Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis
Background One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene l...
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| Published in | BMC bioinformatics Vol. 10; no. 1; p. 200 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
29.06.2009
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1471-2105 1471-2105 |
| DOI | 10.1186/1471-2105-10-200 |
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| Abstract | Background
One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself.
Results
We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets.
Conclusion
This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at
http://www.abcc.ncifcrf.gov/wps/wps_index.php |
|---|---|
| AbstractList | One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself.BACKGROUNDOne of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself.We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets.RESULTSWe now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets.This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php.CONCLUSIONThis tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php. One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php. Background One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. Results We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. Conclusion This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php BACKGROUND: One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. RESULTS: We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. CONCLUSION: This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php Background One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. Results We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. Conclusion This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at Abstract Background One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for common or unique underlying biological themes. A common way to approach this problem is to extract common genes from these gene lists and then subject these genes to enrichment analysis to reveal the underlying biology. However, the capacity of this approach is largely restricted by the limited number of common genes shared by datasets from multiple experiments, which could be caused by the complexity of the biological system itself. Results We now introduce a new Pathway Pattern Extraction Pipeline (PPEP), which extends the existing WPS application by providing a new pathway-level comparative analysis scheme. To facilitate comparing and correlating results from different studies and sources, PPEP contains new interfaces that allow evaluation of the pathway-level enrichment patterns across multiple gene lists. As an exploratory tool, this analysis pipeline may help reveal the underlying biological themes at both the pathway and gene levels. The analysis scheme provided by PPEP begins with multiple gene lists, which may be derived from different studies in terms of the biological contexts, applied technologies, or methodologies. These lists are then subjected to pathway-level comparative analysis for extraction of pathway-level patterns. This analysis pipeline helps to explore the commonality or uniqueness of these lists at the level of pathways or biological processes from different but relevant biological systems using a combination of statistical enrichment measurements, pathway-level pattern extraction, and graphical display of the relationships of genes and their associated pathways as Gene-Term Association Networks (GTANs) within the WPS platform. As a proof of concept, we have used the new method to analyze many datasets from our collaborators as well as some public microarray datasets. Conclusion This tool provides a new pathway-level analysis scheme for integrative and comparative analysis of data derived from different but relevant systems. The tool is freely available as a Pathway Pattern Extraction Pipeline implemented in our existing software package WPS, which can be obtained at http://www.abcc.ncifcrf.gov/wps/wps_index.php |
