Evidence map›Paper›PMID 42005385›Full record

ArticlePatterns (New York, N.Y.)2026

DestinyNet: A deep-learning framework for cell-fate analysis from lineage-tracing single-cell RNA sequencing data.

Zuozhu Liu, Songtao Jiang, Tianxiang Hu, Ziwei Xue, Yuhui Chen, Dengfeng Ruan, Haoxiang Xia, Yang Feng, Jin Hao, Wei Li and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Zuozhu LiuStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China.
Songtao JiangStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China.
Tianxiang HuStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China.
Ziwei XueDepartment of Rheumatology and Immunology of the Second Affiliated Hospital, Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Hangzhou 310003, China.
Yuhui ChenDepartment of Rheumatology and Immunology of the Second Affiliated Hospital, Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Hangzhou 310003, China.
Dengfeng RuanDepartment of Rheumatology and Immunology of the Second Affiliated Hospital, Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Hangzhou 310003, China.
Haoxiang XiaDepartment of Rheumatology and Immunology of the Second Affiliated Hospital, Centre of Biomedical Systems and Informatics of Zhejiang University-University of Edinburgh Institute, Zhejiang University School of Medicine, Hangzhou 310003, China.
Yang FengStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China.
Jin HaoStomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310016, China.
Wei LiDepartment of Breast and Thyroid Surgery, The Second Affiliated Hospital, Hengyang Medical School, University of South China, Hunan 421001, China.
Joey Tianyi ZhouCFAR, Agency for Science, Technology and Research, Singapore, Singapore.
Bing FangShanghai Ninth People's Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai 200011, China.
Jian WuCollege of Computer Science and Technology, Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Hangzhou, Zhejiang 310027, China.
Wanlu LiuCollege of Computer Science and Technology, Zhejiang University-University of Illinois Urbana-Champaign Institute, Zhejiang University, Hangzhou, Zhejiang 310027, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unraveling cell-development dynamics, including lineage commitment, differentiation, and disease progression, is fundamental to biology. Despite advances in single-cell omics and barcoding technologies, comprehensive frameworks for accurate, robust, and scalable cell-fate analysis using lineage-tracing single-cell RNA sequencing (LT-scSeq) data remain limited. We introduce DestinyNet, a multi-task deep-learning framework addressing three key challenges: (1) fate clustering, integrating fate and cell-type information; (2) fate flow, depicting dynamic pseudotime trajectories with fate information; and (3) fate prediction, identifying early-stage cell-fate biases. DestinyNet enables end-to-end cell representation learning through cell-relation triplets and is robust across various LT-scSeq data types, including static, cumulative, and dynamic barcoding with single or multiple time points. Experiments on diverse datasets, including hematopoiesis differentiation and fibroblast reprogramming (

Indexed as

cell fate predictiondeep learningmulti-task learningsingle-cell lineage tracingtrajectory inference

Identifiers

PMID42005385
PMCPMC13083642

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.