請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91202完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏安祺 | zh_TW |
| dc.contributor.advisor | An-Chi Wei | en |
| dc.contributor.author | 林宇恆 | zh_TW |
| dc.contributor.author | Yu-Heng Lin | en |
| dc.date.accessioned | 2023-12-12T16:11:25Z | - |
| dc.date.available | 2023-12-13 | - |
| dc.date.copyright | 2023-12-12 | - |
| dc.date.issued | 2023 | - |
| dc.date.submitted | 2023-12-01 | - |
| dc.identifier.citation | [1] A. M. Richard et al., “The tox21 10K compound library: collaborative chemistry advancing toxicology.,” Chem. Res. Toxicol., vol. 34, no. 2, pp. 189–216, Feb. 2021, doi: 10.1021/acs.chemrestox.0c00264.
[2] S. Kim et al., “PubChem Substance and Compound databases.,” Nucleic Acids Res., vol. 44, no. D1, pp. D1202-13, Jan. 2016, doi: 10.1093/nar/gkv951. [3] J. A. Dykens and Y. Will, “The significance of mitochondrial toxicity testing in drug development.,” Drug Discov. Today, vol. 12, no. 17–18, pp. 777–785, Sep. 2007, doi: 10.1016/j.drudis.2007.07.013. [4] F. Bartolomé and A. Y. Abramov, “Measurement of mitochondrial NADH and FAD autofluorescence in live cells.,” Methods Mol. Biol., vol. 1264, pp. 263–270, 2015, doi: 10.1007/978-1-4939-2257-4_23. [5] A. I. Jonckheere, J. A. M. Smeitink, and R. J. T. Rodenburg, “Mitochondrial ATP synthase: architecture, function and pathology.,” J. Inherit. Metab. Dis., vol. 35, no. 2, pp. 211–225, Mar. 2012, doi: 10.1007/s10545-011-9382-9. [6] L. D. Osellame, T. S. Blacker, and M. R. Duchen, “Cellular and molecular mechanisms of mitochondrial function.,” Best Pract. Res. Clin. Endocrinol. Metab., vol. 26, no. 6, pp. 711–723, Dec. 2012, doi: 10.1016/j.beem.2012.05.003. [7] Y. Maeda and J. Chida, “Control of cell differentiation by mitochondria, typically evidenced in dictyostelium development.,” Biomolecules, vol. 3, no. 4, pp. 943–966, Nov. 2013, doi: 10.3390/biom3040943. [8] M. Golpich, E. Amini, Z. Mohamed, R. Azman Ali, N. Mohamed Ibrahim, and A. Ahmadiani, “Mitochondrial Dysfunction and Biogenesis in Neurodegenerative diseases: Pathogenesis and Treatment.,” CNS Neurosci. Ther., vol. 23, no. 1, pp. 5–22, Jan. 2017, doi: 10.1111/cns.12655. [9] F. Liu, J. Lu, A. Manaenko, J. Tang, and Q. Hu, “Mitochondria in ischemic stroke: new insight and implications.,” Aging Dis., vol. 9, no. 5, pp. 924–937, Oct. 2018, doi: 10.14336/AD.2017.1126. [10] S. Seal, J. Carreras-Puigvert, M.-A. Trapotsi, H. Yang, O. Spjuth, and A. Bender, “Integrating cell morphology with gene expression and chemical structure to aid mitochondrial toxicity detection.,” Commun. Biol., vol. 5, no. 1, p. 858, Aug. 2022, doi: 10.1038/s42003-022-03763-5. [11] S. Sakamuru et al., “Application of a homogenous membrane potential assay to assess mitochondrial function.,” Physiol. Genomics, vol. 44, no. 9, pp. 495–503, May 2012, doi: 10.1152/physiolgenomics.00161.2011. [12] R. C. Scaduto and L. W. Grotyohann, “Measurement of mitochondrial membrane potential using fluorescent rhodamine derivatives.,” Biophys. J., vol. 76, no. 1 Pt 1, pp. 469–477, Jan. 1999, doi: 10.1016/S0006-3495(99)77214-0. [13] S. W. Perry, J. P. Norman, J. Barbieri, E. B. Brown, and H. A. Gelbard, “Mitochondrial membrane potential probes and the proton