Our research aims to understand the manner in which living systems respond to chemical agents. We are particularly interested in understanding how the response to chemical exposure is affected by genetic and environmental factors and how this translates into altered capabilities at the molecular level. Our research interests include:
We develop semantic technologies to make sense out of the vast amounts of existing biomedical and clinical data. We build a biomedical knowledge graph using Semantic Web technologies, including the Web Ontology Language (OWL) to build ontologies, and the Resource Description Framework (RDF) to build Linked Data. Our research interests include:
We develop community standards to make it easier to navigate the knowledge landscape. We work with the following organizations:
Bio2RDF is an open source project to generate Linked Data for the Life Sciences.
The Semanticscience Integrated Ontology (SIO) provides a simple, integrated ontology of types and relations for rich description of objects, processes and their attributes. It provides foundational support for a number of Linked Data projects and SADI semantic web services.
A trusty URI is a verifiable, immutable, and permanent digital artifact identifier for Linked Data.
A guideline for the description of datasets that meets key functional requirements, uses existing vocabularies, and is expressed using the Resource Description Framework. Produced by the W3C Semantic Web for Health Care and Life Sciences [HCLS] Interest Group.
PhenomeDrug matches animal model phenotypes with drug effects to identify human and model drug targets.
We generate comparative tables for human-based entity reconciliation and conduct an empirical link analysis of the Bio2RDF data.
The FAIRport project focuses on the specification of minimal standards to drive the principles of Findability, Accessibility, Interoperability, and Reusability (FAIR).
Moving Evidence Based Medicine into a Linked Data model. In collaboration with the Cochrane Group, we are working to annotate Systematic Reviews in order to provide a semantic representation of the patients, interventions, comparators, and outcomes described by their meta-research.
The massive growth in experimental data and scientific literature poses a substantial challenge in finding relevant evidence to support or dispute a scientific hypothesis. HyQue is a semantic web application that leverages ontologies, Linked Data, and SADI services to evaluate scientific hypotheses.
The INTREPID project merges the Stanford Translational Research Integrated Database Environment (STRIDE) Clinical EHR database with the Bio2RDF Linked Open Data ecosystem using Semantic Web technologies. This unique system enriches Clinical EHR data with Biomedical Knowledge bases to find underlying patterns within the clinical patient data.
The VERITABLE project focuses on providing mechanistic plausibility validation mechanisms of multi-drug combinations in the context of drug safety / pharmacovigilance using available biomedical linked data resources and computational techniques.
Ebola virus (EBOV) is a lethal human pathogen responsible for causing Ebola virus disease (EVD), a severe hemorrhagic fever and has a cumulative death rate of 41% in the ongoing epidemic in West Africa. We developed an Ebola-KB dashboard to facilitates interactive access to an Ebola virus-centered Knowledge Base (Ebola-KB) developed using Semantic Web Technologies which integrates data from several open data sources, namely NCBI Gene, InterPro, Gene Ontology, PubMed, DrugBank, PDB and KEGG.
Juan M. Banda
Visiting Undergraduate Researcher
Sep 2014 - Dec 2014
Visiting Postdoctoral Researcher
Jan 2015 - Apr 2015
Visiting Graduate Researcher
Feb 2015 - May 2015
1265 Welch Rd, Stanford University, Stanford, CA.
+1 (650) 497-3260