Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Cholera Antibiotic REsistance in Bangladesh: big data mining and machine learning to improve diagnostics and treatment selection

PROVIDER: PRJNA1185448 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	SRR31323044_1.fastq.gz	Fastqsanger.gz
	SRR31323044_2.fastq.gz	Fastqsanger.gz
	SRR31322884_1.fastq.gz	Fastqsanger.gz
	SRR31322884_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 5

Similar Datasets

Project description:Machine learning for digital diagnostics of antimicrobial resistance

| PRJNA1106824 | ENA

Vibrio Cholera from Bangladesh

Project description:Vibrio Cholera from Bangladesh

| PRJEB18714 | ENA

Project description:Mining Bangladesh Biogold

| PRJNA1055760 | ENA

Machine Learning Identifies Signatures of Macrophage Reactivity and Tolerance

Project description:A continuum of macrophage polarization states is essential tissue homeostasis. We used machine learning approaches to identify universally relevant definition of macrophage polarization states and create a predictive framework for developing macrophage-targeted precision diagnostics and therapeutics. CCDC88A was identified as a key gene in the continuum state clusters that is essential for the tolerant polarization state.

2022-05-20 | GSE203423 | GEO

Project description:GBE machine learning data

| PRJNA729223 | ENA

Project description:ABE-igRNA-machine learning

| PRJNA942901 | ENA

Energetic portrait of the amyloid beta transition state

Project description:In this study we use massively parallel combinatorial mutagenesis, a kinetic selection assay, and machine learning to better understand the nucleation reaction of amyloid beta (Aꞵ42), the protein that aggregates as a hallmark of Alzheimer’s disease (AD) and is mutated to cause familial AD.

2024-07-26 | GSE269461 | GEO

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

| PRJNA422201 | ENA

Single cell RNA-seq and machine learning reveal novel subpopulations and regulatory sequences in low-grade inflammatory monocytes

Project description:We report the application of single-cell RNA sequencing(scRNA-seq) in mouse monocyte cells by integrating scRNA-seq, transcriptionfactor binding motifs, and ATAC-seq data using machine learning. We generated scRNA-seqdata from mouse monocytes treated with PBS, SD-LPS, 4-PBA, and SD-LPS + 4-PBA tounderstand the gene regulatory networks of monocytes under the low-grade inflammatorycondition and the mechanism of action for 4-PBA. We find two novelsubpopulations of monocyte cells in response to SD-LPS. We show that 4-PBApotently reprograms an anti-inflammatory monocyte phenotype and masks theeffects of subclinical low dose LPS. Together with TF binding motifs and ATAC-seqdata, a machine learning method, using guided, regularized random forest (GRRF)and feature selection was developed to select the best candidate TFs that areinvolved in the activation of monocytes within different clusters. Our results suggestthat our new machine learning method can select candidate regulatory genes aspotential targets for developing new therapeutics against low-gradeinflammation.

2021-01-29 | GSE160450 | GEO

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]

Project description:A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia [array]

| PRJNA420193 | ENA