gradient: a practical usage guide.,” BioTechniques, vol. 50, no. 2, pp. 98–115, Feb. 2011, doi: 10.2144/000113610. [14] J. Eakins et al., “A combined in vitro approach to improve the prediction of mitochondrial toxicants.,” Toxicol In Vitro, vol. 34, pp. 161–170, Aug. 2016, doi: 10.1016/j.tiv.2016.03.016. [15] V. M. Gohil et al., “Nutrient-sensitized screening for drugs that shift energy metabolism from mitochondrial respiration to glycolysis.,” Nat. Biotechnol., vol. 28, no. 3, pp. 249–255, Mar. 2010, doi: 10.1038/nbt.1606. [16] M. S. Attene-Ramos et al., “Profiling of the Tox21 chemical collection for mitochondrial function to identify compounds that acutely decrease mitochondrial membrane potential.,” Environ. Health Perspect., vol. 123, no. 1, pp. 49–56, Jan. 2015, doi: 10.1289/ehp.1408642. [17] A. Rosell-Hidalgo, A. L. Moore, and T. Ghafourian, “Prediction of drug-induced mitochondrial dysfunction using succinate-cytochrome c reductase activity, QSAR and molecular docking.,” Toxicology, vol. 485, p. 153412, Feb. 2023, doi: 10.1016/j.tox.2022.153412. [18] S. Wang et al., “Comparison of modes of action between fish, cell and mitochondrial toxicity based on toxicity correlation, excess toxicity and QSAR for class-based compounds.,” Toxicology, vol. 470, p. 153155, Mar. 2022, doi: 10.1016/j.tox.2022.153155. [19] H. Zhang, Q.-Y. Chen, M.-L. Xiang, C.-Y. Ma, Q. Huang, and S.-Y. Yang, “In silico prediction of mitochondrial toxicity by using GA-CG-SVM approach.,” Toxicol In Vitro, vol. 23, no. 1, pp. 134–140, Feb. 2009, doi: 10.1016/j.tiv.2008.09.017. [20] E. Semenova, D. P. Williams, A. M. Afzal, and S. E. Lazic, “A Bayesian neural network for toxicity prediction,” Computational Toxicology, vol. 16, p. 100133, Nov. 2020, doi: 10.1016/j.comtox.2020.100133. [21] P. Zhao et al., “In silico prediction of mitochondrial toxicity of chemicals using machine learning methods.,” J. Appl. Toxicol., vol. 41, no. 10, pp. 1518–1526, Oct. 2021, doi: 10.1002/jat.4141. [22] F. Bringezu, J. Carlos Gómez-Tamayo, and M. Pastor, “Ensemble prediction of mitochondrial toxicity using machine learning technology,” Computational Toxicology, vol. 20, p. 100189, Nov. 2021, doi: 10.1016/j.comtox.2021.100189. [23] J. Hemmerich, F. Troger, B. Füzi, and G. F Ecker, “Using machine learning methods and structural alerts for prediction of mitochondrial toxicity.,” Mol. Inform., vol. 39, no. 5, p. e2000005, May 2020, doi: 10.1002/minf.202000005. [24] S. J. Wijeyesakere, D. Wilson, T. Auernhammer, A. Parks, D. Kovacs, and M. S. Marty, “Hybrid Machine-Learning/SMARTS Profiling Model for Mitochondrial Inhibition,” Applied In Vitro Toxicology, vol. 5, no. 4, pp. 196–204, Dec. 2019, doi: 10.1089/aivt.2019.0010. [25] A. Gaulton et al., “ChEMBL: a large-scale bioactivity database for drug discovery.,” Nucleic Acids Res., vol. 40, no. Database issue, pp. D1100-7, Jan. 2012, doi: 10.1093/nar/gkr777. [26] D. S. Wishart et al., “DrugBank 5.0: a major update to the DrugBank database for 2018.,” Nucleic Acids Res., vol. 46, no. D1, pp. D1074–D1082, Jan. 2018, doi: 10.1093/nar/gkx1037. [27] K. Jaganathan, M. U. Rehman, H. Tayara, and K. T. Chong, “XML-CIMT: Explainable Machine Learning (XML) Model for Predicting Chemical-Induced Mitochondrial