PLX167899

GSE132829: Distinguishing smoking related lung disease phenotypes via imaging and molecular features

  • Organsim human
  • Type RNASEQ
  • Target gene
  • Project ARCHS4

Using unsupervised machine learning applied to computed tomography-based imaging characteristics we have found three distinct phenotypes of lung disease in two large cohorts of ever-smokers and have identified their molecular correlates. SOURCE: Adam,C,Gower (agower@bu.edu) - Division of Computational Biomedicine Boston University School of Medicine

View on GEOView in Pluto

Key Features

Enhance your research with our curated data sets and powerful platform features. Pluto Bio makes it simple to find and use the data you need.

Learn More

14K+ Published Experiments

Access an extensive range of curated bioinformatics data sets, including genomic, transcriptomic, and proteomic data.

Easy Data Import

Request imports from GEO or TCGA directly within Pluto Bio. Seamlessly integrate external data sets into your workflow.

Advanced Search Capabilities

Utilize powerful search tools to quickly find the data sets relevant to your research. Filter by type, disease, gene, and more.

Analyze and visualize data for this experiment

Use Pluto's intuitive interface to analyze and visualize data for this experiment. Pluto's platform is equipped with an API & SDKs, making it easy to integrate into your internal bioinformatics processes.

Read about post-pipeline analysis

View QC data and experiment metadata

View quality control data and experiment metadata for this experiment.

Request import of other GEO data

Request imports from GEO or TCGA directly within Pluto Bio.

Chat with our Scientific Insights team