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Laboratory Information Management Software (LIMS)

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bizzApps
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Agri-Labs Information Systems (ALIS) LIMS
Desert Oasis Software Company LLC
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AIS LIMS
Analytical Information Systems (AIS), Ltd.
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Alis LIMS
Asystance
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ANALYS LMS
Inlog S.A.
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Analysis LIMS
Polisystem Informatica S.r.l.
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ApiMate LIMS
Interface Engineering Co Ltd.
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ApolloLIMS
Common Cents Systems
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ArrayHub LIMS
Integromics
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ASM-LIMS
ASM Soft, SL
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Scientific News
Changing the Biological Data Visualisation World
Scientists at TGAC, alongside European partners, have created a cutting-edge, open source community for the life sciences.
Searching Big Data Faster
Theoretical analysis could expand applications of accelerated searching in biology, other fields.
Imaging Software Could Speed Breast Cancer Diagnosis
Technology could improve access to diagnostic services in developing countries.
Data Mining DNA For Polycystic Ovary Syndrome Genes
A new Northwestern Medicine genome-wide association study of PCOS – the first of its kind to focus on women of European ancestry – has provided important new insights into the underlying biology of the disorder.
Firefly Protein Enables Visualization of Roots in Soil
A new imaging tool from a team led by Carnegie’s José Dinneny allows researchers to study the dynamic growth of root systems in soil, and to uncover the molecular signaling pathways that control such growth.
UEA Research Could Help Build Computers From DNA
New research from the University of East Anglia could one day help build computers from DNA.
Viral Comparisons
ORNL team applies genomics expertise to analyze, map virus sequence database.
Preserving Fleeting Digital Information with DNA
A team has demonstrated that DNA they encapsulated can preserve information for at least 2,000 years, and they’re now working on a filing system to make it easier to navigate.
TGAC Leads Development to Diminish Threat to Vietnam’s Most Important Crop
Advanced bioinformatics capabilities for next-generation rice genomics in Vietnam to aid precision breeding.
Mass Extinctions Can Accelerate Evolution
A computer science team at The University of Texas at Austin has found that robots evolve more quickly and efficiently after a virtual mass extinction modeled after real-life disasters such as the one that killed off the dinosaurs.
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