Eintrag weiter verarbeiten
Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets
Gespeichert in:
| Zeitschriftentitel: | Bioinformatics |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Bioinformatics, 26, 2010, 12, S. 1506-1512 |
| Format: | E-Article |
| Sprache: | Englisch |
| veröffentlicht: |
Oxford University Press (OUP)
|
| Schlagwörter: |
| author_facet |
Park, Inho Lee, Kwang H. Lee, Doheon Park, Inho Lee, Kwang H. Lee, Doheon |
|---|---|
| author |
Park, Inho Lee, Kwang H. Lee, Doheon |
| spellingShingle |
Park, Inho Lee, Kwang H. Lee, Doheon Bioinformatics Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| author_sort |
park, inho |
| spelling |
Park, Inho Lee, Kwang H. Lee, Doheon 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btq207 <jats:title>Abstract</jats:title> <jats:p>Motivation: Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently.</jats:p> <jats:p>Results: We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology.</jats:p> <jats:p>Availability: Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/∼ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material.</jats:p> <jats:p>Contact: khlee@biosoft.kaist.ac.kr; dhlee@biosoft.kaist.ac.kr</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets Bioinformatics |
| doi_str_mv |
10.1093/bioinformatics/btq207 |
| facet_avail |
Online Free |
| finc_class_facet |
Mathematik Informatik Biologie Chemie und Pharmazie |
| format |
ElectronicArticle |
| fullrecord |
blob:ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9iaW9pbmZvcm1hdGljcy9idHEyMDc |
| id |
ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9iaW9pbmZvcm1hdGljcy9idHEyMDc |
| institution |
DE-105 DE-14 DE-Ch1 DE-L229 DE-D275 DE-Bn3 DE-Brt1 DE-D161 DE-Zwi2 DE-Gla1 DE-Zi4 DE-15 DE-Pl11 DE-Rs1 |
| imprint |
Oxford University Press (OUP), 2010 |
| imprint_str_mv |
Oxford University Press (OUP), 2010 |
| issn |
1367-4811 1367-4803 |
| issn_str_mv |
1367-4811 1367-4803 |
| language |
English |
| mega_collection |
Oxford University Press (OUP) (CrossRef) |
| match_str |
park2010inferenceofcombinatorialbooleanrulesofsynergisticgenesetsfromcancermicroarraydatasets |
| publishDateSort |
2010 |
| publisher |
Oxford University Press (OUP) |
| recordtype |
ai |
| record_format |
ai |
| series |
Bioinformatics |
| source_id |
49 |
| title |
Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_unstemmed |
Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_full |
Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_fullStr |
Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_full_unstemmed |
Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_short |
Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_sort |
inference of combinatorial boolean rules of synergistic gene sets from cancer microarray datasets |
| topic |
Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability |
| url |
http://dx.doi.org/10.1093/bioinformatics/btq207 |
| publishDate |
2010 |
| physical |
1506-1512 |
| description |
<jats:title>Abstract</jats:title>
<jats:p>Motivation: Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently.</jats:p>
<jats:p>Results: We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology.</jats:p>
<jats:p>Availability: Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/∼ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material.</jats:p>
<jats:p>Contact: khlee@biosoft.kaist.ac.kr; dhlee@biosoft.kaist.ac.kr</jats:p>
<jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
| container_issue |
12 |
| container_start_page |
1506 |
| container_title |
Bioinformatics |
| container_volume |
26 |
| format_de105 |
Article, E-Article |
| format_de14 |
Article, E-Article |
| format_de15 |
Article, E-Article |
| format_de520 |
Article, E-Article |
| format_de540 |
Article, E-Article |
| format_dech1 |
Article, E-Article |
| format_ded117 |
Article, E-Article |
| format_degla1 |
E-Article |
| format_del152 |
Buch |
| format_del189 |
Article, E-Article |
| format_dezi4 |
Article |
| format_dezwi2 |
Article, E-Article |
| format_finc |
Article, E-Article |
| format_nrw |
Article, E-Article |
| _version_ |
1792334718308450307 |
| geogr_code |
not assigned |
| last_indexed |
2024-03-01T14:33:06.712Z |
| geogr_code_person |
not assigned |
| openURL |
url_ver=Z39.88-2004&ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fvufind.svn.sourceforge.net%3Agenerator&rft.title=Inference+of+combinatorial+Boolean+rules+of+synergistic+gene+sets+from+cancer+microarray+datasets&rft.date=2010-06-15&genre=article&issn=1367-4803&volume=26&issue=12&spage=1506&epage=1512&pages=1506-1512&jtitle=Bioinformatics&atitle=Inference+of+combinatorial+Boolean+rules+of+synergistic+gene+sets+from+cancer+microarray+datasets&aulast=Lee&aufirst=Doheon&rft_id=info%3Adoi%2F10.1093%2Fbioinformatics%2Fbtq207&rft.language%5B0%5D=eng |
| SOLR | |
| _version_ | 1792334718308450307 |
| author | Park, Inho, Lee, Kwang H., Lee, Doheon |
| author_facet | Park, Inho, Lee, Kwang H., Lee, Doheon, Park, Inho, Lee, Kwang H., Lee, Doheon |
| author_sort | park, inho |
| container_issue | 12 |
| container_start_page | 1506 |
| container_title | Bioinformatics |
| container_volume | 26 |
