Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ABSTRACT: Machine learning-assisted prediction mixed carbon source with high denitrification potential

PROVIDER: PRJNA1070611 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR27760778_1.fastq.gz	Fastqsanger.gz
	SRR27760778_2.fastq.gz	Fastqsanger.gz
	SRR27760780_1.fastq.gz	Fastqsanger.gz
	SRR27760780_2.fastq.gz	Fastqsanger.gz
	SRR27760781_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 18

Similar Datasets

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (tubulointerstitium)

Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.

2013-08-06 | E-GEOD-47184 | biostudies-arrayexpress

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (glomeruli)

2013-08-06 | E-GEOD-47183 | biostudies-arrayexpress

Machine learning-based prediction of the activity and specificity of Cas9 variants in gene editing

Project description:Machine learning-based prediction of the activity and specificity of Cas9 variants in gene editing

2024-08-22 | GSE231840 | GEO

The Prediction of Anastomotic Insufficiency Risk After Colorectal Surgery (PANIC) Study

Project description:The Prediction of Anastomotic Insufficiency risk after Colorectal surgery (PANIC) study aims to establish a machine-learning-based application that allows for accurate preoperative prediction of patients at risk for anastomotic insufficiency after colon and colorectal surgery.

| 2385155 | ecrin-mdr-crc

Homo sapiens

Project description:Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

| PRJNA140059 | ENA

Project description:Development of machine learning models for the prediction of the skin sensitization potential of cosmetic compounds

| PRJNA1148804 | ENA

Project description:A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets

| PRJNA939699 | ENA

High-throughput proteomic analysis of rectal cancer for prediction of non-complete response after concurrent chemoradiation therapy

Project description:Colorectal cancer (CRC) is the third most common lethal malignancy in Korea and worldwide. Rectal cancer patients occupy about 30% of CRC patients, and the majority of rectal cancer patients had locally advanced disease at diagnosis. The standard treatment of locally advanced rectal cancer (LARC) is neoadjuvant radiation therapy with concurrent chemotherapy (CCRT) followed by total mesorectal excision (TME). This multidisciplinary team approach improved local tumor control and overall survival of rectal cancer patients. High throughput proteomic analysis and machine learning algorithm identify DUOX2 (dual oxidase 2) as a novel biomarker for prediction of non-complete response after concurrent chemoradiation therapy for rectal cancer.High throughput proteomic analysis and machine learning algorithm identify DUOX2 (dual oxidase 2) as a novel biomarker for prediction of non-complete response after concurrent chemoradiation therapy for rectal cancer.

2023-03-11 | PXD023302 | Pride

Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration

Project description:Improved prediction of bacterial CRISPRi guide efficiency from depletion screens through mixed-effect machine learning and data integration

| PRJNA807710 | ENA

Detection of SARS-CoV-2 in nasal swabs using MALDI-MS

Project description:Detection of SARS-CoV-2 using RT–PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT–PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.

2020-09-11 | PXD021388 | Pride

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