Additional file 3: Table S17. of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Additional file 2: of Alzheimerâs disease master regulators analysis: search for potential molecular targets and drug repositioning candidates

D. Vargas (3400316)
M. De Bastiani (5444837)
E. Zimmer (5444834)
F. Klamt (5444840)

June 2018

Table S1. Hippocampus transcription factor-centered network nodes and edges information. (XLSX 40 kb

Additional file 3: of Revealing Alzheimerâs disease genes spectrum in the whole-genome by machine learning

Xiaoyan Huang (112885)
Hankui Liu (4751289)
Xinming Li (143063)
Liping Guan (420348)
Jiankang Li (773081)
Laurent Tellier (4751292)
Huanming Yang (5905)
Jian Wang (5901)
Jianguo Zhang (6482)

January 2018

The classification of AD-associated genes and AD-non associated genes. C1-AD: probable pathogenic ge...

Additional file 2: of Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease

Erik Johnson (528214)
Eric Dammer (4174213)
Duc Duong (4174198)
Luming Yin (5821523)
Madhav Thambisetty (133912)
Juan Troncoso (249012)
James Lah (3504050)
Allan Levey (3504062)
Nicholas Seyfried (3504065)

October 2018

Table S2. Case Characteristics. Values shown are means ± SD. AD, Alzheimer’s disease; AsymAD, asympt...

Additional file 1: of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Minghui Wang (15418)
Panos Roussos (318249)
Andrew McKenzie (522240)
Xianxiao Zhou (3464498)
Yuji Kajiwara (265261)
Kristen Brennand (228552)
Gabriele De Luca (244081)
John Crary (3464492)
Patrizia Casaccia (225204)
Joseph Buxbaum (3464495)
Michelle Ehrlich (3453359)
Sam Gandy (6210)
Alison Goate (48756)
Pavel Katsel (257692)
Eric Schadt (105785)
Vahram Haroutunian (23823)
Bin Zhang (64682)

November 2016

This document contains Supplementary Tables S1â16, plus their associated legends/captions. (XLSX 6...

Additional file 3: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S2. List of 30âAD-specific modules and 31 normal-specific modules. (XLSX 27 kb

Additional file 3: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S3. Enrichment of AD-risk variants at H3K4me1 peaks from Roadmap Epigenomics Consortium data. ...

Additional file 4: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S3. Enriched GO terms (p-value<0.01) of all condition-specific modules. Table S4. Enriched KEG...

Additional file 1: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S1. Comparison of module genes identified by lmQCM and WGCNA from GSE48350 AD samples. (XLSX 1...

Additional file 2: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S2. Enrichment of AD-risk variants at H3K27ac peaks from Roadmap Epigenomics Consortium data. ...

Additional file 3: Table S2. of Gene expression and functional annotation of human choroid plexus epithelium failure in Alzheimerâs disease

Arthur Bergen (290307)
Sovann Kaing (3577850)
Jacoline ten Brink (3577847)
Theo Gorgels (3577853)
Sarah Janssen (3577856)

November 2015

Contains compilations of lists of (disease) genes previously implicated in relevant disease. The ent...

Additional file 4: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S4. Enrichment of AD-risk variants at H4K3me3 peaks from Roadmap Epigenomics Consortium data. ...

Additional file 1: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S1. Enrichment of AD-risk variants at DHSs from Roadmap Epigenomics Consortium data. (XLSX 36 ...

Additional file 5: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S5. Modules with targets of specific differentially expressed TF enriched in certain pathways....

Additional file 1: of Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimerâs disease

Qi Zhang (28502)
Cheng Ma (298689)
Marla Gearing (1939525)
Peng Wang (34436)
Lih-Shen Chin (62183)
Lian Li (49049)

March 2018

Table S1. Demographic and neuropathological data of human AD patient and control cases. For each cas...

Additional file 8: of Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease

Erik Johnson (528214)
Eric Dammer (4174213)
Duc Duong (4174198)
Luming Yin (5821523)
Madhav Thambisetty (133912)
Juan Troncoso (249012)
James Lah (3504050)
Allan Levey (3504062)
Nicholas Seyfried (3504065)

October 2018

Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue homoge...

Additional file 2: of Alzheimerâs disease master regulators analysis: search for potential molecular targets and drug repositioning candidates

D. Vargas (3400316)
M. De Bastiani (5444837)
E. Zimmer (5444834)
F. Klamt (5444840)

June 2018

Table S1. Hippocampus transcription factor-centered network nodes and edges information. (XLSX 40 kb

Additional file 3: of Revealing Alzheimerâs disease genes spectrum in the whole-genome by machine learning

Xiaoyan Huang (112885)
Hankui Liu (4751289)
Xinming Li (143063)
Liping Guan (420348)
Jiankang Li (773081)
Laurent Tellier (4751292)
Huanming Yang (5905)
Jian Wang (5901)
Jianguo Zhang (6482)

January 2018

The classification of AD-associated genes and AD-non associated genes. C1-AD: probable pathogenic ge...

Additional file 2: of Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease

Erik Johnson (528214)
Eric Dammer (4174213)
Duc Duong (4174198)
Luming Yin (5821523)
Madhav Thambisetty (133912)
Juan Troncoso (249012)
James Lah (3504050)
Allan Levey (3504062)
Nicholas Seyfried (3504065)

October 2018

Table S2. Case Characteristics. Values shown are means ± SD. AD, Alzheimer’s disease; AsymAD, asympt...