Toxicity.,” Int. J. Mol. Sci., vol. 23, no. 24, Dec. 2022, doi: 10.3390/ijms232415655. [28] E. O. Perlstein, “Drug repurposing for mitochondrial diseases using a pharmacological model of complex I deficiency in the yeast Yarrowia lipolytica,” BioRxiv, Jan. 2020, doi: 10.1101/2020.01.08.899666. [29] L. Naia et al., “Neuronal cell-based high-throughput screen for enhancers of mitochondrial function reveals luteolin as a modulator of mitochondria-endoplasmic reticulum coupling.,” BMC Biol., vol. 19, no. 1, p. 57, Mar. 2021, doi: 10.1186/s12915-021-00979-5. [30] M. Xia et al., “Comprehensive Analyses and Prioritization of Tox21 10K Chemicals Affecting Mitochondrial Function by in-Depth Mechanistic Studies.,” Environ. Health Perspect., vol. 126, no. 7, p. 077010, Jul. 2018, doi: 10.1289/EHP2589. [31] M.-A. Bray et al., “Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes.,” Nat. Protoc., vol. 11, no. 9, pp. 1757–1774, Sep. 2016, doi: 10.1038/nprot.2016.105. [32] A. Subramanian et al., “A next generation connectivity map: L1000 platform and the first 1,000,000 profiles.,” Cell, vol. 171, no. 6, pp. 1437-1452.e17, Nov. 2017, doi: 10.1016/j.cell.2017.10.049. [33] R. J. Weaver et al., “Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models.,” Nat. Rev. Drug Discov., vol. 19, no. 2, pp. 131–148, Feb. 2020, doi: 10.1038/s41573-019-0048-x. [34] “CHEMBL database release 23,” EMBL-EBI, May 2017. doi: 10.6019/CHEMBL.database.23. [35] C. W. Yap, “PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints.,” J. Comput. Chem., vol. 32, no. 7, pp. 1466–1474, May 2011, doi: 10.1002/jcc.21707. [36] T. Ferrari, D. Cattaneo, G. Gini, N. Golbamaki Bakhtyari, A. Manganaro, and E. Benfenati, “Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction.,” SAR QSAR Environ. Res., vol. 24, no. 5, pp. 365–383, May 2013, doi: 10.1080/1062936X.2013.773376. [37] K. Jaganathan, H. Tayara, and K. T. Chong, “An explainable supervised machine learning model for predicting respiratory toxicity of chemicals using optimal molecular descriptors.,” Pharmaceutics, vol. 14, no. 4, Apr. 2022, doi: 10.3390/pharmaceutics14040832. [38] B. K. Wagner et al., “Large-scale chemical dissection of mitochondrial function.,” Nat. Biotechnol., vol. 26, no. 3, pp. 343–351, Mar. 2008, doi: 10.1038/nbt1387. [39] L. Yang et al., “Serine Catabolism Feeds NADH when Respiration Is Impaired.,” Cell Metab., vol. 31, no. 4, pp. 809-821.e6, Apr. 2020, doi: 10.1016/j.cmet.2020.02.017. [40] B. Zagidullin, Z. Wang, Y. Guan, E. Pitkänen, and J. Tang, “Comparative analysis of molecular fingerprints in prediction of drug combination effects.,” Brief. Bioinformatics, vol. 22, no. 6, Nov. 2021, doi: 10.1093/bib/bbab291. [41] H. Moriwaki, Y.-S. Tian, N. Kawashita, and T. Takagi, “Mordred: a molecular descriptor calculator.,” J. Cheminform., vol. 10, no. 1, p. 4, Feb. 2018, doi: 10.1186/s13321-018-0258-y. [42] D. Bajusz, A. Rácz, and K. Héberger, “Why is Tanimoto index an appropriate choice for fingerprint-based similarity calculations?,” J. Cheminform., vol. 7, p. 20, May 2015, doi: 10.1186/s13321-015-0069-3. [43] I. Kahn, A. Lomaka, and M. Karelson, “Topological fingerprints as an aid in finding structural patterns for LRRK2 inhibition.,” Mol. Inform., vol. 