| ArticleNumber | 200 |
| Audience | Academic |
| Author | Che, Anney Yi, Ming Stephens, Robert M Mudunuri, Uma |
| AuthorAffiliation | 1 Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc, NCI-Frederick, Frederick, MD 21702, USA |
| AuthorAffiliation_xml | – name: 1 Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc, NCI-Frederick, Frederick, MD 21702, USA |
| Author_xml | – sequence: 1 givenname: Ming surname: Yi fullname: Yi, Ming email: myi@ncifcrf.gov organization: Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc – sequence: 2 givenname: Uma surname: Mudunuri fullname: Mudunuri, Uma organization: Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc – sequence: 3 givenname: Anney surname: Che fullname: Che, Anney organization: Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc – sequence: 4 givenname: Robert M surname: Stephens fullname: Stephens, Robert M organization: Advanced Biomedical Computing Center, Advanced Technology Program, SAIC-Frederick Inc |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/19563622$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.4161/cc.5.7.2628 10.1093/bioinformatics/btg264 10.5507/bp.2006.029 10.2217/14796694.2.6.743 10.1534/genetics.107.082602 10.1038/ng1180 10.1073/pnas.95.25.14863 10.1186/1471-2105-8-74 10.1186/gb-2003-4-10-r70 10.1016/S1476-5586(04)80047-2 10.1101/gr.169101 10.1186/1471-2105-7-30 10.1016/j.clon.2006.11.013 10.1016/j.ejca.2007.04.002 10.1111/j.1365-2141.2007.06611.x 10.1158/0008-5472.CAN-07-2608 10.1002/ijc.22648 10.1016/j.canlet.2005.12.004 10.1371/journal.pone.0002484 10.1073/pnas.0506580102 10.1186/1471-2105-6-168 10.1073/pnas.96.6.2907 10.1002/pros.20786 10.1101/gr.1239303 10.1371/journal.pone.0003288 10.1073/pnas.0304146101 10.1038/sj.onc.1210422 10.1016/S1046-2023(03)00157-9 10.1038/35090585 10.1002/ijc.23237 10.1073/pnas.091062498 10.1126/science.291.5507.1224 10.1093/bioinformatics/btg165 10.1158/1055-9965.EPI-08-0261 10.1200/JCO.2004.05.158 10.2307/2346830 10.1186/gb-2003-4-5-p3 10.1073/pnas.0400782101 10.1038/ng1570 10.1096/fj.04-2415fje 10.1159/000098207 10.1093/bioinformatics/btl424 10.1093/nar/gki230 10.1038/ng1935 10.1001/jama.286.18.2239 |
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| Keywords | Biological Theme Enrichment Score Pattern Extraction Gene List Oncomine Database |
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| References | AI Su (2930_CR43) 2004; 101 JJ Chen (2930_CR25) 2007; 8 EA Wiemer (2930_CR38) 2007; 43 N Cordes (2930_CR39) 2006; 242 DR Rhodes (2930_CR26) 2004; 6 G Turashvili (2930_CR42) 2006; 73 SM Dhanasekaran (2930_CR27) 2001; 412 P Pavlidis (2930_CR8) 2003; 31 BR Zeeberg (2930_CR14) 2005; 6 JB Welsh (2930_CR31) 2001; 61 A Subramanian (2930_CR16) 2005; 102 JM Pellikainen (2930_CR33) 2007; 120 TA Wallace (2930_CR50) 2008; 68 F Al-Shahrour (2930_CR13) 2005 N Fatima (2930_CR53) 2008; 17 P Tamayo (2930_CR3) 1999; 96 DR Rhodes (2930_CR45) 2005; 37 F Simunovic (2930_CR54) 2008 AJ Galliher (2930_CR41) 2006; 2 DR Rhodes (2930_CR44) 2002; 62 BJ Boersma (2930_CR48) 2008; 122 VK Mootha (2930_CR15) 2003; 34 PJ Roberts (2930_CR40) 2007; 26 M Yi (2930_CR4) 2006; 7 WK Scott (2930_CR18) 2001; 286 MN Katherine (2930_CR51) 2008; 178 J Lapointe (2930_CR29) 2004; 101 2930_CR23 2930_CR24 VG Tusher (2930_CR5) 2001; 98 CH Lawrie (2930_CR37) 2007; 137 H Jiang (2930_CR52) 2008; 3 N Jain (2930_CR6) 