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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 賴飛羆(Fei-Pei Lai) | |
| dc.contributor.author | Wei-Hsiang Tseng | en |
| dc.contributor.author | 曾煒翔 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:26:38Z | - |
| dc.date.available | 2021-09-11 | |
| dc.date.available | 2022-11-24T03:26:38Z | - |
| dc.date.copyright | 2021-09-11 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-08-31 | |
| dc.identifier.citation | Abhishek Niroula and Mauno Vihinen, “Predicting Severity of Disease-Causing Variants,” Published online 9 January 2017 in Wiley Online Library (www.wiley.com/humanmutation), doi: 10.1002/humu.23173 Sorting Intolerant From Tolerant Help (https://sift.bii.a-star.edu.sg/www/SIFT_help.html#SIFT_PROCEDURE) Ivan Adzhubei, Daniel M. Jordan, and Shamil R. Sunyaev, “Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2,” Current Protocols in Human Genetics 7.20.1-7.20.41, January 2013. Sung Chun and Justin C. Fay, “Identification of deleterious mutations within three human genomes,” Advance Genome Res. 2009. 19: 1553-1561July 14, 2009, doi:10.1101/gr.092619.109 Wiki MutationTaster https://en.wikipedia.org/wiki/MutationTaster Reva and Antipin, and Sander, “Predicting the functional impact of protein mutations: application to cancer genomics,” Nucleic Acids Research. Published in Advance, 2011 doi:10.1093/nar/gkr407. Reva and Antipin, and Sander. “Determinants of protein function revealed by combinatorial entropy optimization,” Genome Biology. doi:10.1186/gb-2007-8-11-r232 Hashem A. Shihab and Julian Gough, “Predicting the Functional, Molecular, and Phenotypic Consequences of Amino Acid Substitutions using Hidden Markov Models,” Published online 3 October 2012 in Wiley Online Library doi: 10.1002/humu.22225 Yongwook Choi, Gregory E. Sims and Sean Murphy, “Predicting the Functional Effect of Amino Acid Substitutions and Indels,” Published online 2012 Oct 8. doi: 10.1371/journal.pone.0046688 Ensembl Variation - Pathogenicity predictions http://m.ensembl.org/info/genome/variation/prediction/protein_function.html Karthik A Jagadeesh, Aaron M Wenger and Mark J Berger, “M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity,” published nature genetics 24 October 2016; doi:10.1038/ng.3703 Philipp Rentzsch, Max Schubach and Jay Shendure, “CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores,” Genome Medicine Published in 2021 https://doi.org/10.1186/s13073-021-00835-9 Hashem A. Shihab, Mark F. Rogers and Julian Gough, “An integrative approach to predicting the functional effects of non-coding and coding sequence variation,” Published in Bioinformatics, 31(10), 2015, 1536–1543, doi: 10.1093/bioinformatics/btv009 The Human Gene Mutation Database (HGMD®): 2003 Update. Hum Mutat (2003) 21:577-581. ANNOVAR. https://annovar.openbioinformatics.org/en/latest/ In-depth-NGS-Data-Analysis-Course.https://hbctraining.github.io/In-depth-NGS-Data-Analysis-Course/sessionVI/lessons/03_annotation-snpeff.html A Guide to KNN imputation. https://medium.com/@kyawsawhtoon/a-guide-to-knn-imputation-95e2dc496e Breiman, L. (2001) Random Forests. Machine Learning, 45, 5-32, http://dx.doi.org/10.1023/A:1010933404324 Yu-Shan Huang “Prioritization of Disease-Causing Variants of Exome Data by Machine Learning” Published in ariti Library 2020, DOI： 10.6342/NTU202002376 Nilah M. Ioannidis and Joseph H. Rothstein REVEL: An Ensemble Method for Predicting the Pathogenicity of Rare Missense Variants. Published: September 22, 2016 DOI: https://doi.org/10.1016/j.ajhg.2016.08.016 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/81023 | - |
