arXiv subjects

Tiffany J. Callahan

Publications and source records attributed to Tiffany J. Callahan.

14 recordsLinked to original sources

arXiv2026

Understanding Structural Representation in Foundation Models for Polymers

From the relative scarcity of training data to the lack of standardized benchmarks, the creation of effective foundation models for polymers faces significant and multi-faceted challenges. At the core, many of these issues are tied directly to the structural representation of polymers. Here, we present a chemical language foundation model built on using a SMILES-based polymer graph representation (CPG) that incorporates polymer architectural features and connectivity that are often missing in other line notations. This foundation model exhibited excellent performance on 30 different polymer property benchmark datasets. Critical evaluation of the developed representation against other variations in control experiments reveals this approach to be a robust method of representing polymers in language-based foundation models. These experiments also reveal a strong invariance of structural representations to small perturbations, with many variations of structural representation exceeding or equaling state-of-the-art (SOTA) performance. Surprisingly, SMILES representations which are chemically or semantically invalid also provided near or SOTA performance in several instances--underscoring an unexamined blind spot in the development of chemistry language models. Examination of error sources and attention maps for the evaluated structural representations corroborate the findings of the control experiments, highlighting the ability of the model to interpolate SMILES sequence space in a manner that is loosely congruent to chemical and architectural space for polymers. Overall, this work highlights the surprising robustness of chemistry language models to structural representation perturbations and identifies the conditions under which CPG representation provides meaningful advantages.

cond-mat.soft↗

arXiv2025

Agentic Mixture-of-Workflows for Multi-Modal Chemical Search

The vast and complex materials design space demands innovative strategies to integrate multidisciplinary scientific knowledge and optimize materials discovery. While large language models (LLMs) have demonstrated promising reasoning and automation capabilities across various domains, their application in materials science remains limited due to a lack of benchmarking standards and practical implementation frameworks. To address these challenges, we introduce Mixture-of-Workflows for Self-Corrective Retrieval-Augmented Generation (CRAG-MoW) - a novel paradigm that orchestrates multiple agentic workflows employing distinct CRAG strategies using open-source LLMs. Unlike prior approaches, CRAG-MoW synthesizes diverse outputs through an orchestration agent, enabling direct evaluation of multiple LLMs across the same problem domain. We benchmark CRAG-MoWs across small molecules, polymers, and chemical reactions, as well as multi-modal nuclear magnetic resonance (NMR) spectral retrieval. Our results demonstrate that CRAG-MoWs achieve performance comparable to GPT-4o while being preferred more frequently in comparative evaluations, highlighting the advantage of structured retrieval and multi-agent synthesis. By revealing performance variations across data types, CRAG-MoW provides a scalable, interpretable, and benchmark-driven approach to optimizing AI architectures for materials discovery. These insights are pivotal in addressing fundamental gaps in benchmarking LLMs and autonomous AI agents for scientific applications.

cs.AI↗

arXiv2024

Leveraging Chemistry Foundation Models to Facilitate Structure Focused Retrieval Augmented Generation in Multi-Agent Workflows for Catalyst and Materials Design

Molecular property prediction and generative design via deep learning models has been the subject of intense research given its potential to accelerate development of new, high-performance materials. More recently, these workflows have been significantly augmented with the advent of large language models (LLMs) and systems of autonomous agents capable of utilizing pre-trained models to make predictions in the context of more complex research tasks. While effective, there is still room for substantial improvement within agentic systems on the retrieval of salient information for material design tasks. Within this context, alternative uses of predictive deep learning models, such as leveraging their latent representations to facilitate cross-modal retrieval augmented generation within agentic systems for task-specific materials design, has remained unexplored. Herein, we demonstrate that large, pre-trained chemistry foundation models can serve as a basis for enabling structure-focused, semantic chemistry information retrieval for both small-molecules, complex polymeric materials, and reactions. Additionally, we show the use of chemistry foundation models in conjunction with multi-modal models such as OpenCLIP facilitate unprecedented queries and information retrieval across multiple characterization data domains. Finally, we demonstrate the integration of these models within multi-agent systems to facilitate structure and topological-based natural language queries and information retrieval for different research tasks.

