Biology subjects

Callahan, T. J.

Publications and source records attributed to Callahan, T. J..

10 recordsLinked to original sources

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↗

bioRxiv2025

Interpretable Machine Learning Uncovers Structural Determinants of Wnt-Wls Binding Specificity from Extended Atomistic Simulations

The Wnt protein family plays a critical role in cell development, with each Wnt protein interacting differently with the Wls membrane protein through distinct binding residues. A direct comparison and elucidation of the molecular mechanisms underlying Wnt-Wls binding across the diverse Wnt family remain challenging, owing to variations in sequence length and amino acid composition among Wnt proteins, which can affect their binding affinity and trafficking efficiency via Wls. Here we combine extended atomistic molecular dynamics simulations with supervised machine learning to elucidate binding specificity among four Wnt proteins, selected based on experimental structure availability and scientific relevance. We implement a local structure alignment algorithm to enable cross-system matching and comparison of residue interactions, and we apply a two-stage clustering strategy to reduce feature redundancy and facilitate robust feature selection. After training a Random Forest classifier that achieved high predicting accuracy, our feature importance analysis reveals both previously known and novel key residue pairs responsible for distinguishing among the Wnt systems. Our findings highlight that the binding specificity across different systems arises from the distributed nature of interactions across the protein binding surface and demonstrate how interpretable machine learning can effectively uncover crucial biophysical interactions. Importantly, our integrated strategy is generalizable to other systems and provides a data-driven approach for analyzing protein- protein interactions and guiding experimental validation or therapeutic targeting.

biochemistry↗

bioRxiv2025

SAIR: Enabling deep learning for protein-ligand lnteractions with a synthetic structural dataset

AO_SCPLOWBSTRACTC_SCPLOWAccurate prediction of protein-ligand binding affinities remains a cornerstone problem in drug discovery. While binding affinity is inherently dictated by the 3D structure and dynamics of protein-ligand complexes, current deep learning approaches are limited by the lack of high-quality experimental structures with annotated binding affinities. To address this limitation, we introduce the Struc-turally Augmented IC50 Repository (SAIR), the largest publicly available dataset of protein-ligand 3D structures with associated activity data. The dataset com-prises 5, 244, 285 structures across 1, 048, 857 unique protein-ligand systems, cu-rated from the ChEMBL and BindingDB databases, which were then computa-tionally folded using the Boltz-1x model. We provide a comprehensive charac-terization of the dataset, including distributional statistics of proteins and ligands, and evaluate the structural fidelity of the folded complexes using PoseBusters. Our analysis reveals that approximately 3% of structures exhibit physical anoma-lies, predominantly related to internal energy violations. As an initial demon-stration, we benchmark several binding affinity prediction methods, including empirical scoring functions (Vina, Vinardo), a 3D convolutional neural network (Onionnet-2), and a graph neural network (AEV-PLIG). While machine learning-based models consistently outperform traditional scoring function methods, nei-ther exhibit a high correlation with ground truth affinities, highlighting the need for models specifically fine-tuned to synthetic structure distributions. This work provides a foundation for developing and evaluating next-generation structure and binding-affinity prediction models and offers insights into the structural and phys-ical underpinnings of protein-ligand interactions. The dataset can be found at https://www.sandboxaq.com/sair.

biochemistry↗

bioRxiv2024

The Unified Phenotype Ontology (uPheno): A framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

bioinformatics↗

bioRxiv2023

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

The use of biomedical knowledge graphs (BMKG) for knowledge representation and data integration has increased drastically in the past several years due to the size, diversity, and complexity of biomedical datasets and databases. Data extraction from a single dataset or database is usually not particularly challenging. However, if a scientific question must rely on integrative analysis across multiple databases or datasets, it can often take many hours to correctly and reproducibly extract and integrate data towards effective analysis. To overcome this issue, we created Petagraph, a large-scale BMKG that integrates biomolecular data into a schema incorporating the Unified Medical Language System (UMLS). Petagraph is instantiated on the Neo4j graph platform, and to date, has fifteen integrated biomolecular datasets. The majority of the data consists of entities or relationships related to genes, animal models, human phenotypes, drugs, and chemicals. Quantitative data sets containing values from gene expression analyses, chromatin organization, and genetic analyses have also been included. By incorporating models of biomolecular data types, the datasets can be traversed with hundreds of ontologies and controlled vocabularies native to the UMLS, effectively bringing the data to the ontologies. Petagraph allows users to analyze relationships between complex multi-omics data quickly and efficiently.