33, no. 4, pp. 269–275, Apr. 2014, doi: 10.1002/minf.201300057. [44] S. Zhong and X. Guan, “Count-Based Morgan Fingerprint: A More Efficient and Interpretable Molecular Representation in Developing Machine Learning-Based Predictive Regression Models for Water Contaminants’ Activities and Properties.,” Environ. Sci. Technol., Jul. 2023, doi: 10.1021/acs.est.3c02198. [45] J. L. Durant, B. A. Leland, D. R. Henry, and J. G. Nourse, “Reoptimization of MDL keys for use in drug discovery.,” J. Chem. Inf. Comput. Sci., vol. 42, no. 6, pp. 1273–1280, Dec. 2002, doi: 10.1021/ci010132r. [46] R. C. Staudemeyer and E. R. Morris, “Understanding LSTM -- a tutorial into Long Short-Term Memory Recurrent Neural Networks,” arXiv, 2019, doi: 10.48550/arxiv.1909.09586. [47] S. Mastromichalakis, “ALReLU: A different approach on Leaky ReLU activation function to improve Neural Networks Performance,” arXiv, 2020, doi: 10.48550/arxiv.2012.07564. [48] S. Kim et al., “PubChem 2023 update.,” Nucleic Acids Res., vol. 51, no. D1, pp. D1373–D1380, Jan. 2023, doi: 10.1093/nar/gkac956. [49] S. Jaeger, S. Fulle, and S. Turk, “Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition.,” J. Chem. Inf. Model., vol. 58, no. 1, pp. 27–35, Jan. 2018, doi: 10.1021/acs.jcim.7b00616. [50] S. Zhang, H. Tong, J. Xu, and R. Maciejewski, “Graph convolutional networks: a comprehensive review,” Compu Social Networls, vol. 6, no. 1, p. 11, Dec. 2019, doi: 10.1186/s40649-019-0069-y. [51] N. A. Verdikha, T. B. Adji, and A. E. Permanasari, “Study of Undersampling Method: Instance Hardness Threshold with Various Estimators for Hate Speech Classification,” IJITEE, vol. 2, no. 2, Dec. 2018, doi: 10.22146/ijitee.42152. [52] I. Uzieliene et al., “The Antihypertensive Drug Nifedipine Modulates the Metabolism of Chondrocytes and Human Bone Marrow-Derived Mesenchymal Stem Cells.,” Front Endocrinol (Lausanne), vol. 10, p. 756, Nov. 2019, doi: 10.3389/fendo.2019.00756. [53] P. Bouyer, Y. Zhou, and W. F. Boron, “An increase in intracellular calcium concentration that is induced by basolateral CO2 in rabbit renal proximal tubule.,” Am. J. Physiol. Renal Physiol., vol. 285, no. 4, pp. F674-87, Oct. 2003, doi: 10.1152/ajprenal.00107.2003. [54] M. A. S. Fernandes et al., “Effects of 1,4-dihydropyridine derivatives (cerebrocrast, gammapyrone, glutapyrone, and diethone) on mitochondrial bioenergetics and oxidative stress: a comparative study.,” Mitochondrion, vol. 3, no. 1, pp. 47–59, Aug. 2003, doi: 10.1016/S1567-7249(03)00060-6. [55] A. Velena et al., “1,4-Dihydropyridines as Tools for Mitochondrial Medicine Against Oxidative Stress and Associated Metabolic Disorders,” COC, vol. 21, no. 20, Oct. 2017, doi: 10.2174/1385272821666170207104206. [56] H. I. Abdel-Shafy and M. S. M. Mansour, “A review on polycyclic aromatic hydrocarbons: Source, environmental impact, effect on human health and remediation,” Egyptian Journal of Petroleum, vol. 25, no. 1, pp. 107–123, Mar. 2016, doi: 10.1016/j.ejpe.2015.03.011. [57] J. C. Foster, I. Akar, M. C. Grocott, A. K. Pearce, R. T. Mathers, and R. K. O’Reilly, “100th anniversary of macromolecular science viewpoint: the role of hydrophobicity in polymer phenomena.,” ACS Macro Lett., vol. 9, no. 11, pp. 1700–1707, Nov. 2020, doi: 10.1021/acsmacrolett.0c00645. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91202 | - |