| dc.description.abstract | 疾病嚴重程度評分，是判斷變異點位致病性的其中一個重要的依據，大多數的遺傳性疾病會呈現不同的嚴重程度，也就是遺傳疾病症狀其基因變異所表現的輕重層級的分別，若以變異點位的資訊去預測這些由於遺傳導致的疾病嚴重程度給出評分，這個資訊可以增加醫生診斷的正確率，更進一步可以對於不同疾病的嚴重程度選擇合適的治療的方法。儘管目前台大醫院基因學部已經有一套基於規則的計算疾病嚴重程度的評分方法，但在計算非致病性的基因變異的準確度並不足夠，且對於插入、缺失、置換的變異無法進行預測。在此篇研究中，我針對三種不同的變異種類分別蒐集了三組訓練的數據集，包含導致嚴重症狀的致病變異和不嚴重或無症狀的良性變異，使用六種機器學習的演算方法，各別訓練出對應的模型，嘗試找出最合適的演算法。其中以隨機森林的模型與XGB模型的準確率最高，經過驗證資料後的準確度可以穩定達到約87%，整體上比現在使用的方法提高5%，其中以預測不嚴重或無症狀的良性變異進步最多，可以使原本37%被預測錯誤的良性變異，大幅降低致12%。此次研究的預測模型已經寫成工具可以做使用，若有病人的基因資料，可以使用此工具直接進行嚴重程度評分的預測任務，其嚴重程度評分結果可以縮短變異判讀的時間，提高了醫生跟基因研究人員的效率跟準確率。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:26:38Z (GMT). No. of bitstreams: 1 U0001-3008202115063600.pdf: 1977652 bytes, checksum: 7a65b9ce32f7c1f9a209966a9bc91a3b (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "(口試委員審定書) I 致謝 II 中文摘要 III Contents IV Abstract VI List of Figures VII List of Tables VIII Chapter 1 Introduction 1 1.1 Research Background 1 1.2 Thesis Motivation 2 1.3 Aim of the Study 3 Chapter 2 Brief Review of Severity Score 4 2.1 The Severity Score 4 2.2 Previous Severity Score Rule 4 2.3 Variants Predicting Tools 6 2.4 The Challenge of Current Severity Score Method 14 2.5 Summary 15 Chapter 3 Materials and Methods 16 3.1 Data Collection 16 3.1.1 Confidence Database 16 3.1.2 Variants Annotation 17 3.1.3 Clinical Data 19 3.1.4 Summary of the Data collection 19 3.2 Deal with Missing Value 20 3.3 Data Preprocessing 21 3.3.1 In Single Nucleotide Variants 21 3.3.2 In Startloss, Stopgain, and Stoploss 22 3.3.3 In Frameshift Insertion Deletion 23 3.4 Training and Validation Set 23 3.5 Feature Selection 24 3.6 Model Building 25 3.7 Workflow Overview 25 Chapter 4 Results 28 4.1 Different Model Algorithm Performance 28 4.2 Random forest predictor 30 4.3 Compared with previous Severity Score 32 4.4 Performance in the Clinical Data 34 4.5 Use Severity Score to optimize AIVP 35 Chapter 5 Discussion 37 5.1 The New Discovery from Result 37 5.2 The Pros and Cons 38 5.3 Severity Score to Optimize AIVP model 38 5.4 Compare with Related Work 39 5.5 Limitation of Study 39 Chapter 6 Conclusion and Future Work 41 6.1 Conclusion 41 6.2 Future Work 41 Supplement 43 Reference 48 " | |
| dc.language.iso | en | |
| dc.subject | 疾病嚴重程度評分 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | 基因變異 | zh_TW |
| dc.subject | Severity Score | en |
| dc.subject | Machine Learning | en |
| dc.subject | Variant | en |
| dc.title | 利用不同變異基因點位資訊去預測相關遺傳疾病嚴重程度評分 | zh_TW |
| dc.title | Predicting the Severity Score for Genetic Disease Using Variants Information from Multiple Mutation types | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 李妮鍾(Hsin-Tsai Liu),顏廷聿(Chih-Yang Tseng),郭律成,陳啟煌 | |
| dc.subject.keyword | 機器學習,基因變異,疾病嚴重程度評分, | zh_TW |
| dc.subject.keyword | Machine Learning,Variant,Severity Score, | en |
| dc.relation.page | 50 | |
| dc.identifier.doi | 10.6342/NTU202102847 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-09-02 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
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