Projects
The High-performance Integrated Virtual Environment (HIVE) platform
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HIVE is a cloud-based environment optimized for the storage and analysis of extra-large data, such as biomedical data, clinical data, next-generation sequencing (NGS) data, mass spectrometry files, confocal microscopy images, post-market surveillance data, medical recall data, and many others. HIVE provides secure web access for authorized users to deposit, retrieve, annotate and compute on Big Data, and analyze the outcomes using web user interfaces. More here.
BioCompute Objects (BCO)
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The BioCompute is FDA funded project to establish a framework for community-based development of standards for harmonization of High-throughput Sequencing (HTS), standardization of data formats, promotion of interoperability, and bioinformatics verification protocols. The BioCompute Object (BCO) was developed in the High-throughput Sequencing Computational Standards for Regulatory Sciences (HTS-CSRS) initiative in the BioCompute Objects Portal (BOP), a web portal to serve as a collaborative ground to encourage a dialogue to facilitate interoperability between different bioinformatic pipelines, industries, and developers. HIVE capabilities have been leveraged to support the development of the BCO. The BCO is versatile and adaptable to other common HTS analysis platforms. More here.
GlyGen
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GlyGen (gly-glycobiology; gen-information), is an advanced glycoinformatics resource developed to facilitate discovery in basic and translational glycobiology research along with enhancing the integration of multidisciplinary information from diverse resources. GlyGen includes knowledge about molecular, biophysical and functional properties of glycans, genes, and proteins organized in pathways and ontologies, plus a rapidly growing body of biological big data related to cancer mutation and expression. GlyGen adopts an innovative user-driven approach for implementing, prioritizing and knowledge disseminating tools to address the questions and needs of glycobiology community.
PredictMod
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PredictMod is an application designed to predict the outcome of an intervention prior to a patient initiating treatment. Our goal is to provide clinicians with a powerful decision making tool that enhances clinical understanding of patient-level data. The PredictMod platform utilizes machine learning tools and complex datasets based on electronic health records, gut microbiome, and -omics data to forecast patient outcomes, often in response to treatment for a particular condition. While our primary condition of interest is Prediabetes, the tool is designed to be used for a variety of conditions, interventions, and data types.
GW-FEAST
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The GW Federated Ecosystems for Analytics and Standardized Technologies (GW-FEAST) project is part of the ARPA-H FEAST performer team initiative that includes academic and industry partners. The goal of the ARPA-H performer teams is “to create bridges across data silos to make health data more accessible and usable”.
Biomarker Partnership
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The Biomarker Partnership is a CFDE sponsored project to develop a knowledgebase that will organize and integrate biomarker data from different public sources. The data will be connected to contextual information to show a novel systems-level view of biomarkers. The motivation for this project is to improve the harmonization and organization of biomarker data. This will be done by mapping biomarkers from public sources to, and across, CF data elements. This mapping will bridge knowledge across multiple DCCs and biomedical disciplines.