| dc.description.abstract | 毒性預測是藥物開發上的一個重要關鍵，有助於科學家更精確的分析化合物潛在毒性，且能省去在體外生物實驗花費過多時間，近期的研究指出粒線體在藥物的毒性上有著至關重要的影響，粒線體是細胞內的能量中心，也是自由基產生的地點，而多種疾病的發生被認為與粒線體受損有相關性，一些藥物也因為其對粒線體的毒性而退出市場，如曲格列酮等。因此在研究哪種藥物對於粒線體可能會在產生毒性方面，藥物開發上已變成一個關鍵的試驗終點，傳統上透過臨床試驗來取得化合物對於粒線體的毒性，但臨床試驗通常需要投入大量時間和龐大的資源方能完成，並且亦有倫理道德等問題。在體外試驗上則可能有細胞異質性的潛在問題，因此我們決定採用定量結構-性質關係（QSAR）在這項研究中，來利用機器學習預測未知化合物的毒性，也避免了以上幾種方式帶來的潛在問題。我們的資料來源於Tox21，PubChem以及許多文獻，使用相同的標準之粒線體膜電位試驗資料，並且利用分別利用了化合物的分子指紋以及描述符來分析毒性對化合物的化學物理性質之關係，也分析毒性和結構的關聯，在結構分析上使用了Tanimoto演算法和PCA分析毒性分佈，在可解釋性分析中，我們對分子指紋的化合物結構警示進行分析，來判斷可能造成毒性的子結構，也利用機器學習對分子描述符進行解釋性分析，由於粒線體膜電位試驗之複雜性導致於資料的不平衡，我們也透過多種方法改變陰性陽性資料比例來增加模型之表現，包含比較了過擬和方法、重組化合物以及產生異構物等方法，我們利用不同的機器和深度學習模型來評估粒線體毒性，包含了SVM，隨機森林模型等等，最後透過模型之特徵重要性找出了影響毒性分析之性質，以及探索了粒線體毒性之結構警示，這都有助於科學家在藥物開發上更好的開發的對於人體更安全的藥物。 | zh_TW |
| dc.description.abstract | Toxicity prediction constitutes a pivotal phase in drug development, enabling scientists to rapidly and accurately assess the potential toxicity of compounds. Recent research highlights the pivotal role of mitochondria in toxicity, as they serve as cellular energy centers and sites of free radical generation. Damaged mitochondria are associated with various diseases, and certain pharmaceuticals have been withdrawn from the market owing to their adverse impact on mitochondrial function, such as rosiglitazone. Consequently, investigating the potential mitochondrial toxicity of drugs has become a crucial endpoint in drug development. Traditionally, assessing compound toxicity toward mitochondria involves time-consuming and resource-intensive clinical trials, fraught with ethical concerns. In vitro tests may present issues related to cellular heterogeneity. Thus, this study employs Quantitative Structure-Activity Relationship (QSAR) methodologies, utilizing statistical methods such as machine learning to forecast the unknown compounds’ toxicity, thereby circumventing potential limitations posed by other approaches. Data for this research originates from Tox21[1], PubChem, and various literature sources, utilizing standardized mitochondrial membrane potential assay data. Molecular fingerprints and descriptors are utilized to analyze the relationship between toxicity and structural or physicochemical properties. The structural analysis incorporates the Tanimoto algorithm and Principal Component Analysis (PCA) to scrutinize toxicity distribution, while interpretability analysis explores compound structure alerts within molecular fingerprints to identify potential substructures causing toxicity. Machine learning is also employed for interpretable analysis of molecular descriptors. Given the complexity of mitochondrial membrane potential assays leading to imbalanced data, various algorithms are employed to adjust the ratio of negative to positive data, encompassing methods like oversampling and compound recombination. This study evaluates mitochondrial toxicity using six distinct machine and deep learning models, including SVM and random forest models. Ultimately, by assessing the feature importance of these models, key properties influencing toxicity analysis are identified, along with the exploration of structural alerts associated with mitochondrial toxicity. These findings collectively aid scientists in developing safer drugs for human use in the realm of drug development. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-12-12T16:11:25Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2023-12-12T16:11:25Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledge i
