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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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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 ).

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