Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph‐based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these “knowledge graphs” (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open‐access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open‐source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object‐oriented classification and graph‐oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

Calibrating an agent-based model of longitudinal human activity patterns using the Consolidated Human Activity Database

Patterns of human behavior over extended periods of time are important for characterizing human exposure to hazardous chemicals. Because longitudinal behavior patterns for an individual are difficult to obtain, exposure-assessors have characterized such patterns by linking daily records from multiple individuals. In an earlier publication, we developed an alternative strategy that was based on agent-based simulation modeling. Specifically, we created a software program, Agent-Based Model of Human Activity Patterns (ABMHAP), that generates year-long longitudinal behavior patterns. In this paper, we both calibrate and evaluate ABMHAP using human behavior data from the U.S. Environmental Protection Agency's Consolidated Human Activity Database (CHAD). We use the longitudinal data (data on individuals' activities over multiple days) in CHAD to parameterize ABMHAP, and we use single-day behavior data from CHAD to evaluate ABMHAP predictions. We evaluate ABMHAP's ability to simulate sleeping, eating, commuting, and working (or attending school) for four populations: working adults, nonworking adults, school-age children, and preschool children. The results demonstrate that ABMHAP, when parameterized with empirical data, can capture both interindividual and intraindividual variation in behaviors in different types of individuals. We propose that simulating annual activity patterns via ABMHAP may allow exposure-assessors to characterize exposure-related behavior in ways not possible with traditional survey methods.

Simulating exposure-related behaviors using agent-based models embedded with needs-based artificial intelligence

Exposure to a chemical is a critical consideration in the assessment of risk, as it adds real-world context to toxicological information. Descriptions of where and how individuals spend their time are important for characterizing exposures to chemicals in consumer products and in indoor environments. Herein we create an agent-based model (ABM) that simulates longitudinal patterns in human behavior. By basing the ABM upon an artificial intelligence (AI) system, we create agents that mimic human decisions on performing behaviors relevant for determining exposures to chemicals and other stressors. We implement the ABM in a computer program called the Agent-Based Model of Human Activity Patterns (ABMHAP) that predicts the longitudinal patterns for sleeping, eating, commuting, and working. We then show that ABMHAP is capable of simulating behavior over extended periods of time. We propose that this framework, and models based on it, can generate longitudinal human behavior data for use in exposure assessments.

Evidence for non-axial A/P patterning in the nonneural ectoderm of Xenopus and zebrafish pregastrula embryos

Recent studies in early Xenopus and zebrafish embryos have demonstrated that posteriorizing, non-axial signals arising from outside the organizer (or shield) contribute to A/P patterning of the neural axis, in contradiction to the classical Spemann model in which such signals were proposed to be solely organizer derived. Our studies on the early expression of the transcription factors GATA-2 and 3 in both Xenopus and zebrafish nonneural ectoderm lend support to the existence of such non-axial signaling in the A/P axis. Thus we find that the earliest expression of GATA-2 and 3 is located in nonneural ectoderm and is strongly patterned in a graded manner along the A/P axis, being high anteriorly and absent from the most posterior regions. This results by early neurula stages in three broad zones: an anterior region which is positive for both GATA-2 and 3, a middle region which is positive for GATA-2 alone and a posterior region in which neither gene is expressed. These regions correspond to head, trunk and tail ectoderm and may represent the beginnings of functional segmentation of nonneural ectoderm, as suggested in the concept of the 'ectomere'. We find that A/P patterning of GATA expression in nonneural ectoderm may occur as early as late blastula/early gastrula stages. We investigate which posteriorizing signals might contribute to such distinct non axial ectodermal patterning in the A/P axis and provide evidence that both FGF and a Wnt family member contribute towards the final A/P pattern of GATA expression in nonneural ectoderm.