摘要 ii Abstract iv Table of Contents vi List of Figure viii List of Tables x Chapter I: Introduction 1 Section 1-1: Background and Motivation 1 Section 1-2: Literature Review 6 Section 1-3: Significance 16 Chapter II: Methods and Materials 19 Section 2-1: Data Collection 19 Section 2-2: Data Standardized 26 Section 2-3: PCA Analysis 27 Section 2-4: Tanimoto Analysis 28 Section 2-5: Substructure Analysis 30 Section 2-6: Models 31 Section 2-7: Model Performance 34 Section 2-8: Retrospective Analysis 35 Chapter III: Results 36 Section 3-1: Dataset Analysis 36 Section 3-2: Tanimoto Analysis 38 Section 3-3: Dimension reduction analysis of the chemical spatial distribution of the compounds: PCA and tSNE 40 Section 3-4: Structural Alert 47 Section 3-5: Descriptor Analysis 57 Section 3-6: Machine Learning Performance 64 Section 3-7: Deep Learning Performance 70 Section 3-8: SHAP Analysis 78 Chapter IV: Discussion 84 Section 4-1: Interpretability Analysis 84 Section 4-2: Result Analysis 85 Section 4-3: Limitation 88 Chapter V: Conclusion and Future Work 90 References 92 Appendix 98 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 分子指紋 | zh_TW |
| dc.subject | 描述符 | zh_TW |
| dc.subject | 分子指紋 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 定量結構-性質關係 | zh_TW |
| dc.subject | 粒線體膜電位試驗 | zh_TW |
| dc.subject | 描述符 | zh_TW |
| dc.subject | 粒線體膜電位試驗 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 定量結構-性質關係 | zh_TW |
| dc.subject | descriptor | en |
| dc.subject | machine learning | en |
| dc.subject | mitochondrial membrane potential assay | en |
| dc.subject | quantitative structure-property relationship | en |
| dc.subject | molecular fingerprint | en |
| dc.subject | descriptor | en |
| dc.subject | machine learning | en |
| dc.subject | mitochondrial membrane potential assay | en |
| dc.subject | quantitative structure-property relationship | en |
| dc.subject | molecular fingerprint | en |
| dc.title | 利用機器學習比較分子指紋和描述符對於粒線體毒性之可解釋性預測 | zh_TW |
| dc.title | Using Machine Learning to Comparing Molecular Fingerprints and Descriptors for Interpretable Prediction of Mitochondrial Toxicity | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 何亦平;蔡幸真;曾宇鳳 | zh_TW |
| dc.contributor.oralexamcommittee | Yi-Ping HO;Hsing-Chen Tsai;Yu-feng Tseng | en |
| dc.subject.keyword | 粒線體膜電位試驗,定量結構-性質關係,機器學習,分子指紋,描述符, | zh_TW |
| dc.subject.keyword | machine learning,mitochondrial membrane potential assay,quantitative structure-property relationship,molecular fingerprint,descriptor, | en |
| dc.relation.page | 98 | - |
| dc.identifier.doi | 10.6342/NTU202304465 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2023-12-04 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | - |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-112-1.pdf | 4.21 MB | Adobe PDF | 檢視/開啟 |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。