bioinformatics↗

bioRxiv2022

Causal feature selection using a knowledge graph combining structured knowledge from the biomedical literature and ontologies: a use case studying depression as a risk factor for Alzheimer's disease

BackgroundCausal feature selection is essential for estimating effects from observational data. Identifying confounders is a crucial step in this process. Traditionally, researchers employ content-matter expertise and literature review to identify confounders. Uncontrolled confounding from unidentified confounders threatens validity, conditioning on intermediate variables (mediators) weakens estimates, and conditioning on common effects (colliders) induces bias. Additionally, without special treatment, erroneous conditioning on variables combining roles introduces bias. However, the vast literature is growing exponentially, making it infeasible to assimilate this knowledge. To address these challenges, we introduce a novel knowledge graph (KG) application enabling causal feature selection by combining computable literature-derived knowledge with biomedical ontologies. We present a use case of our approach specifying a causal model for estimating the total causal effect of depression on the risk of developing Alzheimers disease (AD) from observational data. MethodsWe extracted computable knowledge from a literature corpus using three machine reading systems and inferred missing knowledge using logical closure operations. Using a KG framework, we mapped the output to target terminologies and combined it with ontology-grounded resources. We translated epidemiological definitions of confounder, collider, and mediator into queries for searching the KG and summarized the roles played by the identified variables. We compared the results with output from a complementary method and published observational studies and examined a selection of confounding and combined role variables in-depth. ResultsOur search identified 128 confounders, including 58 phenotypes, 47 drugs, 35 genes, 23 collider, and 16 mediator phenotypes. However, only 31 of the 58 confounder phenotypes were found to behave exclusively as confounders, while the remaining 27 phenotypes played other roles. Obstructive sleep apnea emerged as a potential novel confounder for depression and AD. Anemia exemplified a variable playing combined roles. ConclusionOur findings suggest combining machine reading and KG could augment human expertise for causal feature selection. However, the complexity of causal feature selection for depression with AD highlights the need for standardized field-specific databases of causal variables. Further work is needed to optimize KG search and transform the output for human consumption. HighlightsO_LIKnowledge of causal variables and their roles is essential for causal inference. C_LIO_LIWe show how to search a knowledge graph (KG) for causal variables and their roles. C_LIO_LIThe KG combines literature-derived knowledge with ontology-grounded knowledge. C_LIO_LIWe design queries to search the KG for confounder, collider, and mediator roles. C_LIO_LIKG search reveals variables in these roles for depression and Alzheimers disease. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=127 SRC="FIGDIR/small/500549v4_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1c3f417org.highwire.dtl.DTLVardef@1ce1703org.highwire.dtl.DTLVardef@148d645org.highwire.dtl.DTLVardef@eb55e7_HPS_FORMAT_FIGEXP M_FIG C_FIG Statement of SignificanceO_ST_ABSProblemC_ST_ABSExtensive knowledge is required to identify confounders for estimating total effects in observational settings. The literature is too vast for humans to process. Bias may remain from not adjusting on unknown confounders or erroneously adjusting on a collider or mediator. What is already knownStructured literature-derived knowledge is often useful but is noisy. What this papers addsWe present a biomedical knowledge graph linking literature-derived and ontology-ground knowledge of biochemical processes with definitions of clinical disease. We search the KG to distill a model for estimating the (total) effect of depression on Alzheimers disease from observational data.

systems biology↗

bioRxiv2022

The Bioregistry: Unifying the Identification of Biomedical Entities through an Integrative, Open, Community-driven Metaregistry

The standardized identification of biomedical entities is a cornerstone of interoperability, reuse, and data integration in the life sciences. Several registries have been developed to catalog resources maintaining identifiers for biomedical entities such as small molecules, proteins, cell lines, and clinical trials. However, existing registries have struggled to provide sufficient coverage and metadata standards that meet the evolving needs of modern life sciences researchers. Here, we introduce the Bioregistry, an integrative, open, community-driven metaregistry that synthesizes and substantially expands upon 23 existing registries. The Bioregistry addresses the need for a sustainable registry by leveraging public infrastructure and automation, and employing a progressive governance model centered around open code and open data to foster community contribution. The Bioregistry can be used to support the standardized annotation of data, models, ontologies, and scientific literature, thereby promoting their interoperability and reuse. The Bioregistry can be accessed through https://bioregistry.io and its source code and data are available under the MIT and CC0 Licenses at https://github.com/biopragmatics/bioregistry.