2003; 19 S Dohr (2930_CR20) 2005; 33 M Yi (2930_CR22) 2008; 3 S Draghici (2930_CR7) 2003; 19 RO Escárcega (2930_CR35) 2007; 19 2930_CR9 DA Hosack (2930_CR46) 2003; 4 SM Dhanasekaran (2930_CR28) 2005; 19 L Peltonen (2930_CR17) 2001; 291 S St Clair (2930_CR34) 2006; 5 G Dennis Jr (2930_CR12) 2003; 4 B Kusenda (2930_CR36) 2006; 150 2930_CR10 P Pavlidis (2930_CR47) 2002 2930_CR55 SA Tomlins (2930_CR30) 2007; 39 RL Prueitt (2930_CR49) 2008; 68 MB Eisen (2930_CR1) 1998; 95 P Shannon (2930_CR11) 2003; 13 LT Reiter (2930_CR19) 2001; 11 YP Yu (2930_CR32) 2004; 22 T Manoli (2930_CR21) 2006; 22 JA Hartigan (2930_CR2) 1979; 28 |
| References_xml | – volume: 5 start-page: 709 issue: 7 year: 2006 ident: 2930_CR34 publication-title: Cell Cycle doi: 10.4161/cc.5.7.2628 – volume: 19 start-page: 1945 issue: 15 year: 2003 ident: 2930_CR6 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg264 – volume: 150 start-page: 205 issue: 2 year: 2006 ident: 2930_CR36 publication-title: Biomed Pap Med Fac Univ Palacky Olomouc Czech Repub doi: 10.5507/bp.2006.029 – volume: 2 start-page: 743 issue: 6 year: 2006 ident: 2930_CR41 publication-title: Future Oncol doi: 10.2217/14796694.2.6.743 – volume: 178 start-page: 197 issue: 1 year: 2008 ident: 2930_CR51 publication-title: Genetics doi: 10.1534/genetics.107.082602 – volume: 34 start-page: 267 year: 2003 ident: 2930_CR15 publication-title: Nat Genet doi: 10.1038/ng1180 – volume: 95 start-page: 14863 year: 1998 ident: 2930_CR1 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.95.25.14863 – volume: 8 start-page: 74 year: 2007 ident: 2930_CR25 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-8-74 – volume-title: Nucleic Acids Research year: 2005 ident: 2930_CR13 – ident: 2930_CR23 – volume: 4 start-page: R70.1 year: 2003 ident: 2930_CR46 publication-title: Genome Biology doi: 10.1186/gb-2003-4-10-r70 – volume-title: Brain year: 2008 ident: 2930_CR54 – volume: 6 start-page: 1 year: 2004 ident: 2930_CR26 publication-title: Neoplasia doi: 10.1016/S1476-5586(04)80047-2 – volume: 11 start-page: 1114 year: 2001 ident: 2930_CR19 publication-title: Genome Res doi: 10.1101/gr.169101 – ident: 2930_CR10 – volume: 7 start-page: 30 year: 2006 ident: 2930_CR4 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-7-30 – volume: 61 start-page: 5974 issue: 16 year: 2001 ident: 2930_CR31 publication-title: Cancer Res – volume: 19 start-page: 154 issue: 2 year: 2007 ident: 2930_CR35 publication-title: Clin Oncol (R Coll Radiol) doi: 10.1016/j.clon.2006.11.013 – ident: 2930_CR55 – volume: 43 start-page: 1529 issue: 10 year: 2007 ident: 2930_CR38 publication-title: Eur J Cancer doi: 10.1016/j.ejca.2007.04.002 – volume: 137 start-page: 503 issue: 6 year: 2007 ident: 2930_CR37 publication-title: Br J Haematol doi: 10.1111/j.1365-2141.2007.06611.x – volume: 68 start-page: 927 issue: 3 year: 2008 ident: 2930_CR50 publication-title: Cancer Res doi: 10.1158/0008-5472.CAN-07-2608 – ident: 2930_CR24 – volume: 120 start-page: 2061 issue: 10 year: 2007 ident: 2930_CR33 publication-title: Int J Cancer doi: 10.1002/ijc.22648 – volume: 242 start-page: 11 issue: 1 year: 2006 ident: 2930_CR39 publication-title: Cancer Lett doi: 10.1016/j.canlet.2005.12.004 – volume: 3 start-page: e2484 issue: 6 year: 2008 ident: 2930_CR52 publication-title: PLoS ONE doi: 10.1371/journal.pone.0002484 – volume: 102 start-page: 15545 year: 2005 ident: 2930_CR16 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.0506580102 – volume: 6 start-page: 168 year: 2005 ident: 2930_CR14 publication-title: BMC Bioinformatics doi: 10.1186/1471-2105-6-168 – volume: 96 start-page: 2907 year: 1999 ident: 2930_CR3 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.96.6.2907 – volume: 68 start-page: 1152 issue: 