Research Projects and Collaborations

01/01/2012 to 01/01/2016
Semantic LAMHDI: Linking diseases to model organism resources

(NIH – University of Oregon)

PI: M. Haendel  

Our understanding of human disease is vitally informed by the use of model systems to investigate disease mechanisms and therapies. Our goal of this work is to facilitate the identification of models for disease research, make better use of existing model organisms and in vitro resources and data about them, and provide the ability to uncover new relationships between disease, phenotypes and genes that will further our understanding of disease. To this end we will : 1) Enable computation of candidate disease models based on semantic similarity of phenotypes using imported and aligned phenotype data from humans and model organisms. We will include expression data to refine search of phenotypes based on presence of expression within a particular anatomical location and/or genotype. 2) Expand semantic linkage between diseases and in vitro model systems, including resources such as biospecimens and cell lines. 3) Create a discovery tool to refine searches and to uncover novel relationships between diseases, model organisms, and in vitro resources using genetic, pathway, and phenotype relationships.

01/01/2012 to 01/01/2016
Quantifying Electronic Medical Records Usability to Improve Clinical Workflow

(VA) 5R01HS021290-02
PI: Agha 

Electronic Medical Record (EMR) have the potential to improve quality of care but to date, there is little research to quantify the effect of EMR as barrier or facilitator of quality.  Design and implementation of EMR’s, should not be viewed as an end in distracting them with burdensome documentation or Human-Computer Interaction (HCI) limitations.  In an ideal patient-centered process, the provider would focus primarily on the patient.  However in the time-constrained framework of office consultations, the EMR competes for the provider’s focus of attention.  It is desirable to understand the degree to which EMR task complexity imposes a cognitive burden on providers.

01/01/2012 to 01/01/2015
Sarcoidosis and A1AT Genomics & Informatics Center

(NIH) 1U01HL112707-01

PI: N. Kaminski 

Alpha-1 antitrypsin deficiency (A1AT), an autosomal recessive genetic disease that is associated with a variable risk of COPD, and Sarcoidosis, a systemic disease characterized by the formation of granulomatous lesions especially in the lungs, liver, skin, and lymph nodes that leads to a dramatically heterogeneous set of clinical manifestations, differ in etiology and clinical presentation but share a variable and unpredictable course.  To improve disease classification, facilitate biomarker discovery and accelerate advent of novel therapy an integrative approach that combines the results of clinical studies with molecular phenotyping results is required.  The Sarcoidosis and A1AT Genomics and Informatics Center (SAGIC) will facilitate this process by coordinating clinical center activities; conducting transcriptome and microbiome analyses of the samples obtained by the Clinical Centers; analyzing and integrating data; and providing data to the community via an interactive web portal.  

01/01/2011 to 01/01/2014
Ontology-based integration of human studies data

(NIH) 3U01DE020050-03S1
PI:  L. Shapiro  

The goal of the Ontology of Craniofacial Development and Malformation (OCDM) is to provide detailed anatomic, developmental, and phenotypic descriptions of craniofacial anatomy.  Development of this ontology will be driven by defined use cases describing researcher needs. In collaboration with OCDM and FaceBase partners, we will be developing and evaluating interactive tools that will leverage the models contained in this ontology to help users annotate, explore, query, and interpret diverse data sets describing craniofacial development.

01/01/2011 to 01/01/2014
Interactive Search and Review of Clinical Records with Multi-layered Semantic Annotation

(NIH) 1R01LM010964

PI:  W. Chapman  

The use of retrospective review of free-text medical reports to identify potential participants in research studies is a known limiting factor in many clinical studies.  By combining natural language processing approaches with information visualization techniques, we will build interactive tools that will visually display information extracted from clinical records, allowing users to both explore results to identify potential participants and to provide feedback to fine-tune information extraction.

01/01/2009 to 01/01/2014
FaceBase Management and Coordination Hub

(NIH) University of Iowa 1 U01 DE020057-01

PI: Marazita 

The FaceBase hub ( is a data portal for craniofacial development.  When complete, the hub will support research involving heterogeneous data including genetics, genomics, and multiple imaging modalities. Challenges include integrating diverse data sets and developing highly interactive data exploration environments that will help researchers find non-obvious connections between disparate data items.