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transcriptomic reference dataset  (Allen Institute for Brain Science)

 
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    Allen Institute for Brain Science transcriptomic reference dataset
    Transcriptomic Reference Dataset, supplied by Allen Institute for Brain Science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/transcriptomic+reference+dataset/transcriptomics+explorer/pm39353431-47-3-20
    Average 90 stars, based on 1 article reviews
    transcriptomic reference dataset - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Transcriptomic cell-type specificity of local cortical circuits.
    Article Snippet: We used a transcriptomic reference dataset composed of uninfected nuclei collected from V1 with cell-type annotations established according to the Allen Institute for Brain Science (AIBS) celltype taxonomy.5,26 This dataset has been previously shown to enable accurate characterization of rabies-infected cortical neurons.27 SingleR was used to conduct supervised label transfer annotation of cell types, and this annotation method was selected for its robustness against cell-type similarity and increased cell-type number compared with other computational methods.28 Input neurons were classified at the class level (glutamatergic, GABAergic, or non-neuronal), subclass level (e.g., L2/3, L4, L5 IT; Sst, Pvalb, Vip, etc.), and at the subtype level (e.g., Sst Chodl, Sst Esm1, etc.) (Figure 1B).



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    The schema of TransPilot server. (A) LLR-matrix coding for positive training nucleotide sequences. (B) LLR-matrix coding for negative training nucleotide sequences. (C) The ANN model and creation of the ranked and directed TF-target dataset. (D) Creation of ranked gene list from <t>transcriptome</t> data, identification of key TFs by TransPilot, and finally build the regulation network.
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    transcriptomic reference dataset  (Allen Institute for Brain Science)
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    Allen Institute for Brain Science transcriptomic reference dataset
    The schema of TransPilot server. (A) LLR-matrix coding for positive training nucleotide sequences. (B) LLR-matrix coding for negative training nucleotide sequences. (C) The ANN model and creation of the ranked and directed TF-target dataset. (D) Creation of ranked gene list from <t>transcriptome</t> data, identification of key TFs by TransPilot, and finally build the regulation network.
    Transcriptomic Reference Dataset, supplied by Allen Institute for Brain Science, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/transcriptomic+reference+dataset/transcriptomics+explorer/pm39353431-47-3-20
    Average 90 stars, based on 1 article reviews
    transcriptomic reference dataset - by Bioz Stars, 2026-10
    90/100 stars
    		  Buy from Supplier

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    The schema of TransPilot server. (A) LLR-matrix coding for positive training nucleotide sequences. (B) LLR-matrix coding for negative training nucleotide sequences. (C) The ANN model and creation of the ranked and directed TF-target dataset. (D) Creation of ranked gene list from transcriptome data, identification of key TFs by TransPilot, and finally build the regulation network.

    Journal: Bioinformatics Advances

    Article Title: TransPilot: mining key transcription factors by correlating binding sites with differentially expressed genes

    doi: 10.1093/bioadv/vbag145

    Figure Lengend Snippet: The schema of TransPilot server. (A) LLR-matrix coding for positive training nucleotide sequences. (B) LLR-matrix coding for negative training nucleotide sequences. (C) The ANN model and creation of the ranked and directed TF-target dataset. (D) Creation of ranked gene list from transcriptome data, identification of key TFs by TransPilot, and finally build the regulation network.

    Article Snippet: The TF-target pairs were ranked by the ANN score ( Y ANN , > 0.5) and the directions (positive or negative) of the regulatory relationships for each pair were determined by the correlation coefficient between their expression levels in a reference transcriptome dataset obtained from the Human Protein Atlas database ( Uhlén et al. 2015 ).

    Techniques: