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Prediction of Time to Tumor Recurrence in Ovarian Cancer: Comparison of Three Sparse Regression Methods

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Bioinformatics Research and Applications (ISBRA 2017)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10330))

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Abstract

Ovarian cancer is the most fatal gynecological malignancy among women. Making a reliable prediction of time to tumor recurrence would be a valuable contribution to post-surgery follow-up care. In this paper we study three well-known data sets, known as TCGA, Tothill and Yoshihara, and compare three sparse regression methods, two of which (LASSO and EN) are well-known and the third (CLOT) is from our laboratory. It is established that the three data sets are very different from each other. Therefore a two-stage predictor is built, whereby each test sample is first assigned to the most likely data set and then the corresponding predictor is used. The weighted concordance of each regression method is computed to compare the methods and select the best one. CLOT uses a biomarker panel of 103 genes and achieves a concordance index of 0.7829, which is higher than that achieved by the other two methods.

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Notes

  1. 1.

    International Federation of Gynecology and Obstetrics.

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Authors and Affiliations

  1. University of Texas at Dallas, Richardson, TX, 75080, USA

    Mahsa Lotfi & Mathukumalli Vidyasagar

  2. Harvard University, Cambridge, MA, 02138, USA

    Burook Misganaw

Authors
  1. Mahsa Lotfi
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  2. Burook Misganaw
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  3. Mathukumalli Vidyasagar
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Corresponding author

Correspondence to Mahsa Lotfi .

Editor information

Editors and Affiliations

  1. Georgia State University, Atlanta, Georgia, USA

    Zhipeng Cai

  2. University of Texas at Dallas, Richardson, Texas, USA

    Ovidiu Daescu

  3. Central South University, Changsha, China

    Min Li

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Lotfi, M., Misganaw, B., Vidyasagar, M. (2017). Prediction of Time to Tumor Recurrence in Ovarian Cancer: Comparison of Three Sparse Regression Methods. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_1

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  • DOI: https://doi.org/10.1007/978-3-319-59575-7_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59574-0

  • Online ISBN: 978-3-319-59575-7

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