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利用 RNA 次代定序資料候選轉錄因子結合位點之基因多型性與退化性關節炎之關聯性研究

為了解決Python exp complex的問題，作者王定廉 這樣論述：

研究背景：GWAS研究已被廣泛用於鑑定遺傳位點與疾病的相關性，但可能因為過於嚴格得統計檢定力（p < 5 × 10−8）導致可能具有功能性的位點被排除。而利用NGS資料庫候選轉錄因子結合位點則可避免具功能性的位點被排除。轉錄因子結合位點的變異會間接影響疾病，因此，找出轉錄因子結合位點將可為遺傳疾病的調控機轉提供新的視野。研究目的：利用TWB NGS資料庫找出台灣人特有NF-κB結合位點的變異與退化性關節炎風險之相關性。研究方法：本研究參考Fisch學者等人之RNA-seq研究，使用其中NF-κB為候選轉錄因子；並利用JASPAR權重矩陣，以A、T=30%，C、G=20%為切點，候選其辨識序列

，並自NCBI網站下載GRCh37-hg19之人體基因序列資料，將辨識序列與其進行比對，找出潛在的結合位點。運用生物資訊學blast mapping方法，將潛在的結合位點與台灣人體生物資料庫次世代定序（Next Generation Sequencing, NGS）資料進行比對，再與GEO資料庫中NF-κB ChIP-seq資料進行比對找出台灣人特有之轉錄因子結合位置的SNPs。先以台灣人體生物資料庫GWAS資料（n = 88,347），排除其他類型關節炎者，保留個案依據自填式問卷資料進行分組，病例組（n = 3,878）及對照組（n = 83,436）進行候選SNPs與OA之相關性研究；再以

105年至110年三軍總醫院健檢中心參與研究之個案（n = 1,167），排除無X光資料者，保留個案依據KL分級進行分組，病例組（n = 533）及對照組（n = 614）進行候選SNPs與OA之相關性研究。將具有顯著差異之SNPs利用GTEx-Portal資料庫查詢其功能表現。研究結果：本研究使用NF-κB為OA相關之候選轉錄因子，候選其辨識序列為5’-KGGRMTTYCCM-3’。經由GRCh37-hg19之人體基因序列資料比對，共有33,731個潛在的結合位點。將潛在的結合位點與台灣人體生物資料庫次世代定序（Next Generation Sequencing, NGS）資料進行比對，再

與GEO資料庫中NF-κB ChIP-seq資料進行比對，找出9個候選SNPs。經由TWB GWAS資料及本研究室病例對照研究發現，位於NF-κB結合位點 rs73164856的T對偶基因，在男性具有保護作用(OR=0.55，95%CI=0.33-0.91)，在嚴重退化性關節炎分組KL=3及KL=4中，其保護作用更為明顯(OR=0.16，95% CI=0.04-0.70)。位於NF-κB結合位點 rs545654在女性嚴重退化性關節炎分組中，T對偶基因罹病風險較高(OR=2.11，95% CI = 1.20 - 3.69 )。在GTEx功能性資料庫中，亦顯示rs73164856 T對偶基因會使

AKR1B15基因表現量降低(p= 0.00019)；rs545654 T對偶基因則將使血液中nNOS基因表現量升高(p=1.2e-17)。結論：透過本研究之新穎轉錄因子結合位點候選基因多型性策略所發現台灣人特有之致病基因位點，將為台灣人致病機轉及治療研究提供新的視野。

A MACHINE LEARNING MODEL FOR DYNAMIC PREDICTION OF CHRONIC KIDNEY DISEASE RISK USING LABORATORY DATA, NON‐LABORATORY DATA, AND NOVEL METABOLIC INDICES

為了解決Python exp complex的問題，作者賈馬瑞 這樣論述：

Chronic kidney disease (CKD) is a major public health challenge with high prevalence, rising incidence, and serious adverse consequences. Developing effective risk prediction models is a cost-effective approach to predict and prevent complications of chronic kidney disease (CKD). This study aimed t

o develop an accurate machine learning model that can dynamically identify individuals at risk of CKD using various kinds of diagnostic data, with or without laboratory data, at different follow-up points. Creatinine is a key component used to predict CKD. These models will enable affordable and eff

ective screening for CKD even with incomplete patient data, such as the absence of creatinine testing. This retrospective cohort study included data on 19,429 adults provided by a private research institute and screening laboratory in Taiwan, gathered between 2001 and 2015. Univariate Cox proportion

al hazard regression analyses were performed to determine the variables with high prognostic value for predicting CKD. We then identified interacting variables and grouped them according to diagnostic data categories. Our models used three types of data gathered at three points in time: non-laborato

ry, laboratory, and novel metabolic indices data. Next, we used subgroups of variables within each category to train two machine learning models (Random Forest and XGBoost). Our machine learning models can dynamically discriminate individuals at risk for developing CKD. All the models performed well

using all three kinds of data, with or without laboratory data. Using only non-laboratory-based data (such as age, sex, BMI, and waist circumference), both models predict chronic kidney disease as accurately as models using laboratory and metabolic indices data. Our machine learning models have dem

onstrated the use of different categories of diagnostic data for CKD prediction, with or without laboratory data. The ML models are simple to use and flexible, because they work even with incomplete data, and can be applied in any clinical setting, including settings where laboratory data is difficu

lt to obtain.