Feb 5, 2020

Multitask learning for Transformers with application to large-scale single-cell transcriptomes

bioRxiv
Minxing Pang, Jesper Tegner

Abstract

Recent progress in machine learning provides competitive methods for bioinformatics in many traditional topics, such as transcriptomes sequence and single-cell analysis. However, discovering the biomedical correlation of cells that are present across large-scale data sets remains challenging. Our attention-based neural network module with 300 million parameters is able to capture biological knowledge in a data-driven way. The module contains high-quality embedding, taxonomy analysis and similarity measurement. We tested the model on Mouse Brain Atlas, which consists of 160,000 cells and 25,000 genes. Our module obtained some interesting findings that have been verified by biologists and got better performance when benchmarked against autoencoder and principal components analysis.

  • References
  • Citations

References

  • We're still populating references for this paper, please check back later.
  • References
  • Citations

Citations

  • This paper may not have been cited yet.

Mentioned in this Paper

Statistical Study
Transcriptome
Proteomics
MRNA Maturation
Analysis
Embedding
Biomedicine
Epigenetic Process
Genes
Research Activities

Related Feeds

Bioinformatics in Biomedicine (Preprints)

Bioinformatics in biomedicine incorporates computer science, biology, chemistry, medicine, mathematics and statistics. Discover the latest preprints on bioinformatics in biomedicine here.

BioRxiv & MedRxiv Preprints

BioRxiv and MedRxiv are the preprint servers for biology and health sciences respectively, operated by Cold Spring Harbor Laboratory. Here are the latest preprint articles (which are not peer-reviewed) from BioRxiv and MedRxiv.