cs.AI↗

arXiv2024

An Open-Source Knowledge Graph Ecosystem for the Life Sciences

Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these data, but researchers face significant integration challenges. Knowledge graphs (KGs) are used to model complex phenomena, and methods exist to construct them automatically. However, tackling complex biomedical integration problems requires flexibility in the way knowledge is modeled. Moreover, existing KG construction methods provide robust tooling at the cost of fixed or limited choices among knowledge representation models. PheKnowLator (Phenotype Knowledge Translator) is a semantic ecosystem for automating the FAIR (Findable, Accessible, Interoperable, and Reusable) construction of ontologically grounded KGs with fully customizable knowledge representation. The ecosystem includes KG construction resources (e.g., data preparation APIs), analysis tools (e.g., SPARQL endpoints and abstraction algorithms), and benchmarks (e.g., prebuilt KGs and embeddings). We evaluated the ecosystem by systematically comparing it to existing open-source KG construction methods and by analyzing its computational performance when used to construct 12 large-scale KGs. With flexible knowledge representation, PheKnowLator enables fully customizable KGs without compromising performance or usability.

cs.AI↗

arXiv2023

RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to the patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are continuously produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph encompassing biological knowledge about RNAs gathered from more than 50 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

cs.CE↗

arXiv2023

GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding

Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications comprise millions of nodes and billions of edges and are beyond the capabilities of current methods and software implementations. We present GRAPE, a software resource for graph processing and embedding that can scale with big graphs by using specialized and smart data structures, algorithms, and a fast parallel implementation of random walk-based methods. Compared with state-of-the-art software resources, GRAPE shows an improvement of orders of magnitude in empirical space and time complexity, as well as a competitive edge and node label prediction performance. GRAPE comprises about 1.7 million well-documented lines of Python and Rust code and provides 69 node embedding methods, 25 inference models, a collection of efficient graph processing utilities and over 80,000 graphs from the literature and other sources. Standardized interfaces allow seamless integration of third-party libraries, while ready-to-use and modular pipelines permit an easy-to-use evaluation of GRL methods, therefore also positioning GRAPE as a software resource to perform a fair comparison between methods and libraries for graph processing and embedding.

cs.LG↗

arXiv2023

Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality

Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.

cs.DB↗

arXiv2022

Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome

Preeclampsia is a leading cause of maternal and fetal morbidity and mortality. Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease. Transcriptional profiling of human placenta from pregnancies complicated by preeclampsia has been extensively performed to identify differentially expressed genes (DEGs). The decisions to investigate DEGs experimentally are biased by many factors, causing many DEGs to remain uninvestigated. A set of DEGs which are associated with a disease experimentally, but which have no known association to the disease in the literature are known as the ignorome. Preeclampsia has an extensive body of scientific literature, a large pool of DEG data, and only one definitive treatment. Tools facilitating knowledge-based analyses, which are capable of combining disparate data from many sources in order to suggest underlying mechanisms of action, may be a valuable resource to support discovery and improve our understanding of this disease. In this work we demonstrate how a biomedical knowledge graph (KG) can be used to identify novel preeclampsia molecular mechanisms. Existing open source biomedical resources and publicly available high-throughput transcriptional profiling data were used to identify and annotate the function of currently uninvestigated preeclampsia-associated DEGs. Experimentally investigated genes associated with preeclampsia were identified from PubMed abstracts using text-mining methodologies. The relative complement of the text-mined- and meta-analysis-derived lists were identified as the uninvestigated preeclampsia-associated DEGs (n=445), i.e., the preeclampsia ignorome. Using the KG to investigate relevant DEGs revealed 53 novel clinically relevant and biologically actionable mechanistic associations.

q-bio.GN↗

arXiv2022

A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative

Healthcare datasets obtained from Electronic Health Records have proven to be extremely useful to assess associations between patients' predictors and outcomes of interest. However, these datasets often suffer from missing values in a high proportion of cases and the simple removal of these cases may introduce severe bias. For these reasons, several multiple imputation algorithms have been proposed to attempt to recover the missing information. Each algorithm presents strengths and weaknesses, and there is currently no consensus on which multiple imputation algorithms works best in a given scenario. Furthermore, the selection of each algorithm parameters and data-related modelling choices are also both crucial and challenging. In this paper, we propose a novel framework to numerically evaluate strategies for handling missing data in the context of statistical analysis, with a particular focus on multiple imputation techniques. We demonstrate the feasibility of our approach on a large cohort of type-2 diabetes patients provided by the National COVID Cohort Collaborative (N3C) Enclave, where we explored the influence of various patient characteristics on outcomes related to COVID-19. Our analysis included classic multiple imputation techniques as well as simple complete-case Inverse Probability Weighted models. The experiments presented here show that our approach could effectively highlight the most valid and performant missing-data handling strategy for our case study. Moreover, our methodology allowed us to gain an understanding of the behavior of the different models and of how it changed as we modified their parameters. Our method is general and can be applied to different research fields and on datasets containing heterogeneous types.

