Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Integrating Microbial Profiling and Machine Learning for Inference of Drowning Sites: A Forensic Investigation in the Northwest River

PROVIDER: PRJNA1181121 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR31201000_1.fastq.gz	Fastqsanger.gz
	SRR31201000_2.fastq.gz	Fastqsanger.gz
	SRR31201100_1.fastq.gz	Fastqsanger.gz
	SRR31201100_2.fastq.gz	Fastqsanger.gz
	SRR31201200_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 1090

Similar Datasets

Project description:Inference of drowning sites using aquatic bacterial composition and random forest algorithm

| PRJNA962514 | ENA

Project description:Research on microbiome for forensic diagnosis of drowning

| PRJNA637372 | ENA

Project description:Metagenomic 16S forensic investigation

| PRJNA646601 | ENA

Wild dolphin PBL analysis

Project description:To determine what can the transcriptome tell us about populations of free-ranging bottlenose dolphins, Tursiops truncatus. Keywords: health assessment A total of 151 individuals were sampled from 4 different geographic location in U.S. waters between June of 2003 and June of 2006. Of the 151 dolphins, 59 were from Charleston, 35 from Indian River Lagoon, FL, 32 from Sarasota Bay, FL and 25 from St. Josephs Bay FL. Total RNA extracted from blood leukocytes of wild dolphins was analyzed and different sets of genes were used as classifiers in a machine learning approach, Artificial Neural Networks (ANN).

2010-05-05 | E-GEOD-10617 | biostudies-arrayexpress

Project description:Diatom Diversity in kharoon River: A Metagenomic Approach For Drowning Case Investigation

| PRJNA1258736 | ENA

Project description:ABE-igRNA-machine learning

| PRJNA942901 | ENA

Project description:GBE machine learning data

| PRJNA729223 | 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

Genome-wide profiling and validation for forensic tissue-specific DNA methylation markers

Project description:Forensic body fluid identification is important for crime scene reconstruction. We used Illumina HumanMethylation 450K bead array containing over the 450,000 CpG sites in 16 body fluid samples to find novel DNA methylation marker for forensic body fluid identification. Examination of genome-wide DNA methylation profiling in 16 body fluid samples

2014-07-31 | E-GEOD-55734 | biostudies-arrayexpress

Short Stature, GH stimulation testing and RNASeq

Project description:Case series of children and adolescents undergoing growth hormone stimulation testing for investigation of short stature. The aim of this study was to identify whether a machine learning approach utilising gene expression data could predict which short children would test positive for GHD and which would not.

2021-12-15 | GSE190502 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data