Main Page: Difference between revisions
Jump to navigation
Jump to search
No edit summary |
mNo edit summary |
||
Line 4: | Line 4: | ||
<div id="ggw-topbanner" style="clear:both; position:relative; box-sizing:border-box; width:100%; margin:1.2em 0 6px; min-width:47em; border:1px solid #ddd; background-color:#f9f9f9; color:#000;"> | <div id="ggw-topbanner" style="clear:both; position:relative; box-sizing:border-box; width:100%; margin:1.2em 0 6px; min-width:47em; border:1px solid #ddd; background-color:#f9f9f9; color:#000;"> | ||
<div style="margin:0.4em; text-align:center;"> | <div style="margin:0.4em; text-align:center;"> | ||
<div style="font-size:160%; padding:.1em;">Welcome to the HIVE Lab Wiki | <div style="font-size:160%; padding:.1em;">Welcome to the HIVE Lab (Mazumder Research Group) Wiki</div> | ||
<div style="font-size:100%;">The HIVE Lab group is involved in developing the High-performance Integrated Virtual Environment (HIVE) which is a collaborative project between the CBER Food and Drug Administration (FDA) and Dr. Raja Mazumder's team at The George Washington University (GW). This effort aims to integrate various high throughput data analysis tools into the HIVE platform. | <div style="font-size:100%;">The HIVE Lab group is involved in developing the High-performance Integrated Virtual Environment (HIVE) which is a collaborative project between the CBER Food and Drug Administration (FDA) and Dr. Raja Mazumder's team at The George Washington University (GW). This effort aims to integrate various high throughput data analysis tools into the HIVE platform. | ||
In addition to the HIVE platform, the group is involved in developing standards for bioinformatics communication via BioCompute Objects, knowledgebases for glycoinformatics (GlyGen), cancer research (BiomarkerKB, OncoMX, BioMuta, BioXpress), and microbiome analysis via the GutFeeling Knowledge Base (GFKB). The lab leverages knowledge graphs and advanced ML/AI technologies, including large language models, to harmonize, map, and uncover valuable insights from clinical data, omics datasets, knowledgebases, and scientific publications.</div> | In addition to the HIVE platform, the group is involved in developing standards for bioinformatics communication via BioCompute Objects, knowledgebases for glycoinformatics (GlyGen), cancer research (BiomarkerKB, OncoMX, BioMuta, BioXpress), and microbiome analysis via the GutFeeling Knowledge Base (GFKB). The lab leverages knowledge graphs and advanced ML/AI technologies, including large language models, to harmonize, map, and uncover valuable insights from clinical data, omics datasets, knowledgebases, and scientific publications.</div> |
Latest revision as of 21:42, 22 November 2024
Projects
Template:Main
HIVE team projects fall into two major categories:
1) Developing infrastructure for biomedical data analysis.
2) Using that infrastructure to integrate and mine the data for knowledge.
Publications
Template:Main
The HIVE team has a variety of peer-reviewed publications, book chapters, posters, brochures, and multimedia available for the public.
People
Template:Main
This page contains details about the HIVE Lab team.
Tool Resources
Template:Main
There are a variety of bioinformatic tool resources developed by our team.
Dataset Resources
Template:Main
There are a variety of bioinformatic dataset resources integrated by our team.
Opportunities
Template:Main
Interested in joining our team? Learn about our team.
External links
- Official