cs.AI↗

arXiv2022

Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions

Pharmacokinetic natural product-drug interactions (NPDIs) occur when botanical natural products are co-consumed with pharmaceutical drugs. Understanding mechanisms of NPDIs is key to preventing adverse events. We constructed a knowledge graph framework, NP-KG, as a step toward computational discovery of pharmacokinetic NPDIs. NP-KG is a heterogeneous KG with biomedical ontologies, linked data, and full texts of the scientific literature, constructed with the Phenotype Knowledge Translator framework and the semantic relation extraction systems, SemRep and Integrated Network and Dynamic Reasoning Assembler. NP-KG was evaluated with case studies of pharmacokinetic green tea- and kratom-drug interactions through path searches and meta-path discovery to determine congruent and contradictory information compared to ground truth data. The fully integrated NP-KG consisted of 745,512 nodes and 7,249,576 edges. Evaluation of NP-KG resulted in congruent (38.98% for green tea, 50% for kratom), contradictory (15.25% for green tea, 21.43% for kratom), and both congruent and contradictory (15.25% for green tea, 21.43% for kratom) information. Potential pharmacokinetic mechanisms for several purported NPDIs, including the green tea-raloxifene, green tea-nadolol, kratom-midazolam, kratom-quetiapine, and kratom-venlafaxine interactions were congruent with the published literature. NP-KG is the first KG to integrate biomedical ontologies with full texts of the scientific literature focused on natural products. We demonstrate the application of NP-KG to identify pharmacokinetic interactions involving enzymes, transporters, and pharmaceutical drugs. We envision that NP-KG will facilitate improved human-machine collaboration to guide researchers in future studies of pharmacokinetic NPDIs. The NP-KG framework is publicly available at https://doi.org/10.5281/zenodo.6814507 and https://github.com/sanyabt/np-kg.

cs.AI↗

arXiv2021

A Simple Standard for Sharing Ontological Mappings (SSSOM)

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Are they associated in some other way? Such relationships between the mapped terms are often not documented, leading to incorrect assumptions and making them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Also, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. The Simple Standard for Sharing Ontological Mappings (SSSOM) addresses these problems by: 1. Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. 2. Defining an easy to use table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data standards. 3. Implementing open and community-driven collaborative workflows designed to evolve the standard continuously to address changing requirements and mapping practices. 4. Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases, and survey some existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable, and Reusable (FAIR). The SSSOM specification is at http://w3id.org/sssom/spec.

cs.DB↗

arXiv2021

A Biomedically oriented automatically annotated Twitter COVID-19 Dataset

The use of social media data, like Twitter, for biomedical research has been gradually increasing over the years. With the COVID-19 pandemic, researchers have turned to more nontraditional sources of clinical data to characterize the disease in near real-time, study the societal implications of interventions, as well as the sequelae that recovered COVID-19 cases present (Long-COVID). However, manually curated social media datasets are difficult to come by due to the expensive costs of manual annotation and the efforts needed to identify the correct texts. When datasets are available, they are usually very small and their annotations do not generalize well over time or to larger sets of documents. As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes. Incorporating best practices, we identify tweets with potentially high clinical relevance. We evaluated our work by comparing several SpaCy-based annotation frameworks against a manually annotated gold-standard dataset. Selecting the best method to use for automatic annotation, we then annotated 120 million tweets and released them publicly for future downstream usage within the biomedical domain.

cs.IR↗

arXiv2020

Hypernetwork Science: From Multidimensional Networks to Computational Topology

As data structures and mathematical objects used for complex systems modeling, hypergraphs sit nicely poised between on the one hand the world of network models, and on the other that of higher-order mathematical abstractions from algebra, lattice theory, and topology. They are able to represent complex systems interactions more faithfully than graphs and networks, while also being some of the simplest classes of systems representing topological structures as collections of multidimensional objects connected in a particular pattern. In this paper we discuss the role of (undirected) hypergraphs in the science of complex networks, and provide a mathematical overview of the core concepts needed for hypernetwork modeling, including duality and the relationship to bicolored graphs, quantitative adjacency and incidence, the nature of walks in hypergraphs, and available topological relationships and properties. We close with a brief discussion of two example applications: biomedical databases for disease analysis, and domain-name system (DNS) analysis of cyber data.

cs.DM↗

arXiv2019

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.

cs.AI↗