Evidence map›Paper›PMID 38125297›Full record

ArticleComputational and structural biotechnology journal2024

SCInter: A comprehensive single-cell transcriptome integration database for human and mouse.

Jun Zhao, Yuezhu Wang, Chenchen Feng, Mingxue Yin, Yu Gao, Ling Wei, Chao Song, Bo Ai, Qiuyu Wang, Jian Zhang and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Computational and structural biotechnology journal, 2024. 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. Article
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

12 authors.

Jun ZhaoSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Yuezhu WangSchool of Artificial Intelligence, Jilin University, Changchun 130012, China.
Chenchen FengSchool of Computer, University of South China, Hengyang, Hunan 421001, China.
Mingxue YinThe First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Yu GaoSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Ling WeiInstitute of Medical Innovation and Research, Peking University Third Hospital, Beijing 100191, China.
Chao SongThe First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Bo AiSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Qiuyu WangThe First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.
Jian ZhangSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Jiang ZhuSchool of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China.
Chunquan LiThe First Affiliated Hospital, Cardiovascular Lab of Big Data and Imaging Artificial Intelligence, Hengyang Medical School, University of South China, Hengyang, Hunan, 421001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Single-cell RNA sequencing (scRNA-seq), which profiles gene expression at the cellular level, has effectively explored cell heterogeneity and reconstructed developmental trajectories. With the increasing research on diseases and biological processes, scRNA-seq datasets are accumulating rapidly, highlighting the urgent need for collecting and processing these data to support comprehensive and effective annotation and analysis. Here, we have developed a comprehensive

Indexed as

Cell heterogeneityCell to cell communicationMulti-method automatic cell-type annotationSingle cell integration database

Identifiers

PMID38125297
PMCPMC10731004

What OpenQuestion 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 OpenQuestion graph.