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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 周承復 | zh_TW |
| dc.contributor.advisor | Cheng-Fu Chou | en |
| dc.contributor.author | 葉瓊斯 | zh_TW |
| dc.contributor.author | Chiung-Szu Yeh | en |
| dc.date.accessioned | 2023-08-16T17:18:21Z | - |
| dc.date.available | 2023-11-09 | - |
| dc.date.copyright | 2023-08-16 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-08-09 | - |
| dc.identifier.citation | [1] E. Murphy, "Forensic DNA Typing," Annual Review of Criminology, vol. 1, pp. 497-515, Jan. 2018.
[2] R. Saad, "Discovery, Development, and Current Applications," Baylor University Medical Center Proceedings, pp. 130-133, Apr. 2005. [3] S. B. Nizami, S. Z. H. Kazmi, F. Abid, M. M. Babar, A. Noor, N.-S. S. Zaidi, S. U. Khan, H. Hasan, M. Ali, A. Gul, "Chapter 6 - Omics Approaches in Forensic Biotechnology: Looking for Ancestry to Offence," in Omics Technologies and Bio-Engineering, Academic Press, 2018, pp. 111-129. [4] J. M. Butler, Forensic DNA Typing: Biology and Technology behind STR Markers, London: Academic Press, 2001. [5] S. Srinivasan, JA. Clements, J. Batra, "Single nucleotide polymorphisms in clinics: fantasy or reality for cancer?," Critical Reviews in Clinical Laboratory Sciences, vol. 53, pp. 29-39, 2016. [6] Ø. Bleka, G. Storvik, P. Gill, "EuroForMix: An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts," Forensic Science International: Genetics, vol. 21, pp. 35-44, Mar. 2016. [7] M. D. Coble, J.-A. Bright, "Probabilistic genotyping software: An overview," Forensic Science International: Genetics, vol. 38, pp. 219-224, Jan. 2019. [8] C. Y. Hu, "Applying Deep Neural Network for STR-based DNA Mixture Interpretation," 2020. [9] H.-L. Hwa, T.-W. Yang, Y.-H. Li, C.-F. Chou, F.-P. Lai, Y.-H. Chien, H.-I. Yin, T.-T. Lee , "DNA mixture interpretation using linear regression and neural networks on massively parallel sequencing data of single nucleotide polymorphisms," Australian Journal of Forensic Sciences , vol. 54, pp. 150-162, Apr. 2021. [10] R. Press, "DNA Mixtures: A Forensic Science Explainer," NIST, 2019. [11] M. T. Ribeiro, S. Singh, C. Guestrin, "“Why Should I Trust You?” Explaining the Predictions of Any Classifier," in KDD, 2016. [12] S. M. Lundberg, S.-L. Lee, "A Unified Approach to Interpreting Model Predictions," in NIPS, 2017. [13] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel, "Backpropagation Applied to Handwritten Zip Code Recognition," Neural Computation, vol. 1, pp. 541-551, Dec. 1989. [14] D. E. Rumelhart, G. E. Hinton, R. J. Williams, "Learning representations by back-propagating errors," Nature, vol. 323, pp. 533-536, Oct. 1986. [15] D. P. Kingma, J. Ba, "Adam: A Method for Stochastic Optimization," in ICLR, 2015. [16] J. Han, C. Moraga, "The influence of the sigmoid function parameters on the speed of backpropagation learning.," in IWANN: International Work-Conference on Artificial Neural Networks, 1995. [17] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, R. Salakhutdinov, "Dropout: a simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, vol. 15, pp. 1929–1958, Jan. 2014. [18] S. Ioffe, C. Szegedy, "Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift," in PMLR, 2015. [19] L. Breiman, A. Cutler, "Random Forests," Machine Learning, pp. 157-176, Jan. 2011. [20] C. Cortes, V. Vapnik , "Support-vector networks," Machine Learning, p. 273–297, Sep. 1995. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89142 | - |
| dc.description.abstract | 短縱列重複序列 (short tandem repeat; STR) 和單一核苷酸多型性 (single nucleotide polymorphism; SNP) 已廣泛用於 DNA 混合物解釋的臨床研究和法醫應用，例如透過 STR 和 SNP 信息識別個體。
在本論文中，我們提出了一個可解釋的 DNA 混合物解釋模型。透過識別重要位點並聯合考慮STR和SNP信息來提高解釋準確性。此外，我們還將這種方法應用於“觸摸樣本”(Touch sample)，指透過觸碰後殘留在物體上極少量的DNA。如結果所示，我們得知此方法是有效的。 | zh_TW |
| dc.description.abstract | Short tandem repeat (STR) and single nucleotide polymorphisms (SNP) have been widely used in clinical research and forensic applications for DNA mixture interpretation, identifying individuals through STR and SNP information.
