Evidence map›Paper›PMID 28608010›Full record

ArticleNeuroinformatics2017

Transcriptome Architecture of Adult Mouse Brain Revealed by Sparse Coding of Genome-Wide In Situ Hybridization Images.

Yujie Li, Hanbo Chen, Xi Jiang, Xiang Li, Jinglei Lv, Meng Li, Hanchuan Peng, Joe Z Tsien, Tianming Liu

Abstract read
In one paragraph

Article in Neuroinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.3field-weighted citation impact, top 40% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
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  4. Article
  5. Article
  6. Article
  7. Deep Neural Networks for In Situ Hybridization Grid Completion and Clustering.IEEE/ACM transactions on computational biology and bioinformatics
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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

9 authors at 3 institutions in 2 countries.

Yujie LiCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Hanbo ChenCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Xi JiangCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Xiang LiCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Jinglei LvCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA.
Meng LiBrain and Behavior Discovery Institute, Medical College of Georgia at Augusta University, Augusta, GA, USA.
Hanchuan PengAllen Institute for Brain Science, Seattle, WA, USA.
Joe Z TsienBrain and Behavior Discovery Institute, Medical College of Georgia at Augusta University, Augusta, GA, USA.
Tianming LiuCortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA. tliu@cs.uga.edu.
University of Georgia · USAugusta University · USAllen Institute for Brain Science · US

Funding

Assessing Large-scale Connectivities in Prenatal Cocaine Exposure Affected BrainsR01DA033393 · NIDA · UNIVERSITY OF GEORGIA · PI HU, XIAOPING P, LIU, TIANMING · 2012 to 2016
$1.6M
Assessing Large-scale Brain Connectivities in Mild Cognitive ImpairmentR01AG042599 · NIA · UNIVERSITY OF GEORGIA · PI LIU, TIANMING · 2013 to 2017
$1.4M
NIA NIH HHS R01 AG042599NIDA NIH HHS R01 DA033393
6 · The paper itself

Abstract

Highly differentiated brain structures with distinctly different phenotypes are closely correlated with the unique combination of gene expression patterns. Using a genome-wide in situ hybridization image dataset released by Allen Mouse Brain Atlas, we present a data-driven method of dictionary learning and sparse coding. Our results show that sparse coding can elucidate patterns of transcriptome organization of mouse brain. A collection of components obtained from sparse coding display robust region-specific molecular signatures corresponding to the canonical neuroanatomical subdivisions including fiber tracts and ventricular systems. Other components revealed finer anatomical delineation of domains previously considered homogeneous. We also build an open-access informatics portal that contains the detail of each component along with its ontology and expressed genes. This portal allows intuitive visualization, interpretation and explorations of the transcriptome architecture of a mouse brain.

Indexed as

Computational BiologyIn Situ HybridizationAlgorithmsAnimalsBrainDatabases, GeneticMiceMolecular Sequence AnnotationTranscriptomeData-driven gene clusteringMouse brain anatomySparse codingTranscriptome

Identifiers

PMID28608010
PMCPMC5540769
OpenAlexW2626197833

What OpenQuestion holds

Textmetadata
LicenceTDM
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.