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Discovery of multi-omics biomarkers for toxicity using meta-analysis
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 이, 기영 | - |
| dc.contributor.author | 김, 효실 | - |
| dc.date.accessioned | 2015-10-28T02:40:29Z | - |
| dc.date.available | 2015-10-28T02:40:29Z | - |
| dc.date.issued | 2015 | - |
| dc.identifier.uri | http://repository.ajou.ac.kr/handle/201003/11834 | - |
| dc.description.tableofcontents | Ⅰ. INTRODUCTION 3
Ⅱ. MATERIAL S AND METHODS 10 A. MATERIALS 11 1. Cell lines 11 2. Reagents 11 B. METHODS 11 1. Data preparation & class assignment 11 1-1. Data collection and in-depth curation 11 1-2. Preprocessing 14 1-3. Database construction 14 1-4. Determinant of three toxicity levels for each organ using a threshold 16 1-5. Investment of the relationship between experimental factors and defined toxicity level 17 2. Biomarker selection and predictive model generation 18 2-1. Gene selection using meta-analysis 18 2-2. Functional analysis of filtered genes by meta-analysis 19 2-3. Gene selection using sPLS-DA approach 19 2-4. Gene selection using wrappers 19 2-5. Generation of a predictive model 20 3. Model and biomarker verification 20 3-1. Performance assessment of prediction model 20 3-2. Cell culture 20 3-3. Cell proliferation assay 20 3-4. mRNA isolation, semi-quantitative RT-PCR, and qRT-PCR 21 3-5. In vitro validation of the SMOT genes 21 3-6. GO and protein network analysis of the SMOT genes 22 Ⅲ. RESULTS 23 A. Data preparation & class assignment 23 1. Building of toxicogenomics database using gene expression from integrated studies 23 2. Toxicity class assignments 24 3. Correlations and levels of compound toxicity in multiple organs 26 4. Distribution of sample toxicity levels across compounds 28 B. Biomarker selection and predictive model generation 40 1. Gene filter out using meta-analysis 40 2. Functional analysis of selected genes from multiple meta-analyses 45 3. Gene selection using (s)PLS-DA method 51 4. Selection of a multi-organ toxicity signature using wrappers 51 5. Expression pattern of SMOT genes across studies and samples 55 6. Separation of samples with SMOT genes using PLS-DA analysis 60 7. Generation of a multi-organ toxicity classifier 62 C. Model and biomarker verification 63 1. Performance assessment of the predictive models using the SMOT genes for multi-organ toxicity 63 2. Verification of a multi-organ toxicity classifier 64 3. Experimental validation of the SMOT genes 68 4. Protein network and GO analyses of the SMOT genes 70 Ⅳ. DISCUSSION 72 Ⅴ. CONCLUSION 76 | - |
| dc.language.iso | en | - |
| dc.title | Discovery of multi-omics biomarkers for toxicity using meta-analysis | - |
| dc.title.alternative | 메타 분석을 통한 독성 예측 다중 오믹스 바이오 마커 발굴 | - |
| dc.type | Thesis | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000018928 | - |
| dc.subject.keyword | Toxicogenomics | - |
| dc.subject.keyword | Toxicometabolomics | - |
| dc.subject.keyword | Biomarker | - |
| dc.subject.keyword | Meta-analysis | - |
| dc.subject.keyword | Classification | - |
| dc.subject.keyword | Microarray Analysis/methods | - |
| dc.subject.keyword | Algorithms | - |
| dc.subject.keyword | Gene Expression Profiling/methods | - |
| dc.subject.keyword | Organ Toxicity Prediction | - |
| dc.subject.keyword | Integrated Multi-Omics Analysis | - |
| dc.description.degree | Doctor | - |
| dc.contributor.department | 대학원 의생명과학과 | - |
| dc.contributor.affiliatedAuthor | 김, 효실 | - |
| dc.date.awarded | 2015 | - |
| dc.type.local | Theses | - |
| dc.citation.date | 2015 | - |
| dc.embargo.liftdate | 9999-12-31 | - |
| dc.embargo.terms | 9999-12-31 | - |
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