Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Correlating Gene Signatures with Outcome to find Features and Drugs Relevant to Cancer

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE59409 | GEO | 2018/01/01

SECONDARY ACCESSION(S): PRJNA255255

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Correlating Gene Signatures with Outcome to find Features and Drugs Relevant to Cancer

Project description:Correlating Gene Signatures with Outcome to find Features and Drugs Relevant to Cancer

| PRJNA255255 | ENA

Conformational Sampling of Macrocyclic Drugs in Different Environments: Can We Find the Relevant Conformations?

Project description:Conformational flexibility is a major determinant of the properties of macrocycles and other drugs in beyond rule of 5 (bRo5) space. Prediction of conformations is essential for design of drugs in this space, and we have evaluated three tools for conformational sampling of a set of 10 bRo5 drugs and clinical candidates in polar and apolar environments. The distance-geometry based OMEGA was found to yield ensembles spanning larger structure and property spaces than the ensembles obtained by MOE-LowModeMD (MOE) and MacroModel (MC). Both MC and OMEGA but not MOE generated different ensembles for polar and apolar environments. All three conformational search methods generated conformers similar to the crystal structure conformers for 9 of the 10 compounds, with OMEGA performing somewhat better than MOE and MC. MOE and OMEGA found all six conformers of roxithromycin that were identified by NMR in aqueous solutions, whereas only OMEGA sampled the three conformers observed in chloroform. We suggest that characterization of conformers using molecular descriptors, e.g., the radius of gyration and polar surface area, is preferred to energy- or root-mean-square deviation-based methods for selection of biologically relevant conformers in drug discovery in bRo5 space.

| S-EPMC6173504 | biostudies-literature

Using Transcriptional Signatures to Find Cancer Drivers with LURE.

Project description:Cancer genome projects have produced multidimensional datasets on thousands of samples. Yet, depending on the tumor type, 5-50% of samples have no known driving event. We introduce a semi-supervised method called Learning UnRealized Events (LURE) that uses a progressive label learning framework and minimum spanning analysis to predict cancer drivers based on their altered samples sharing a gene expression signature with the samples of a known event. We demonstrate the utility of the method on the TCGA Pan-Cancer Atlas dataset for which it produced a high-confidence result relating 59 new connections to 18 known mutation events including alterations in the same gene, family, and pathway. We give examples of predicted drivers involved in TP53, telomere maintenance, and MAPK/RTK signaling pathways. LURE identifies connections between genes with no known prior relationship, some of which may offer clues for targeting specific forms of cancer. Code and Supplemental Material are available on the LURE website: https://sysbiowiki.soe.ucsc.edu/lure.

| S-EPMC6924983 | biostudies-literature

Self-organized cerebral organoids with human specific features predict effective drugs to combat Zika virus infection

Project description:This SuperSeries is composed of the SubSeries listed below.

2017-10-11 | GSE104279 | GEO

Functional screening implicates miR-371-3p and peroxiredoxin 6 in reversible tolerance to cancer drugs

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2016-08-01 | E-GEOD-83122 | biostudies-arrayexpress

Stromal contribution to expression signatures of colorectal cancer

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2014-05-01 | E-GEOD-56710 | biostudies-arrayexpress

Stromal-Based Signatures for the Classification of Gastric Cancer

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2016-01-08 | E-GEOD-76630 | biostudies-arrayexpress

Anti-cancer drugs curaxins target the 3D genome organization

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-02-18 | GSE122463 | GEO

Large B-Cell Lymphomas in Pediatric and Young Adults Display Clinically Relevant Molecular Features Distinguishable from Adult Counterparts

Project description:This SuperSeries is composed of the SubSeries listed below.

2019-11-28 | GSE128294 | GEO

Clinical Utility of Circulating MicroRNA Signatures for Breast Cancer Diagnosis

Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series

2013-09-06 | E-GEOD-42128 | biostudies-arrayexpress

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