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Data Protection

Number of affected genes its respective network when the master regulatory TF were silenced with siRNA.

Johan L. M. Björkegren (528226)
Sara Hägg (225216)
Husain A. Talukdar (528227)
Hassan Foroughi Asl (528228)
Rajeev K. Jain (528229)
Cecilia Cedergren (528230)
Ming-Mei Shang (256386)
Aránzazu Rossignoli (528231)
Rabbe Takolander (225236)
Olle Melander (25955)
Anders Hamsten (62728)
Tom Michoel (29810)
Josefin Skogsberg (88384)

Publication date

February 2014

DOI

10.1371/journal.pgen.1004201.t003

Abstract

a<p>53 network genes (FDR<0.1);</p>b<p>185 network genes (FDR<0.1);</p>c<p>379 network genes (FDR<0.05);</p><p>TF, transcription factor; -, not applicable.</p

Extracted data

Related items

Percentages of selected genes listed in<i>CancerGenes</i>[<b>40</b>] and gene coding transcription factors (TFs).

Feng-Hsiang Chung (422246)
Henry Hsin-Chung Lee (422247)
Hoong-Chien Lee (250382)

June 2013

<p>Non-tumor TF means not listed in <i>CancerGenes</i>. (A) In gene set selected by statistical thre...

Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data.

Evi Berchtold
Gergely Csaba
Ralf Zimmer

January 2016

Several methods predict activity changes of transcription factors (TFs) from a given regulatory netw...

List of transcription factors and target genes in the second example of supervised GSAA.

Chen-An Tsai (357455)
Li-Yu Daisy Liu (389724)

March 2013

<p>Four subnetworks have been analyzed. The transcription factors (TFs) of interest were set as gene...

Data Set 4: Transcription factors from the network based on statistically validated interactions likely dysfunctional due to SNPs provoking stop-codons gain or loss.

Alvaro Chiner-Oms (3751681)

February 2017

Transcription factors from the network based on statistically validated interactions likely dysfunct...

Gene clustering for 354 genes altered by MI.

Yue-Feng Chen (312996)
James V. Pottala (312998)
Nathan Y. Weltman (304303)
Xijin Ge (88024)
Olga V. Savinova (313001)
A. Martin Gerdes (304306)

February 2013

<p>a. Genes whose expression was increased by MI but reduced or normalized by T4 treatment; b. Genes...

Transcription factors (TF) significantly connected to 198 genes in the “TNFα profile”.

Rachel Grimley (112160)
Oxana Polyakova (112157)
Jessica Vamathevan (2825)
Joanne McKenary (153128)
Brian Hayes (112164)
Champa Patel (153135)
Janet Smith (54931)
Angela Bridges (153140)
Andrew Fosberry (153144)
Anshu Bhardwaja (153148)
Bernadette Mouzon (153152)
Chun-Wa Chung (153155)
Nathalie Barrett (153158)
Nicola Richmond (153161)
Sundip Modha (153164)
Roberto Solari (112167)

July 2012

<p>Method: The genes in the “TNFα profile” were used to query to search an interaction database of 2...

Leveraging Transcription Factor Dependent RNA Profiling to Interrogate Gene Regulatory Networks in Human Disease Models

Smith, Linsin Ann

September 2022

Gene regulation is a tightly controlled process that when disrupted can cause a multitude of cellula...

Regulated transcription factors and their predicted genes that regulated up or down.

Ryan W. Speir (738343)
Jonathan D. Stallings (503286)
Jared M. Andrews (738344)
Mary S. Gelnett (738345)
Timothy C. Brand (738346)
Shashikumar K. Salgar (738347)

May 2015

<p>TF, Transcription factor; P, Probability; FDR, False Discovery Rate; I, Interaction; TG, Target G...

Transcription factor (TF) families identified in our co-expression network are ranked based on number of differentially expressed members (fold-change ≥ |1.2| and p-value ≤ 0.05).

Sanchari Sircar (198485)
Nita Parekh (5066798)

May 2019

In (A) top 20 up-regulated TF families (‘red’ bars), and (B) top 20 down-regulated TF families (‘blu...

