Pathway and network analysis in proteomics
Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics dat...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wu, Xiaogang [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2014transfer abstract |
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| Schlagwörter: |
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| Umfang: |
9 |
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| Übergeordnetes Werk: |
Enthalten in: Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China - Li, Yong ELSEVIER, 2020, Amsterdam |
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| Übergeordnetes Werk: |
volume:362 ; year:2014 ; day:7 ; month:12 ; pages:44-52 ; extent:9 |
| Links: |
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| DOI / URN: |
10.1016/j.jtbi.2014.05.031 |
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ELV028476123 |
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| 520 | |a Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. | ||
| 520 | |a Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. | ||
| 650 | 7 | |a Pathway analysis |2 Elsevier | |
| 650 | 7 | |a Network modules |2 Elsevier | |
| 650 | 7 | |a Complex networks |2 Elsevier | |
| 650 | 7 | |a Functional analysis |2 Elsevier | |
| 650 | 7 | |a Hybrid strategy |2 Elsevier | |
| 700 | 1 | |a Hasan, Mohammad Al |4 oth | |
| 700 | 1 | |a Chen, Jake Yue |4 oth | |
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10.1016/j.jtbi.2014.05.031 doi GBVA2014021000012.pica (DE-627)ELV028476123 (ELSEVIER)S0022-5193(14)00304-X DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Wu, Xiaogang verfasserin aut Pathway and network analysis in proteomics 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Pathway analysis Elsevier Network modules Elsevier Complex networks Elsevier Functional analysis Elsevier Hybrid strategy Elsevier Hasan, Mohammad Al oth Chen, Jake Yue oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:362 year:2014 day:7 month:12 pages:44-52 extent:9 https://doi.org/10.1016/j.jtbi.2014.05.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 362 2014 7 1207 44-52 9 045F 570 |
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10.1016/j.jtbi.2014.05.031 doi GBVA2014021000012.pica (DE-627)ELV028476123 (ELSEVIER)S0022-5193(14)00304-X DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Wu, Xiaogang verfasserin aut Pathway and network analysis in proteomics 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Pathway analysis Elsevier Network modules Elsevier Complex networks Elsevier Functional analysis Elsevier Hybrid strategy Elsevier Hasan, Mohammad Al oth Chen, Jake Yue oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:362 year:2014 day:7 month:12 pages:44-52 extent:9 https://doi.org/10.1016/j.jtbi.2014.05.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 362 2014 7 1207 44-52 9 045F 570 |
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10.1016/j.jtbi.2014.05.031 doi GBVA2014021000012.pica (DE-627)ELV028476123 (ELSEVIER)S0022-5193(14)00304-X DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Wu, Xiaogang verfasserin aut Pathway and network analysis in proteomics 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Pathway analysis Elsevier Network modules Elsevier Complex networks Elsevier Functional analysis Elsevier Hybrid strategy Elsevier Hasan, Mohammad Al oth Chen, Jake Yue oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:362 year:2014 day:7 month:12 pages:44-52 extent:9 https://doi.org/10.1016/j.jtbi.2014.05.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 362 2014 7 1207 44-52 9 045F 570 |
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10.1016/j.jtbi.2014.05.031 doi GBVA2014021000012.pica (DE-627)ELV028476123 (ELSEVIER)S0022-5193(14)00304-X DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Wu, Xiaogang verfasserin aut Pathway and network analysis in proteomics 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Pathway analysis Elsevier Network modules Elsevier Complex networks Elsevier Functional analysis Elsevier Hybrid strategy Elsevier Hasan, Mohammad Al oth Chen, Jake Yue oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:362 year:2014 day:7 month:12 pages:44-52 extent:9 https://doi.org/10.1016/j.jtbi.2014.05.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 362 2014 7 1207 44-52 9 045F 570 |
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10.1016/j.jtbi.2014.05.031 doi GBVA2014021000012.pica (DE-627)ELV028476123 (ELSEVIER)S0022-5193(14)00304-X DE-627 ger DE-627 rakwb eng 570 570 DE-600 660 VZ Wu, Xiaogang verfasserin aut Pathway and network analysis in proteomics 2014transfer abstract 9 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. Pathway analysis Elsevier Network modules Elsevier Complex networks Elsevier Functional analysis Elsevier Hybrid strategy Elsevier Hasan, Mohammad Al oth Chen, Jake Yue oth Enthalten in Elsevier Ltd Li, Yong ELSEVIER Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China 2020 Amsterdam (DE-627)ELV004081676 volume:362 year:2014 day:7 month:12 pages:44-52 extent:9 https://doi.org/10.1016/j.jtbi.2014.05.031 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA AR 362 2014 7 1207 44-52 9 045F 570 |
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English |
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Enthalten in Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China Amsterdam volume:362 year:2014 day:7 month:12 pages:44-52 extent:9 |
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Enthalten in Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China Amsterdam volume:362 year:2014 day:7 month:12 pages:44-52 extent:9 |
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Dissolution versus cementation and its role in determining tight sandstone quality: A case study from the Upper Paleozoic in northeastern Ordos Basin, China |
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Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. |
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Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. |
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Proteomics is inherently a systems science that studies not only measured protein and their expressions in a cell, but also the interplay of proteins, protein complexes, signaling pathways, and network modules. There is a rapid accumulation of Proteomics data in recent years. However, Proteomics data are highly variable, with results sensitive to data preparation methods, sample condition, instrument types, and analytical methods. To address the challenge in Proteomics data analysis, we review current tools being developed to incorporate biological function and network topological information. We categorize these tools into four types: tools with basic functional information and little topological features (e.g., GO category analysis), tools with rich functional information and little topological features (e.g., GSEA), tools with basic functional information and rich topological features (e.g., Cytoscape), and tools with rich functional information and rich topological features (e.g., PathwayExpress). We first review the potential application of these tools to Proteomics; then we review tools that can achieve automated learning of pathway modules and features, and tools that help perform integrated network visual analytics. |
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