bioinformatics↗

bioRxiv2020

KG-COVID-19: a framework to produce customized knowledge graphs for COVID-19 response

Integrated, up-to-date data about SARS-CoV-2 and coronavirus disease 2019 (COVID-19) is crucial for the ongoing response to the COVID-19 pandemic by the biomedical research community. While rich biological knowledge exists for SARS-CoV-2 and related viruses (SARS-CoV, MERS-CoV), integrating this knowledge is difficult and time consuming, since much of it is in siloed databases or in textual format. Furthermore, the data required by the research community varies drastically for different tasks - the optimal data for a machine learning task, for example, is much different from the data used to populate a browsable user interface for clinicians. To address these challenges, we created KG-COVID-19, a flexible framework that ingests and integrates biomedical data to produce knowledge graphs (KGs) for COVID-19 response. This KG framework can also be applied to other problems in which siloed biomedical data must be quickly integrated for different research applications, including future pandemics. BIGGER PICTUREAn effective response to the COVID-19 pandemic relies on integration of many different types of data available about SARS-CoV-2 and related viruses. KG-COVID-19 is a framework for producing knowledge graphs that can be customized for downstream applications including machine learning tasks, hypothesis-based querying, and browsable user interface to enable researchers to explore COVID-19 data and discover relationships.

bioinformatics↗

bioRxiv2020

A Framework for Automated Construction of Heterogeneous Large-Scale Biomedical Knowledge Graphs

MotivationAlthough knowledge graphs (KGs) are used extensively in biomedical research to model complex phenomena, many KG construction methods remain largely unable to account for the use of different standardized terminologies or vocabularies, are often difficult to use, and perform poorly as the size of the KG increases in scale. We introduce PheKnowLator (Phenotype Knowledge Translator), a novel KG framework and fully automated Python 3 library explicitly designed for optimized construction of semantically-rich, large-scale biomedical KGs. To demonstrate the functionality of the framework, we built and evaluated eight different parameterizations of a large semantic KG of human disease mechanisms. PheKnowLator is available at: https://github.com/callahantiff/PheKnowLator.

bioinformatics↗

bioRxiv2019

Applying knowledge-driven mechanistic inference totoxicogenomics

Government regulators and others concerned about toxic chemicals in the environment hold that a mechanistic, causal explanation of toxicity is strongly preferred over a statistical or machine learning-based prediction by itself. Elucidating a mechanism of toxicity is, however, a costly and time-consuming process that requires the participation of specialists from a variety of fields, often relying on animal models. We present an innovative mechanistic inference framework (MechSpy), which can be used as a hypothesis generation aid to narrow the scope of mechanistic toxicology analysis. MechSpy generates hypotheses of the most likely mechanisms of toxicity, by combining a semantically-interconnected knowledge representation of human biology, toxicology and biochemistry with gene expression time series on human tissue. Using vector representations of biological entities, MechSpy seeks enrichment in a manually-curated list of high-level mechanisms of toxicity, represented as biochemically- and causally-linked ontology concepts. Besides predicting the canonical mechanism of toxicity for many well-studied compounds, we experimentally validated some of our predictions for other chemicals without an established mechanism of toxicity. This framework can be modified to include additional mechanisms of toxicity, and is generalizable to other types of mechanisms of human biology.\n\nAuthor summarySeveral recent computational methods have displayed excellent performance in predicting toxicity outcomes [1-3] of chemicals. Yet, to our knowledge, there is to date no computational approach to generate mechanistic hypotheses to answer why these chemicals elicit a toxic response. There is great value in understanding the mechanism of toxicity for a chemical that appears to elicit an adverse response. Novel small molecule development is one example, where a chemical that failed initial toxicological screenings could be assessed to evaluate the actual mechanism of toxicity, greatly reducing research time and expenses on subsequent ones. The value of a mechanistic awareness of toxicity also applies to pharmacovigilance, when researching rare adverse effects of a drug in subsets of the population. The development of oncological chemotherapeutics is another example, where certain mechanisms of cytotoxicity can actually be desirable to eliminate different types of tumor cells. More importantly, the costs, time expenditure, and ethical concerns of toxicity animal models, make in vitro and in silico approaches an enticing alternative. We present a solution that uses a combination of gene expression assays and biomedical knowledge to address the gap of answering the why question.

bioinformatics↗