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Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment

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  • Yujin Hoshida

Abstract

Gene-expression signature-based disease classification and clinical outcome prediction has not been widely introduced in clinical medicine as initially expected, mainly due to the lack of extensive validation needed for its clinical deployment. Obstacles include variable measurement in microarray assay, inconsistent assay platform, analytical requirement for comparable pair of training and test datasets, etc. Furthermore, as medical device helping clinical decision making, the prediction needs to be made for each single patient with a measure of its reliability. To address these issues, there is a need for flexible prediction method less sensitive to difference in experimental and analytical conditions, applicable to each single patient, and providing measure of prediction confidence. The nearest template prediction (NTP) method provides a convenient way to make class prediction with assessment of prediction confidence computed in each single patient's gene-expression data using only a list of signature genes and a test dataset. We demonstrate that the method can be flexibly applied to cross-platform, cross-species, and multiclass predictions without any optimization of analysis parameters.

Suggested Citation

  • Yujin Hoshida, 2010. "Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment," PLOS ONE, Public Library of Science, vol. 5(11), pages 1-8, November.
  • Handle: RePEc:plo:pone00:0015543
    DOI: 10.1371/journal.pone.0015543
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    References listed on IDEAS

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    1. Laura J. van 't Veer & René Bernards, 2008. "Enabling personalized cancer medicine through analysis of gene-expression patterns," Nature, Nature, vol. 452(7187), pages 564-570, April.
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    1. Young Taek Oh & Hee Jin Cho & Jinkuk Kim & Ji-Hyun Lee & Kyoohyoung Rho & Yun-Jee Seo & Yeon-Sook Choi & Hye Jin Jung & Hyeon Suk Song & Doo-Sik Kong & Ho Jun Seol & Jung-Il Lee & Yeup Yoon & Sunghoon, 2014. "Translational Validation of Personalized Treatment Strategy Based on Genetic Characteristics of Glioblastoma," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-11, August.
    2. Masashi Fujita & Mei-Ju May Chen & Doris Rieko Siwak & Shota Sasagawa & Ayako Oosawa-Tatsuguchi & Koji Arihiro & Atsushi Ono & Ryoichi Miura & Kazuhiro Maejima & Hiroshi Aikata & Masaki Ueno & Shinya , 2022. "Proteo-genomic characterization of virus-associated liver cancers reveals potential subtypes and therapeutic targets," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    3. Zhongqi Fan & Xinchen Zou & Guangyi Wang & Yahui Liu & Yanfang Jiang & Haoyan Wang & Ping Zhang & Feng Wei & Xiaohong Du & Meng Wang & Xiaodong Sun & Bai Ji & Xintong Hu & Liguo Chen & Peiwen Zhou & D, 2024. "A transcriptome based molecular classification scheme for cholangiocarcinoma and subtype-derived prognostic biomarker," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Christel F. A. Ramirez & Daniel Taranto & Masami Ando-Kuri & Marnix H. P. Groot & Efi Tsouri & Zhijie Huang & Daniel Groot & Roelof J. C. Kluin & Daan J. Kloosterman & Joanne Verheij & Jing Xu & Seren, 2024. "Cancer cell genetics shaping of the tumor microenvironment reveals myeloid cell-centric exploitable vulnerabilities in hepatocellular carcinoma," Nature Communications, Nature, vol. 15(1), pages 1-24, December.
    5. Sally Yepes & Maria Mercedes Torres & Rafael E Andrade, 2015. "Clustering of Expression Data in Chronic Lymphocytic Leukemia Reveals New Molecular Subdivisions," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-20, September.

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