Chromatin immunoprecipitation-based identification of gene regulatory networks

Niehof, M.
Borlak, J.

January 2005

Administered in critical doses, drugs and chemicals can damage organs and tissues by turning numerou...

TF with possible binding sites found in the 80 most-regulated genes.

Rafaela Rosa-Ribeiro (571107)
Umar Nishan (571108)
Ramon Oliveira Vidal (571109)
Guilherme Oliveira Barbosa (571110)
Leonardo Oliveira Reis (571111)
Carlos Lenz Cesar (571112)
Hernandes F. Carvalho (129814)

June 2014

<p>(A) TF binding sites not found in the internal control genes. (B) TF binding sites also found in ...

Transcriptional regulatory networks via gene ontology and expression data

Tuncay, Kağan
Ensman, Lisa
Sun, Jingjun
Haidar, Alaa Abi
Stanley, Frank
Trelinski, Michael
Ortoleva, Peter

June 2007

Transcriptional regulatory network (TRN) discovery using information from a single source does not s...

The 1,455 transcription factors (TFs) belonging to 45 families used to construct the FOX system.

Jinxia Wu (384415)
Zhiguo Zhang (147197)
Qian Zhang (49333)
Yayun Liu (770337)
Butuo Zhu (770338)
Jian Cao (16485)
Zhanpeng Li (770339)
Longzhi Han (705752)
Jizeng Jia (193841)
Guangyao Zhao (193828)
Xuehui Sun (384417)

July 2015

<p>The 1,455 transcription factors (TFs) belonging to 45 families used to construct the FOX system.<...

Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data

Berchtold, Evi
Csaba, Gergely
Zimmer, Ralf

January 2016

Several methods predict activity changes of transcription factors (TFs) from a given regulatory netw...

Analysis and comparision of Transcription Factor - Target Gene databases using network analysis. - The gene regulatory network documented on the basis of experimental evidence is analysed and compared by the graph based analysis method of various resources of gene regulatory network available from various public resource database is performed.

Poudel, Pratik Bikram

January 2014

Gene regulatory network assembly and analysis requires high quality knowledge sources that cover fun...

Percentages of selected genes listed in<i>CancerGenes</i>[<b>40</b>] and gene coding transcription factors (TFs).

Feng-Hsiang Chung (422246)
Henry Hsin-Chung Lee (422247)
Hoong-Chien Lee (250382)

June 2013

<p>Non-tumor TF means not listed in <i>CancerGenes</i>. (A) In gene set selected by statistical thre...

Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data.

Evi Berchtold
Gergely Csaba
Ralf Zimmer

January 2016

Several methods predict activity changes of transcription factors (TFs) from a given regulatory netw...

List of transcription factors and target genes in the second example of supervised GSAA.

Chen-An Tsai (357455)
Li-Yu Daisy Liu (389724)

March 2013

<p>Four subnetworks have been analyzed. The transcription factors (TFs) of interest were set as gene...

Data Set 4: Transcription factors from the network based on statistically validated interactions likely dysfunctional due to SNPs provoking stop-codons gain or loss.

Alvaro Chiner-Oms (3751681)

February 2017

Transcription factors from the network based on statistically validated interactions likely dysfunct...

Gene clustering for 354 genes altered by MI.

Yue-Feng Chen (312996)
James V. Pottala (312998)
Nathan Y. Weltman (304303)
Xijin Ge (88024)
Olga V. Savinova (313001)
A. Martin Gerdes (304306)

February 2013

<p>a. Genes whose expression was increased by MI but reduced or normalized by T4 treatment; b. Genes...

Transcription factors (TF) significantly connected to 198 genes in the “TNFα profile”.

Rachel Grimley (112160)
Oxana Polyakova (112157)
Jessica Vamathevan (2825)
Joanne McKenary (153128)
Brian Hayes (112164)
Champa Patel (153135)
Janet Smith (54931)
Angela Bridges (153140)
Andrew Fosberry (153144)
Anshu Bhardwaja (153148)
Bernadette Mouzon (153152)
Chun-Wa Chung (153155)
Nathalie Barrett (153158)
Nicola Richmond (153161)
Sundip Modha (153164)
Roberto Solari (112167)

July 2012

<p>Method: The genes in the “TNFα profile” were used to query to search an interaction database of 2...

