Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference | Zendy

Pierre-Cyril Aubin-Frankowski | Zendy; JeanPhilippe Vert | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Gene regulation inference from single-cell RNA-seq data with linear differential equations and velocity inference

Author(s) -

Pierre-Cyril Aubin-Frankowski,

JeanPhilippe Vert

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa576

Subject(s) - inference , computer science , ordinary differential equation , code (set theory) , data mining , field (mathematics) , matlab , gene regulatory network , differential (mechanical device) , algorithm , differential equation , artificial intelligence , biology , mathematics , gene , gene expression , mathematical analysis , biochemistry , set (abstract data type) , aerospace engineering , pure mathematics , engineering , programming language , operating system

Single-cell RNA sequencing (scRNA-seq) offers new possibilities to infer gene regulatory network (GRNs) for biological processes involving a notion of time, such as cell differentiation or cell cycles. It also raises many challenges due to the destructive measurements inherent to the technology.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research