Publication — IRIC

Factorized embeddings learns rich and biologically meaningful embedding spaces using factorized tensor decomposition.

The recent development of sequencing technologies revolutionized our understanding of the inner workings of the cell as well as the way disease is treated. A single RNA sequencing (RNA-Seq) experiment, however, measures tens of thousands of parameters simultaneously. While the results are information rich, data analysis provides a challenge. Dimensionality reduction methods help with this task by extracting patterns from the data by compressing it into compact vector representations.

Publication date
July 1, 2020
Principal Investigators
Trofimov A, Cohen JP, Bengio Y, Perreault C, Lemieux S
PubMed reference
Bioinformatics 2020;36(Supplement_1):i417-i426
PubMed ID
32657403
Affiliation
Department of Computer Science, Univerity of Montreal, Québec, Canada.