Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer

Sheida Nabavi; Mayinuer Maitituoheti; Michiyo Yamada; Peter Tonellato

doi:10.1109/NEBEC.2014.6972886

Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer

Sheida Nabavi, Mayinuer Maitituoheti, Michiyo Yamada, Peter Tonellato

Houston Methodist

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

2 Scopus citations

Abstract

Drug resistance is one of the major challenges in the treatment of ovarian cancer. To facilitate identification of candidate biomarkers of resistant to platinum-based chemotherapy in ovarian cancer, we employed statistical machine learning techniques and integrative genomic data analysis. We used gene expression, somatic mutation and copy number aberration data of platinum sensitive and resistant tumors from the cancer genome atlas. Using regression tree and module network analysis, we identified genes that both contain mutations (copy number aberration and/or point mutation) and their expressions influence groups of their co-regulated genes for resistant and sensitive tumors. Finally, we compared these two gene lists and their associated pathways to extract a short list of genes as potential biomarkers of resistant to platinum-based chemotherapy.

Original language	English (US)
Title of host publication	Proceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781479937288
DOIs	https://doi.org/10.1109/NEBEC.2014.6972886
State	Published - Dec 2 2014
Event	2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014 - Boston, United States Duration: Apr 25 2014 → Apr 27 2014

Publication series

Name	Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC
Volume	2014-December
ISSN (Print)	1071-121X
ISSN (Electronic)	2160-7001

Conference

Conference	2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014
Country/Territory	United States
City	Boston
Period	4/25/14 → 4/27/14

Keywords

copy number aberration
gene expression
integrative analysis
module network analysis
regression tree

ASJC Scopus subject areas

Bioengineering

Access to Document

10.1109/NEBEC.2014.6972886

Cite this

Nabavi, S., Maitituoheti, M., Yamada, M., & Tonellato, P. (2014). Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer. In Proceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014 Article 6972886 (Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC; Vol. 2014-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/NEBEC.2014.6972886

Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer. / Nabavi, Sheida; Maitituoheti, Mayinuer; Yamada, Michiyo et al.
Proceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. 6972886 (Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC; Vol. 2014-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nabavi, S, Maitituoheti, M, Yamada, M & Tonellato, P 2014, Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer. in Proceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014., 6972886, Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC, vol. 2014-December, Institute of Electrical and Electronics Engineers Inc., 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014, Boston, United States, 4/25/14. https://doi.org/10.1109/NEBEC.2014.6972886

Nabavi S, Maitituoheti M, Yamada M, Tonellato P. Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer. In Proceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014. Institute of Electrical and Electronics Engineers Inc. 2014. 6972886. (Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC). doi: 10.1109/NEBEC.2014.6972886

Nabavi, Sheida ; Maitituoheti, Mayinuer ; Yamada, Michiyo et al. / Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer. Proceedings - 2014 40th Annual Northeast Bioengineering Conference, NEBEC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. (Proceedings of the IEEE Annual Northeast Bioengineering Conference, NEBEC).

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abstract = "Drug resistance is one of the major challenges in the treatment of ovarian cancer. To facilitate identification of candidate biomarkers of resistant to platinum-based chemotherapy in ovarian cancer, we employed statistical machine learning techniques and integrative genomic data analysis. We used gene expression, somatic mutation and copy number aberration data of platinum sensitive and resistant tumors from the cancer genome atlas. Using regression tree and module network analysis, we identified genes that both contain mutations (copy number aberration and/or point mutation) and their expressions influence groups of their co-regulated genes for resistant and sensitive tumors. Finally, we compared these two gene lists and their associated pathways to extract a short list of genes as potential biomarkers of resistant to platinum-based chemotherapy.",

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AB - Drug resistance is one of the major challenges in the treatment of ovarian cancer. To facilitate identification of candidate biomarkers of resistant to platinum-based chemotherapy in ovarian cancer, we employed statistical machine learning techniques and integrative genomic data analysis. We used gene expression, somatic mutation and copy number aberration data of platinum sensitive and resistant tumors from the cancer genome atlas. Using regression tree and module network analysis, we identified genes that both contain mutations (copy number aberration and/or point mutation) and their expressions influence groups of their co-regulated genes for resistant and sensitive tumors. Finally, we compared these two gene lists and their associated pathways to extract a short list of genes as potential biomarkers of resistant to platinum-based chemotherapy.

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Application of statistical machine learning in identifying candidate biomarkers of resistant to anti-cancer drugs in ovarian cancer

Abstract

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Keywords

ASJC Scopus subject areas

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