Viral communities detected from three large grassland soil metagenomes with historically different precipitation moisture regimes.
Filter results
Content type
Tags
- (-) Machine Learning (4)
- Virology (14)
- Viruses (10)
- Health (8)
- Soil Microbiology (8)
- Virus (8)
- Omics (6)
- Omics-LHV Project (6)
- Differential Expression Analysis (5)
- Gene expression profile data (5)
- Immune Response (5)
- Multi-Omics (5)
- PerCon SFA (5)
- Time Sampled Measurement Datasets (5)
- Autoimmunity (4)
- Biomarkers (4)
- Homo sapiens (4)
- Ions (4)
- Mass Spectrometry (4)
- Mass spectrometry data (4)
- Microbiome (4)
- Molecular Profiling (4)
- Nanoparticles (4)
- sequencing (4)
- Synthetic (4)
- Synthetic Biology (4)
- Type 1 Diabetes (4)
- Fungi (3)
- Genomics (3)
- Spectroscopy (3)
A major challenge in biotechnology and biomanufacturing is the identification of a set of biomarkers for perturbations and metabolites of interest. Here, we develop a data-driven, transcriptome-wide approach to rank perturbation-inducible genes from time-series RNA sequencing data for the discovery...
Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host...
Category
Last updated on 2024-02-11T22:41:43+00:00 by LN Anderson PNNL DataHub NIAID Program Project: Modeling Host Responses to Understand Severe Human Virus Infections, Multi-Omic Viral Dataset Catalog Collection Background The National Institute of Allergy and Infectious Diseases (NIAID) "Modeling Host...
Category
Datasets
45
Machine learning is a core technology that is rapidly advancing within type 1 diabetes (T1D) research. Our Human Islet Research Network (HIRN) grant is studying early cellular response initiating β cell stress in T1D through the generation of heterogenous low- and high-throughput molecular...
Datasets
3
The Environmental Determinants of Diabetes in the Young (TEDDY) study is searching for factors influencing the development of type 1 diabetes (T1D) in children. Research has shown that there are certain genes that correlate to higher risk of developing T1D, but not all children with these genes...
Datasets
1
The Diabetes Autoimmunity Study in the Young (DAISY) seeks to find environmental factors that can trigger the development of type 1 diabetes (T1D) in children. DAISY follows children with high-risk of developing T1D based on family history or genetic markers. Genes, diets, infections, and...
Datasets
1