Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

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

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

PROVIDER: PRJNA422201 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Similar Datasets

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

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

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

| PRJNA422203 | ENA

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

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

2017-12-13 | GSE108004 | GEO

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

Project description:We demonstrate a promising approach to identify robust molecular markers for targeted treatment of acute myeloid leukemia. We show that our method outperforms several state-of-the-art approaches in identifying molecular markers replicated in validation data and predicting drug sensitivity accurately. Finally, we identify SMARCA4 as a marker and driver of sensitivity to topoisomerase II inhibitors, mitoxantrone and etoposide, in AML by showing that cell lines transduced to have high SMARCA4 expression reveal dramatically increased sensitivity to these agents.

2017-11-30 | GSE107465 | GEO

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

2017-12-13 | GSE108003 | GEO

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

Project description: Not available

| S-EPMC5752671 | biostudies-literature

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

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Precision medicine strategies for inflammatory bowel disease

Project description:Combining explainable machine learning, demographic and multi-omic data to identify precision medicine strategies for inflammatory bowel disease

| PRJEB43220 | ENA

The Brazilian Initiative on Precision Medicine (BIPMed)

Project description:The development of precision medicine strategies requires prior knowledge of the genetic background of the target population. However, despite the availability of data from admixed Americans within large reference population databases, we cannot use these data as a surrogate for that of the Brazilian population. This lack of transferability is mainly due to differences between ancestry proportions of Brazilian and other admixed American populations. To address the issue, a coalition of research centres created the Brazilian Initiative on Precision Medicine (BIPMed), an initiative of five Research Innovation and Dissemination Centers (RIDCs) supported by FAPESP.

2020-08-22 | GSE156652 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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