Leveraging Transcription Factor Dependent RNA Profiling to Interrogate Gene Regulatory Networks in Human Disease Models

Smith, Linsin Ann

September 2022

Gene regulation is a tightly controlled process that when disrupted can cause a multitude of cellula...

Regulated transcription factors and their predicted genes that regulated up or down.

Ryan W. Speir (738343)
Jonathan D. Stallings (503286)
Jared M. Andrews (738344)
Mary S. Gelnett (738345)
Timothy C. Brand (738346)
Shashikumar K. Salgar (738347)

May 2015

<p>TF, Transcription factor; P, Probability; FDR, False Discovery Rate; I, Interaction; TG, Target G...

Transcription factor (TF) families identified in our co-expression network are ranked based on number of differentially expressed members (fold-change ≥ |1.2| and p-value ≤ 0.05).

Sanchari Sircar (198485)
Nita Parekh (5066798)

May 2019

In (A) top 20 up-regulated TF families (‘red’ bars), and (B) top 20 down-regulated TF families (‘blu...

Chromatin immunoprecipitation-based identification of gene regulatory networks

Niehof, M.
Borlak, J.

January 2005

Administered in critical doses, drugs and chemicals can damage organs and tissues by turning numerou...

TF with possible binding sites found in the 80 most-regulated genes.

Rafaela Rosa-Ribeiro (571107)
Umar Nishan (571108)
Ramon Oliveira Vidal (571109)
Guilherme Oliveira Barbosa (571110)
Leonardo Oliveira Reis (571111)
Carlos Lenz Cesar (571112)
Hernandes F. Carvalho (129814)

June 2014

<p>(A) TF binding sites not found in the internal control genes. (B) TF binding sites also found in ...

Transcriptional regulatory networks via gene ontology and expression data

Tuncay, Kağan
Ensman, Lisa
Sun, Jingjun
Haidar, Alaa Abi
Stanley, Frank
Trelinski, Michael
Ortoleva, Peter

June 2007

Transcriptional regulatory network (TRN) discovery using information from a single source does not s...

The 1,455 transcription factors (TFs) belonging to 45 families used to construct the FOX system.

Jinxia Wu (384415)
Zhiguo Zhang (147197)
Qian Zhang (49333)
Yayun Liu (770337)
Butuo Zhu (770338)
Jian Cao (16485)
Zhanpeng Li (770339)
Longzhi Han (705752)
Jizeng Jia (193841)
Guangyao Zhao (193828)
Xuehui Sun (384417)

July 2015

<p>The 1,455 transcription factors (TFs) belonging to 45 families used to construct the FOX system.<...

Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data

Berchtold, Evi
Csaba, Gergely
Zimmer, Ralf

January 2016

Several methods predict activity changes of transcription factors (TFs) from a given regulatory netw...

Analysis and comparision of Transcription Factor - Target Gene databases using network analysis. - The gene regulatory network documented on the basis of experimental evidence is analysed and compared by the graph based analysis method of various resources of gene regulatory network available from various public resource database is performed.

Poudel, Pratik Bikram

January 2014

Gene regulatory network assembly and analysis requires high quality knowledge sources that cover fun...

Percentages of selected genes listed in<i>CancerGenes</i>[<b>40</b>] and gene coding transcription factors (TFs).

Feng-Hsiang Chung (422246)
Henry Hsin-Chung Lee (422247)
Hoong-Chien Lee (250382)

June 2013

<p>Non-tumor TF means not listed in <i>CancerGenes</i>. (A) In gene set selected by statistical thre...

Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data.

Evi Berchtold
Gergely Csaba
Ralf Zimmer

January 2016

Several methods predict activity changes of transcription factors (TFs) from a given regulatory netw...

List of transcription factors and target genes in the second example of supervised GSAA.

Chen-An Tsai (357455)
Li-Yu Daisy Liu (389724)

March 2013

<p>Four subnetworks have been analyzed. The transcription factors (TFs) of interest were set as gene...