ReviewGenomics, proteomics & bioinformatics2019
Application of Computational Biology to Decode Brain Transcriptomes.
Review in Genomics, proteomics & bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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.
Who cites it
7 citing papers in PubMed.
- Transcriptome atlases of rat brain regions and their adaptation to diabetes resolution following gastrectomy in the Goto-Kakizaki rat.Molecular brain · 2025Article
- BrainCellR: A precise cell type nomenclature pipeline for comparative analysis across brain single-cell datasets.Computational and structural biotechnology journal · 2024Article
- scBrainMap: a landscape for cell types and associated genetic markers in the brain.Database : the journal of biological databases and curation · 2023Article
- A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases.PLoS biology · 2023Article
- Editorial: Advanced computational systems biology approaches for accelerating comprehensive research of the human brain.Frontiers in genetics · 2023Article
- Rapid Body-Wide Transcriptomic Turnover During Rhesus Macaque Perinatal Development.Frontiers in physiology · 2021Article
- Big Data and the Brain: Peeking at the Future.Genomics, proteomics & bioinformatics · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases, providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages. Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity. However, our knowledge of brain transcriptional characteristics remains very limited. With the immense efforts to generate high-quality brain transcriptome atlases, new computational approaches to analyze these high-dimensional multivariate data are greatly needed. In this review, we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field, which would aid in making new discoveries in brain development and disorders.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.