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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kwon, Min-Seok Kim, Yongkang Lee, Seungyeoun Namkung, Junghyun Yun, Taegyun Yi, Sung Gon Han, Sangjo Kang, Meejoo Kim, Sun Whe Jang, Jin-Young Park, Taesung |
| Copyright Year | 2014 |
| Description | Author affiliation: Interdisciplinary program in Bioinformatics, Seoul National University, Seoul, Korea (Kwon, Min-Seok; Park, Taesung) || Department of Mathematics and Statistics, Sejong University, Seoul, Korea (Lee, Seungyeoun) || Department of Statistics, Seoul National University, Seoul, Korea (Kim, Yongkang) || Bioinformatics Tech. Lab., Healthcare group, SK telecom, Seoul, Korea (Namkung, Junghyun; Yun, Taegyun; Yi, Sung Gon; Han, Sangjo) || Department of Surgery, Seoul National University Hospital, Seoul, Korea (Kang, Meejoo; Kim, Sun Whe; Jang, Jin-Young) |
| Abstract | Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, which has dismal prognosis because of its silent early symptoms, high metastatic potential, and resistance to conventional therapies. Although a PDAC patient who is diagnosed at an early stage would have a substantial increase in chance of survival, the survival rate is poor because there is no efficient non-invasive diagnostic test in the early stage. In this study, we developed an efficient prediction models to detect PDAC in its early stages. Our prediction models use both mRNA and miRNA expression data from 104 PDAC tissues and 17 normal pancreatic tissues using microarray technology. After quality control, we built prediction models based on support vector machine (SVM) from mRNA and miRNA expressions for detecting early PDAC. To prevent over-fitting effect, we conducted leave-one-out cross validation (LOOCV) and 5-fold cross validation (CV). For independent validation of prediction models, we performed evaluation on independent datasets from Gene Expression Omnibus (GEO). After the validation, we identified 28 single markers and 231 combinations of markers with powerful prediction performance. In addition, the marker candidates are annotated with cancer pathways using gene ontology analysis. Our prediction models for PDAC may have potential for early diagnosis of PDAC. |
| Starting Page | 273 |
| Ending Page | 278 |
| File Size | 377132 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479956692 |
| DOI | 10.1109/BIBM.2014.6999167 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-11-02 |
| Publisher Place | United Kingdom |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Biomarker development Barium miRNA expression prediction model Lungs Pancreatic ductal adenocarcinoma (PDAC) Predictive models Ontologies Reliability Blood Cancer mRNA expression |
| Content Type | Text |
| Resource Type | Article |
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