Computational modeling of cellular structures using conditional deep generative networks

Abstract

Cellular function is closely related to the localizations of its sub-structures. It is, however, challenging to experimentally label all sub-cellular structures simultaneously in the same cell. This raises the need of building a computational model to learn the relationships among these sub-cellular structures and use reference structures to infer the localizations of other structures.

Document Details

Document Type
Pub Defense Publication
Publication Date
Nov 06, 2018
Source ID
10.1093/bioinformatics/bty923

Entities

People

  • Hao Yuan
  • Lei Cai
  • Shaoting Zhang
  • Shuiwang Ji
  • Xia Hu
  • Zhengyang Wang

Organizations

  • Defense Advanced Research Projects Agency
  • National Science Foundation
  • Texas A&M University
  • University of North Carolina at Charlotte
  • Washington State University

Tags

Fields of Study

  • Biology

Readers

  • Molecular Genetics
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Neural Networks