Improving information retrieval in functional analysis
Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequen...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rodriguez, Juan C [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Computers in biology and medicine - New York, NY [u.a.] : Pergamon Press, 1970, 79(2016), Seite 10-20 |
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| Übergeordnetes Werk: |
volume:79 ; year:2016 ; pages:10-20 |
| Links: |
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| DOI / URN: |
10.1016/j.compbiomed.2016.09.017 |
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| 520 | |a Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. | ||
| 650 | 4 | |a Experiments | |
| 650 | 4 | |a Gene expression | |
| 650 | 4 | |a Information retrieval | |
| 650 | 4 | |a Methods | |
| 650 | 4 | |a Datasets | |
| 650 | 4 | |a Algorithms | |
| 650 | 4 | |a Breast cancer | |
| 650 | 4 | |a Genotype & phenotype | |
| 650 | 4 | |a Medical prognosis | |
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| 700 | 1 | |a Fresno, Cristóbal |4 oth | |
| 700 | 1 | |a Llera, Andrea S |4 oth | |
| 700 | 1 | |a Fernández, Elmer A |4 oth | |
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10.1016/j.compbiomed.2016.09.017 doi PQ20170501 (DE-627)OLC1986848183 (DE-599)GBVOLC1986848183 (PRQ)c2316-9ce60629a5e90ea71ee8b9ba4a061bfb1871ce019d73c1a561840339b1fe32840 (KEY)0003445220160000079000000010improvinginformationretrievalinfunctionalanalysis DE-627 ger DE-627 rakwb eng 610 570 DE-600 44.00 bkl 42.00 bkl Rodriguez, Juan C verfasserin aut Improving information retrieval in functional analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Experiments Gene expression Information retrieval Methods Datasets Algorithms Breast cancer Genotype & phenotype Medical prognosis González, Germán A oth Fresno, Cristóbal oth Llera, Andrea S oth Fernández, Elmer A oth Enthalten in Computers in biology and medicine New York, NY [u.a.] : Pergamon Press, 1970 79(2016), Seite 10-20 (DE-627)129312789 (DE-600)127557-4 (DE-576)014525828 0010-4825 nnns volume:79 year:2016 pages:10-20 http://dx.doi.org/10.1016/j.compbiomed.2016.09.017 Volltext http://search.proquest.com/docview/1846851692 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 44.00 AVZ 42.00 AVZ AR 79 2016 10-20 |
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10.1016/j.compbiomed.2016.09.017 doi PQ20170501 (DE-627)OLC1986848183 (DE-599)GBVOLC1986848183 (PRQ)c2316-9ce60629a5e90ea71ee8b9ba4a061bfb1871ce019d73c1a561840339b1fe32840 (KEY)0003445220160000079000000010improvinginformationretrievalinfunctionalanalysis DE-627 ger DE-627 rakwb eng 610 570 DE-600 44.00 bkl 42.00 bkl Rodriguez, Juan C verfasserin aut Improving information retrieval in functional analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Experiments Gene expression Information retrieval Methods Datasets Algorithms Breast cancer Genotype & phenotype Medical prognosis González, Germán A oth Fresno, Cristóbal oth Llera, Andrea S oth Fernández, Elmer A oth Enthalten in Computers in biology and medicine New York, NY [u.a.] : Pergamon Press, 1970 79(2016), Seite 10-20 (DE-627)129312789 (DE-600)127557-4 (DE-576)014525828 0010-4825 nnns volume:79 year:2016 pages:10-20 http://dx.doi.org/10.1016/j.compbiomed.2016.09.017 Volltext http://search.proquest.com/docview/1846851692 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 44.00 AVZ 42.00 AVZ AR 79 2016 10-20 |
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10.1016/j.compbiomed.2016.09.017 doi PQ20170501 (DE-627)OLC1986848183 (DE-599)GBVOLC1986848183 (PRQ)c2316-9ce60629a5e90ea71ee8b9ba4a061bfb1871ce019d73c1a561840339b1fe32840 (KEY)0003445220160000079000000010improvinginformationretrievalinfunctionalanalysis DE-627 ger DE-627 rakwb eng 610 570 DE-600 44.00 bkl 42.00 bkl Rodriguez, Juan C verfasserin aut Improving information retrieval in functional analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Experiments Gene expression Information retrieval Methods Datasets Algorithms Breast cancer Genotype & phenotype Medical prognosis González, Germán A oth Fresno, Cristóbal oth Llera, Andrea S oth Fernández, Elmer A oth Enthalten in Computers in biology and medicine New York, NY [u.a.] : Pergamon Press, 1970 79(2016), Seite 10-20 (DE-627)129312789 (DE-600)127557-4 (DE-576)014525828 0010-4825 nnns volume:79 year:2016 pages:10-20 http://dx.doi.org/10.1016/j.compbiomed.2016.09.017 Volltext http://search.proquest.com/docview/1846851692 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 44.00 AVZ 42.00 AVZ AR 79 2016 10-20 |
