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Found 11 papers, 5 papers with code
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.
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.
RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules
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.
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions
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.
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.
Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome
Currently, the only definitive treatment of preeclampsia is delivery of the placenta, which is central to the pathogenesis of the disease.
A method for comparing multiple imputation techniques: a case study on the U.S. National COVID Cohort Collaborative
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.
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.
A Biomedically oriented automatically annotated Twitter COVID-19 Dataset
As part of the 2021 Biomedical Linked Annotation Hackathon, we release our dataset of over 120 million automatically annotated tweets for biomedical research purposes.
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.