In this thesis, we propose an explainable model for DNA mixture interpretation. Through identifying important locus and jointly considering STR and SNP information to improve the interpretation accuracy. We also applied this method to “touch sample”, which contains extremely small amounts of DNA that left on an object after it has been touched. From the results, we learned that our method is effective. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-16T17:18:21Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-08-16T17:18:21Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 #
誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 Chapter 2 Related Work 7 2.1 Probabilistic Genotyping Software 7 2.2 Neural Networks 8 Chapter 3 Problem Clarification 9 3.1 Problem Description 9 3.2 Datasets 10 3.2.1 Data Properties 10 3.2.2 Dataset A 12 3.2.3 Dataset B 16 3.3 Data Field 19 3.3.1 STR Data 19 3.3.2 SNP Data 22 Chapter 4 Methodology 25 4.1 STR 26 4.1.1 Data preprocess 26 4.1.2 First-time training 34 4.1.3 Identify important locus 36 4.1.4 Second-time training 37 4.2 SNP 39 4.2.1 Data preprocess 40 4.2.2 First-time training 42 4.2.3 Identify important locus 43 4.2.4 Second-time training 44 4.3 Fusion Layer 45 Chapter 5 Results 46 5.1 STR 46 5.2 SNP 47 5.3 Fusion 48 5.4 Comparison 52 5.5 Touch DNA 54 Chapter 6 Conclusion 55 REFERENCES 56 | - |
| dc.language.iso | en | - |
| dc.subject | 單一核苷酸多型性 (single nucleotide polymorphism; SNP) | zh_TW |
| dc.subject | 神經網路 (neural networks) | zh_TW |
| dc.subject | 深度學習 (deep learning) | zh_TW |
| dc.subject | 機器學習 (machine learning) | zh_TW |
| dc.subject | DNA混合物 (DNA mixture) | zh_TW |
| dc.subject | 短縱列重複序列 (short tandem repeat; STR) | zh_TW |
| dc.subject | Machine learning | en |
| dc.subject | neural networks | en |
| dc.subject | short tandem repeat (STR) | en |
| dc.subject | single nucleotide polymorphism (SNP) | en |
| dc.subject | deep learning | en |
| dc.subject | DNA mixture | en |
| dc.title | 應用深度神經網路進行基於STR和SNP的DNA混合物解釋 | zh_TW |
| dc.title | Using Deep Neural Networks for DNA Mixture Interpretation by Jointly Considering STR and SNP | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 111-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 吳曉光;李明穗;吳振吉;華筱玲 | zh_TW |
| dc.contributor.oralexamcommittee | Hsiao-Kuang Wu;Ming-Sui Lee;Chen-Chi Wu;Hsiao-Lin Hwa | en |
| dc.subject.keyword | 機器學習 (machine learning),深度學習 (deep learning),神經網路 (neural networks),短縱列重複序列 (short tandem repeat; STR),單一核苷酸多型性 (single nucleotide polymorphism; SNP),DNA混合物 (DNA mixture), | zh_TW |
| dc.subject.keyword | Machine learning,deep learning,neural networks,short tandem repeat (STR),single nucleotide polymorphism (SNP),DNA mixture, | en |
| dc.relation.page | 58 | - |
| dc.identifier.doi | 10.6342/NTU202303530 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2023-08-10 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | - |
| dc.date.embargo-lift | 2028-08-08 | - |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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