual isolation of one and suppression of the other.

animal behavior and cognition↗

bioRxiv2020

A comprehensive computational model of animal biosonar signal processing

Computational models of animal biosonar seek to identify critical aspects of echo processing responsible for the superior, real-time performance of echolocating bats and dolphins in target tracking and clutter rejection. The Spectrogram Correlation and Transformation (SCAT) model replicates aspects of biosonar imaging in both species by processing wideband biosonar sounds and echoes with auditory mechanisms identified from experiments with bats. The model acquires broadband biosonar broadcasts and echoes, represents them as time-frequency spectrograms using parallel bandpass filters, translates the filtered signals into ten parallel amplitude threshold levels, and then operates on the resulting time-of-occurrence values at each frequency to estimate overall echo range delay. It uses the structure of the echo spectrum by depicting it as a series of local frequency nulls arranged regularly along the frequency axis of the spectrograms after dechirping them relative to the broadcast. Computations take place entirely on the timing of threshold-crossing events for each echo relative to threshold-events for the broadcast. Threshold-crossing times take into account amplitude-latency trading, a physiological feature absent from conventional digital signal processing. Amplitude-latency trading transposes the profile of amplitudes across frequencies into a profile of time-registrations across frequencies. Target shape is extracted from the spacing of the objects individual acoustic reflecting points, or glints, using the mutual interference pattern of peaks and nulls in the echo spectrum. These are merged with the overall range-delay estimate to produce a delay-based reconstruction of the objects distance as well as its glints. Clutter echoes indiscriminately activate multiple parts in the null-detecting system, which then produces the equivalent glint-delay spacings in images, thus blurring the overall echo-delay estimates by adding spurious glint delays to the image. Blurring acts as an anticorrelation process that rejects clutter intrusion into perceptions. Author summaryBats and dolphins use their biological sonar as a versatile, high-resolution perceptual system that performs at levels desirable in man-made sonar or radar systems. To capture the superior real-time capabilities of biosonar so they can be imported into the design of new man-made systems, we developed a computer model of the sonar receiver used by echolocating bats and dolphins. Our intention was to discover the processing methods responsible for the animals ability to find and identify targets, guide locomotion, and prevent classic types of sonar or radar interference that hamper performance of man-made systems in complex, rapidly-changing surroundings. We have identified several features of the ears, hearing, time-frequency representation, and auditory processing that are critical for organizing echo-processing methods and display manifested in the animals perceptions.

neuroscience↗