Author

Nan Papili Gao

SIB Swiss Institute of BioinformaticsORCID 0000-0002-6097-5331Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Bioinformatics· 2 Systems Biology· 1

Signature Assays, Organisms & Methods

Click to filter · or open tag

Assay:scRNA-seq· 3 Approach:statistical model· 3 Research Focus:gene regulatory network inference· 2 Approach:machine learning (classical)· 2 Research Focus:trajectory inference· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Organism:human· 1

Frequent Co-Authors

Rudiyanto GunawaniD· 3 shared Olivier GandrilloniD· 2 shared Andràs PáldiiD· 1 shared S. M. Minhaz Ud-Dean· 1 shared Tao FangiD· 1 shared Thomas Hartmann· 1 shared Ulysse HerbachiD· 1 shared

Activity by Year

2017-2023 · peak 1/yr

Co-Author Collaboration Network

7 top collaborators · 11 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersBioinformatics

Rudiyanto GunawanORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

Nan Papili Gao, Olivier Gandrillon, Andràs Páldi, Ulysse Herbach, Rudiyanto Gunawan

The paper uses CALISTA to reconstruct transcriptional uncertainty landscapes from single-cell data, revealing universal patterns in cell differentiation trajectories.

F1000Research · Apr 20, 2023Peer-reviewedBioinformatics

CALISTA: Clustering And Lineage Inference in Single-Cell Transcriptional Analysis

Nan Papili Gao, Thomas Hartmann, Tao Fang, Rudiyanto Gunawan

CALISTA is a single-cell transcriptomic analysis tool that improves lineage reconstruction and cell ordering compared to existing methods.

Frontiers in Bioengineering and Biotechnology · Feb 3, 2020Peer-reviewedBioinformatics

SINCERITIES: Inferring gene regulatory networks fromtime-stamped single cell transcriptional expression profiles

Nan Papili Gao, S. M. Minhaz Ud-Dean, Olivier Gandrillon, Rudiyanto Gunawan

SINCERITIES infers gene regulatory networks from time-stamped single-cell expression data, showing better accuracy than existing methods in simulated and experimental datasets.

Bioinformatics · Sep 13, 2017Peer-reviewedSystems Biology