Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Machine Learning Approaches Identify Genes Containing Spatial Information from Single-Cell Transcriptomics Data

Phillipe Loher, Nestoras Karathanasis
doi: https://doi.org/10.1101/818393
Phillipe Loher
1Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Nestoras Karathanasis
1Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, 19107, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: nestor.karathanasis@gmail.com
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading
  • Supplementary Figures[supplements/818393_file03.docx]
  • Supplementary Table S1[supplements/818393_file04.xlsx]
Back to top
PreviousNext
Posted October 25, 2019.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Machine Learning Approaches Identify Genes Containing Spatial Information from Single-Cell Transcriptomics Data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Machine Learning Approaches Identify Genes Containing Spatial Information from Single-Cell Transcriptomics Data
Phillipe Loher, Nestoras Karathanasis
bioRxiv 818393; doi: https://doi.org/10.1101/818393
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Machine Learning Approaches Identify Genes Containing Spatial Information from Single-Cell Transcriptomics Data
Phillipe Loher, Nestoras Karathanasis
bioRxiv 818393; doi: https://doi.org/10.1101/818393

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4119)
  • Biochemistry (8828)
  • Bioengineering (6532)
  • Bioinformatics (23486)
  • Biophysics (11806)
  • Cancer Biology (9223)
  • Cell Biology (13336)
  • Clinical Trials (138)
  • Developmental Biology (7444)
  • Ecology (11425)
  • Epidemiology (2066)
  • Evolutionary Biology (15174)
  • Genetics (10453)
  • Genomics (14056)
  • Immunology (9188)
  • Microbiology (22200)
  • Molecular Biology (8823)
  • Neuroscience (47627)
  • Paleontology (351)
  • Pathology (1431)
  • Pharmacology and Toxicology (2493)
  • Physiology (3736)
  • Plant Biology (8090)
  • Scientific Communication and Education (1438)
  • Synthetic Biology (2225)
  • Systems Biology (6042)
  • Zoology (1254)