| description | <jats:title>Abstract</jats:title> <jats:p>Motivation: Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently.</jats:p> <jats:p>Results: We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology.</jats:p> <jats:p>Availability: Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/∼ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material.</jats:p> <jats:p>Contact: khlee@biosoft.kaist.ac.kr; dhlee@biosoft.kaist.ac.kr</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> |
| doi_str_mv | 10.1093/bioinformatics/btq207 |
| facet_avail | Online, Free |
| finc_class_facet | Mathematik, Informatik, Biologie, Chemie und Pharmazie |
| format | ElectronicArticle |
| format_de105 | Article, E-Article |
| format_de14 | Article, E-Article |
| format_de15 | Article, E-Article |
| format_de520 | Article, E-Article |
| format_de540 | Article, E-Article |
| format_dech1 | Article, E-Article |
| format_ded117 | Article, E-Article |
| format_degla1 | E-Article |
| format_del152 | Buch |
| format_del189 | Article, E-Article |
| format_dezi4 | Article |
| format_dezwi2 | Article, E-Article |
| format_finc | Article, E-Article |
| format_nrw | Article, E-Article |
| geogr_code | not assigned |
| geogr_code_person | not assigned |
| id | ai-49-aHR0cDovL2R4LmRvaS5vcmcvMTAuMTA5My9iaW9pbmZvcm1hdGljcy9idHEyMDc |
| imprint | Oxford University Press (OUP), 2010 |
| imprint_str_mv | Oxford University Press (OUP), 2010 |
| institution | DE-105, DE-14, DE-Ch1, DE-L229, DE-D275, DE-Bn3, DE-Brt1, DE-D161, DE-Zwi2, DE-Gla1, DE-Zi4, DE-15, DE-Pl11, DE-Rs1 |
| issn | 1367-4811, 1367-4803 |
| issn_str_mv | 1367-4811, 1367-4803 |
| language | English |
| last_indexed | 2024-03-01T14:33:06.712Z |
| match_str | park2010inferenceofcombinatorialbooleanrulesofsynergisticgenesetsfromcancermicroarraydatasets |
| mega_collection | Oxford University Press (OUP) (CrossRef) |
| physical | 1506-1512 |
| publishDate | 2010 |
| publishDateSort | 2010 |
| publisher | Oxford University Press (OUP) |
| record_format | ai |
| recordtype | ai |
| series | Bioinformatics |
| source_id | 49 |
| spelling | Park, Inho Lee, Kwang H. Lee, Doheon 1367-4811 1367-4803 Oxford University Press (OUP) Computational Mathematics Computational Theory and Mathematics Computer Science Applications Molecular Biology Biochemistry Statistics and Probability http://dx.doi.org/10.1093/bioinformatics/btq207 <jats:title>Abstract</jats:title> <jats:p>Motivation: Gene set analysis has become an important tool for the functional interpretation of high-throughput gene expression datasets. Moreover, pattern analyses based on inferred gene set activities of individual samples have shown the ability to identify more robust disease signatures than individual gene-based pattern analyses. Although a number of approaches have been proposed for gene set-based pattern analysis, the combinatorial influence of deregulated gene sets on disease phenotype classification has not been studied sufficiently.</jats:p> <jats:p>Results: We propose a new approach for inferring combinatorial Boolean rules of gene sets for a better understanding of cancer transcriptome and cancer classification. To reduce the search space of the possible Boolean rules, we identify small groups of gene sets that synergistically contribute to the classification of samples into their corresponding phenotypic groups (such as normal and cancer). We then measure the significance of the candidate Boolean rules derived from each group of gene sets; the level of significance is based on the class entropy of the samples selected in accordance with the rules. By applying the present approach to publicly available prostate cancer datasets, we identified 72 significant Boolean rules. Finally, we discuss several identified Boolean rules, such as the rule of glutathione metabolism (down) and prostaglandin synthesis regulation (down), which are consistent with known prostate cancer biology.</jats:p> <jats:p>Availability: Scripts written in Python and R are available at http://biosoft.kaist.ac.kr/∼ihpark/. The refined gene sets and the full list of the identified Boolean rules are provided in the Supplementary Material.</jats:p> <jats:p>Contact: khlee@biosoft.kaist.ac.kr; dhlee@biosoft.kaist.ac.kr</jats:p> <jats:p>Supplementary information: Supplementary data are available at Bioinformatics online.</jats:p> Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets Bioinformatics |
| spellingShingle | Park, Inho, Lee, Kwang H., Lee, Doheon, Bioinformatics, Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets, Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| title | Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_full | Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_fullStr | Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_full_unstemmed | Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_short | Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| title_sort | inference of combinatorial boolean rules of synergistic gene sets from cancer microarray datasets |
| title_unstemmed | Inference of combinatorial Boolean rules of synergistic gene sets from cancer microarray datasets |
| topic | Computational Mathematics, Computational Theory and Mathematics, Computer Science Applications, Molecular Biology, Biochemistry, Statistics and Probability |
| url | http://dx.doi.org/10.1093/bioinformatics/btq207 |