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10.1016/j.compbiomed.2016.09.017 doi PQ20170501 (DE-627)OLC1986848183 (DE-599)GBVOLC1986848183 (PRQ)c2316-9ce60629a5e90ea71ee8b9ba4a061bfb1871ce019d73c1a561840339b1fe32840 (KEY)0003445220160000079000000010improvinginformationretrievalinfunctionalanalysis DE-627 ger DE-627 rakwb eng 610 570 DE-600 44.00 bkl 42.00 bkl Rodriguez, Juan C verfasserin aut Improving information retrieval in functional analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Experiments Gene expression Information retrieval Methods Datasets Algorithms Breast cancer Genotype & phenotype Medical prognosis González, Germán A oth Fresno, Cristóbal oth Llera, Andrea S oth Fernández, Elmer A oth Enthalten in Computers in biology and medicine New York, NY [u.a.] : Pergamon Press, 1970 79(2016), Seite 10-20 (DE-627)129312789 (DE-600)127557-4 (DE-576)014525828 0010-4825 nnns volume:79 year:2016 pages:10-20 http://dx.doi.org/10.1016/j.compbiomed.2016.09.017 Volltext http://search.proquest.com/docview/1846851692 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 44.00 AVZ 42.00 AVZ AR 79 2016 10-20 |
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10.1016/j.compbiomed.2016.09.017 doi PQ20170501 (DE-627)OLC1986848183 (DE-599)GBVOLC1986848183 (PRQ)c2316-9ce60629a5e90ea71ee8b9ba4a061bfb1871ce019d73c1a561840339b1fe32840 (KEY)0003445220160000079000000010improvinginformationretrievalinfunctionalanalysis DE-627 ger DE-627 rakwb eng 610 570 DE-600 44.00 bkl 42.00 bkl Rodriguez, Juan C verfasserin aut Improving information retrieval in functional analysis 2016 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. Experiments Gene expression Information retrieval Methods Datasets Algorithms Breast cancer Genotype & phenotype Medical prognosis González, Germán A oth Fresno, Cristóbal oth Llera, Andrea S oth Fernández, Elmer A oth Enthalten in Computers in biology and medicine New York, NY [u.a.] : Pergamon Press, 1970 79(2016), Seite 10-20 (DE-627)129312789 (DE-600)127557-4 (DE-576)014525828 0010-4825 nnns volume:79 year:2016 pages:10-20 http://dx.doi.org/10.1016/j.compbiomed.2016.09.017 Volltext http://search.proquest.com/docview/1846851692 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 44.00 AVZ 42.00 AVZ AR 79 2016 10-20 |
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Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. |
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Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. |
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Transcriptome analysis is essential to understand the mechanisms regulating key biological processes and functions. The first step usually consists of identifying candidate genes; to find out which pathways are affected by those genes, however, functional analysis (FA) is mandatory. The most frequently used strategies for this purpose are Gene Set and Singular Enrichment Analysis (GSEA and SEA) over Gene Ontology. Several statistical methods have been developed and compared in terms of computational efficiency and/or statistical appropriateness. However, whether their results are similar or complementary, the sensitivity to parameter settings, or possible bias in the analyzed terms has not been addressed so far. Here, two GSEA and four SEA methods and their parameter combinations were evaluated in six datasets by comparing two breast cancer subtypes with well-known differences in genetic background and patient outcomes. We show that GSEA and SEA lead to different results depending on the chosen statistic, model and/or parameters. Both approaches provide complementary results from a biological perspective. Hence, an Integrative Functional Analysis (IFA) tool is proposed to improve information retrieval in FA. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required, since the best SEA/GSEA alternatives are integrated. IFA utility was demonstrated by evaluating four prostate cancer and the TCGA breast cancer microarray datasets, which showed its biological generalization capabilities. |
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