A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCER
Motivation: Many traditional clinical prognostic factors have been known for cancer for years, but usually provide poor survival prediction. Genomic information is more easily available now which offers opportunities to build more accurate prognostic models. The challenge is how to integrate them to...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Cifra Publishing House
2016-09-01
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| Series: | Journal of Bioinformatics and Genomics |
| Online Access: | http://journal-biogen.org/article/view/9 |
| _version_ | 1818929688130617344 |
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| author | Yan Li Xinyan Zhang Tomi Akinyemiju Akinyemi I. Ojesina Jeff M. Szychowski Nianjun Liu Bo Xu Nengjun Yi |
| author_facet | Yan Li Xinyan Zhang Tomi Akinyemiju Akinyemi I. Ojesina Jeff M. Szychowski Nianjun Liu Bo Xu Nengjun Yi |
| author_sort | Yan Li |
| collection | DOAJ |
| description | Motivation: Many traditional clinical prognostic factors have been known for cancer for years, but usually provide poor survival prediction. Genomic information is more easily available now which offers opportunities to build more accurate prognostic models. The challenge is how to integrate them to improve survival prediction. The common approach of jointly analyzing all type of covariates directly in one single model may not improve the prediction due to increased model complexity and cannot be easily applied to different datasets.
Results: We proposed a two-stage procedure to better combine different sources of information for survival prediction, and applied the two-stage procedure in two cancer datasets: myelodysplastic syndromes (MDS) and ovarian cancer. Our analysis suggests that the prediction performance of different data types are very different, and combining clinical, gene expression and mutation data using the two-stage procedure improves survival prediction in terms of improved concordance index and reduced prediction error.
Availability and implementation: The two-stage procedure can be implemented in BhGLM package which is freely available at http://www.ssg.uab.edu/bhglm/.
Keywords: gene expansion, mutation, clinical data, survival prediction, myelodysplastic syndromes, ovarian cancer. |
| first_indexed | 2024-12-20T03:48:46Z |
| format | Article |
| id | doaj.art-b25a83948b9a409a9d30f3ad72a0db34 |
| institution | Directory Open Access Journal |
| issn | 2530-1381 |
| language | English |
| last_indexed | 2024-12-20T03:48:46Z |
| publishDate | 2016-09-01 |
| publisher | Cifra Publishing House |
| record_format | Article |
| series | Journal of Bioinformatics and Genomics |
| spelling | doaj.art-b25a83948b9a409a9d30f3ad72a0db342022-12-21T19:54:31ZengCifra Publishing HouseJournal of Bioinformatics and Genomics2530-13812016-09-011 (1)10.18454/jbg.2016.1.1.29A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCERYan Li0Xinyan Zhang1Tomi Akinyemiju2Akinyemi I. Ojesina3Jeff M. Szychowski4Nianjun Liu5Bo Xu6Nengjun Yi7Department of Biostatistics, University of Alabama at BirminghamDepartment of Biostatistics, University of Alabama at BirminghamDepartment of Epidemiology, University of Alabama at BirminghamDepartment of Epidemiology, University of Alabama at BirminghamDepartment of Biostatistics, University of Alabama at BirminghamDepartment of Biostatistics, University of Alabama at BirminghamDepartment of Oncology, Southern Research InstituteUniversity of Alabama at BirminghamMotivation: Many traditional clinical prognostic factors have been known for cancer for years, but usually provide poor survival prediction. Genomic information is more easily available now which offers opportunities to build more accurate prognostic models. The challenge is how to integrate them to improve survival prediction. The common approach of jointly analyzing all type of covariates directly in one single model may not improve the prediction due to increased model complexity and cannot be easily applied to different datasets. Results: We proposed a two-stage procedure to better combine different sources of information for survival prediction, and applied the two-stage procedure in two cancer datasets: myelodysplastic syndromes (MDS) and ovarian cancer. Our analysis suggests that the prediction performance of different data types are very different, and combining clinical, gene expression and mutation data using the two-stage procedure improves survival prediction in terms of improved concordance index and reduced prediction error. Availability and implementation: The two-stage procedure can be implemented in BhGLM package which is freely available at http://www.ssg.uab.edu/bhglm/. Keywords: gene expansion, mutation, clinical data, survival prediction, myelodysplastic syndromes, ovarian cancer.http://journal-biogen.org/article/view/9 |
| spellingShingle | Yan Li Xinyan Zhang Tomi Akinyemiju Akinyemi I. Ojesina Jeff M. Szychowski Nianjun Liu Bo Xu Nengjun Yi A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCER Journal of Bioinformatics and Genomics |
| title | A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCER |
| title_full | A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCER |
| title_fullStr | A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCER |
| title_full_unstemmed | A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCER |
| title_short | A TWO-STAGE APPROACH FOR COMBINING GENE EXPRESSION AND MUTATION WITH CLINICAL DATA IMPROVES SURVIVAL PREDICTION IN MYELODYSPLASTIC SYNDROMES AND OVARIAN CANCER |
| title_sort | two stage approach for combining gene expression and mutation with clinical data improves survival prediction in myelodysplastic syndromes and ovarian cancer |
| url | http://journal-biogen.org/article/view/9 |
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