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Genome-Wide Feature Selection of Robust mRNA Biomarkers for Body Fluid Identification

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Data Mining and Big Data (DMBD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1745))

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Abstract

Tracing the origins of body fluids, which can provide information linking sample donors with criminal acts, is one of the primary challenges facing forensic medicine. Gene expression profiling methods have been widely developed to identify biomarkers for body fluid identification. In this study, we systematically investigated large-scale, multi-category, high-throughput gene expression data and identified 36 high potential body fluid-specific mRNAs with robust discriminability based on decision tree models. Robustly expressed reference genes were selected for normalization, which further improved the accuracy. Results on independent datasets suggested the robust performance and good generalizability of our biomarkers. In addition, simulated data indicated that our biomarkers could also be employed for accurate body fluid mixture deconvolution. We believe our methods may facilitate body fluid identification and provide insights into forensic crime scene reconstruction.

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Acknowledgements

We are grateful to all the participants in this study. This work was supported by Beijing Natural Science Foundation (No. 7212065). We are also grateful to the GTEx program, GEO, HPA database and corresponding data contributors for making their enormous database and resources available. The Genotype-Tissue Expression (GTEx) Project was supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH, and NINDS. The data used for the analyses described in this manuscript from GTEx were obtained from: the GTEx Portal on 05/04/2019 and dbGaP accession number phs000424.v7.p2 on 05/04/2019.

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Authors and Affiliations

  1. Department of Medical Bioinformatics, School of Basic Medical Sciences, Peking University, Beijing, 100191, China

    Guangyi He & Ence Yang

  2. Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University, Beijing, 100191, China

    Liming Xiao & Ence Yang

  3. Enlight Medical Technologies (Shanghai) Co., Ltd., Banxia Road, Pudong New Area, Shanghai, 201318, China

    Yingnan Bian

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  1. Guangyi He
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Correspondence to Yingnan Bian or Ence Yang .

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Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

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He, G., Xiao, L., Bian, Y., Yang, E. (2022). Genome-Wide Feature Selection of Robust mRNA Biomarkers for Body Fluid Identification. In: Tan, Y., Shi, Y. (eds) Data Mining and Big Data. DMBD 2022. Communications in Computer and Information Science, vol 1745. Springer, Singapore. https://doi.org/10.1007/978-981-19-8991-9_3

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  • DOI: https://doi.org/10.1007/978-981-19-8991-9_3

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  • Print ISBN: 978-981-19-8990-2

  • Online ISBN: 978-981-19-8991-9

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