11 year: 2008 ident: 2930_CR49 publication-title: Prostate doi: 10.1002/pros.20786 – volume: 13 start-page: 2498 year: 2003 ident: 2930_CR11 publication-title: Genome Res doi: 10.1101/gr.1239303 – volume: 3 start-page: e3288 issue: 9 year: 2008 ident: 2930_CR22 publication-title: PLoS ONE doi: 10.1371/journal.pone.0003288 – volume: 101 start-page: 811 issue: 3 year: 2004 ident: 2930_CR29 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.0304146101 – volume: 26 start-page: 3291 issue: 22 year: 2007 ident: 2930_CR40 publication-title: Oncogene doi: 10.1038/sj.onc.1210422 – volume: 31 start-page: 282 year: 2003 ident: 2930_CR8 publication-title: Methods doi: 10.1016/S1046-2023(03)00157-9 – volume: 412 start-page: 822 year: 2001 ident: 2930_CR27 publication-title: Nature doi: 10.1038/35090585 – volume: 122 start-page: 1324 issue: 6 year: 2008 ident: 2930_CR48 publication-title: Int J Cancer doi: 10.1002/ijc.23237 – volume: 98 start-page: 5116 year: 2001 ident: 2930_CR5 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.091062498 – volume: 291 start-page: 1224 year: 2001 ident: 2930_CR17 publication-title: Science doi: 10.1126/science.291.5507.1224 – volume: 19 start-page: 1348 year: 2003 ident: 2930_CR7 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btg165 – volume: 17 start-page: 3051 issue: 11 year: 2008 ident: 2930_CR53 publication-title: Cancer Epidemiol Biomarkers Prev doi: 10.1158/1055-9965.EPI-08-0261 – volume: 22 start-page: 2790 issue: 14 year: 2004 ident: 2930_CR32 publication-title: J Clin Oncol doi: 10.1200/JCO.2004.05.158 – volume: 28 start-page: 100 year: 1979 ident: 2930_CR2 publication-title: Applied Statistics doi: 10.2307/2346830 – volume: 62 start-page: 4427 year: 2002 ident: 2930_CR44 publication-title: Cancer Res – volume: 4 start-page: P3 year: 2003 ident: 2930_CR12 publication-title: Genome Biol doi: 10.1186/gb-2003-4-5-p3 – volume: 101 start-page: 6062 year: 2004 ident: 2930_CR43 publication-title: Proc Natl Acad Sci USA doi: 10.1073/pnas.0400782101 – volume: 37 start-page: S31 issue: Suppl year: 2005 ident: 2930_CR45 publication-title: Nat Genet doi: 10.1038/ng1570 – volume: 19 start-page: 243 issue: 2 year: 2005 ident: 2930_CR28 publication-title: FASEB J doi: 10.1096/fj.04-2415fje – volume: 73 start-page: 213 issue: 5 year: 2006 ident: 2930_CR42 publication-title: Pathobiology doi: 10.1159/000098207 – volume: 22 start-page: 2500 year: 2006 ident: 2930_CR21 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btl424 – volume: 33 start-page: 864 year: 2005 ident: 2930_CR20 publication-title: Nucleic Acids Res doi: 10.1093/nar/gki230 – ident: 2930_CR9 – volume: 39 start-page: 41 issue: 1 year: 2007 ident: 2930_CR30 publication-title: Nat Genet doi: 10.1038/ng1935 – volume: 286 start-page: 2239 issue: 18 year: 2001 ident: 2930_CR18 publication-title: JAMA doi: 10.1001/jama.286.18.2239 – start-page: 474 volume-title: Pac Symp Biocomput year: 2002 ident: 2930_CR47 |
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One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple... One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple experiments for... Background One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple... BACKGROUND: One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from multiple... Abstract Background One of the challenges in the analysis of microarray data is to integrate and compare the selected (e.g., differential) gene lists from... |
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| Title | Seeking unique and common biological themes in multiple gene lists or datasets: pathway pattern extraction pipeline for pathway-level comparative analysis |
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