Additional file 1: of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Minghui Wang (15418)
Panos Roussos (318249)
Andrew McKenzie (522240)
Xianxiao Zhou (3464498)
Yuji Kajiwara (265261)
Kristen Brennand (228552)
Gabriele De Luca (244081)
John Crary (3464492)
Patrizia Casaccia (225204)
Joseph Buxbaum (3464495)
Michelle Ehrlich (3453359)
Sam Gandy (6210)
Alison Goate (48756)
Pavel Katsel (257692)
Eric Schadt (105785)
Vahram Haroutunian (23823)
Bin Zhang (64682)

November 2016

This document contains Supplementary Tables S1â16, plus their associated legends/captions. (XLSX 6...

Additional file 3: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S2. List of 30âAD-specific modules and 31 normal-specific modules. (XLSX 27 kb

Additional file 3: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S3. Enrichment of AD-risk variants at H3K4me1 peaks from Roadmap Epigenomics Consortium data. ...

Additional file 4: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S3. Enriched GO terms (p-value<0.01) of all condition-specific modules. Table S4. Enriched KEG...

Additional file 1: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S1. Comparison of module genes identified by lmQCM and WGCNA from GSE48350 AD samples. (XLSX 1...

Additional file 2: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S2. Enrichment of AD-risk variants at H3K27ac peaks from Roadmap Epigenomics Consortium data. ...

Additional file 3: Table S2. of Gene expression and functional annotation of human choroid plexus epithelium failure in Alzheimerâs disease

Arthur Bergen (290307)
Sovann Kaing (3577850)
Jacoline ten Brink (3577847)
Theo Gorgels (3577853)
Sarah Janssen (3577856)

November 2015

Contains compilations of lists of (disease) genes previously implicated in relevant disease. The ent...

Additional file 4: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S4. Enrichment of AD-risk variants at H4K3me3 peaks from Roadmap Epigenomics Consortium data. ...

Additional file 1: of Genetic risk for Alzheimerâs disease is concentrated in specific macrophage and microglial transcriptional networks

Katherine Tansey (4889533)
Darren Cameron (4889530)
Matthew Hill (394308)

February 2018

Table S1. Enrichment of AD-risk variants at DHSs from Roadmap Epigenomics Consortium data. (XLSX 36 ...

Additional file 5: of Condition-specific gene co-expression network mining identifies key pathways and regulators in the brain tissue of Alzheimerâs disease patients

Shunian Xiang (3207645)
Zhi Huang (472377)
Tianfu Wang (733641)
Zhi Han (3490682)
Christina Yu (425912)
Dong Ni (172495)
Kun Huang (136241)
Jie Zhang (64655)

January 2019

Table S5. Modules with targets of specific differentially expressed TF enriched in certain pathways....

Additional file 1: of Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimerâs disease

Qi Zhang (28502)
Cheng Ma (298689)
Marla Gearing (1939525)
Peng Wang (34436)
Lih-Shen Chin (62183)
Lian Li (49049)

March 2018

Table S1. Demographic and neuropathological data of human AD patient and control cases. For each cas...

Additional file 8: of Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease

Erik Johnson (528214)
Eric Dammer (4174213)
Duc Duong (4174198)
Luming Yin (5821523)
Madhav Thambisetty (133912)
Juan Troncoso (249012)
James Lah (3504050)
Allan Levey (3504062)
Nicholas Seyfried (3504065)

October 2018

Figure S1. SDS-PAGE of Brain Homogenates. Dorsolateral prefrontal cortex (DLPFC) brain tissue homoge...

Additional file 2: of Alzheimerâs disease master regulators analysis: search for potential molecular targets and drug repositioning candidates

D. Vargas (3400316)
M. De Bastiani (5444837)
E. Zimmer (5444834)
F. Klamt (5444840)

June 2018

Table S1. Hippocampus transcription factor-centered network nodes and edges information. (XLSX 40 kb

Additional file 3: of Revealing Alzheimerâs disease genes spectrum in the whole-genome by machine learning

Xiaoyan Huang (112885)
Hankui Liu (4751289)
Xinming Li (143063)
Liping Guan (420348)
Jiankang Li (773081)
Laurent Tellier (4751292)
Huanming Yang (5905)
Jian Wang (5901)
Jianguo Zhang (6482)

January 2018

The classification of AD-associated genes and AD-non associated genes. C1-AD: probable pathogenic ge...

Additional file 2: of Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease

Erik Johnson (528214)
Eric Dammer (4174213)
Duc Duong (4174198)
Luming Yin (5821523)
Madhav Thambisetty (133912)
Juan Troncoso (249012)
James Lah (3504050)
Allan Levey (3504062)
Nicholas Seyfried (3504065)

October 2018

Table S2. Case Characteristics. Values shown are means ± SD. AD, Alzheimer’s disease; AsymAD, asympt...

Additional file 3: Table S17. of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Abstract

Extracted data

Additional file 3: Table S17. of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Abstract

Extracted data

Related items

Related items

Additional file 3: Table S17. of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Abstract

Extracted data

Additional file 3: Table S17. of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Abstract

Extracted data

Related items

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Additional file 3: Table S17. of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease

Additional file 3: Table S17. of Integrative network analysis of nineteen brain regions identifies molecular signatures and networks underlying selective regional vulnerability to Alzheimerâs disease