A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma.
Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO methods coequally treat all candidates, ignoring individual characteristics, thus frequently deteriorating performance with comparatively more predictors. H...
| Publicado en: | BioMed Research International pp. 1 - 18 |
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| Autores principales: | , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
10/16/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153029387&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153029387 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 10/16/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 153029387 153029387 153029387 10.1155/2021/7865856 153029387 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Wavelet-Based Learning Model Enhances Molecular Prognosis in Pancreatic Adenocarcinoma. aug: au: Tang, Binhua Chen, Yu Wang, Yuqi Nie, Jiafei affil: Epigenetics & Function Group, Hohai University, Jiangsu 213022, China sug: subj: Pancreatic Neoplasms Prognosis Adenocarcinoma Prognosis Deep Learning Genomics Epigenomics Kaplan-Meier Estimator ab: Genome-wide omics technology boosts deep interrogation into the clinical prognosis and inherent mechanism of pancreatic oncology. Classic LASSO methods coequally treat all candidates, ignoring individual characteristics, thus frequently deteriorating performance with comparatively more predictors. Here, we propose a wavelet-based deep learning method in variable selection and prognosis formulation for PAAD with small samples and multisource information. With the genomic, epigenomic, and clinical cohort information from The Cancer Genome Atlas, the constructed five-molecule model is validated via Kaplan-Meier survival estimate, rendering significant prognosis capability on high- and low-risk subcohorts (p value < 0.0001), together with three predictors manifesting the individual prognosis significance (p value: 0.0012~0.024). Moreover, the performance of the prognosis model has been benchmarked against the traditional LASSO and wavelet-based methods in the 3- and 5-year prediction AUC items, respectively. Specifically, the proposed model with discrete stationary wavelet base (bior1.5) overwhelmingly outperformed traditional LASSO and wavelet-based methods (AUC: 0.787 vs. 0.782 and 0.721 for the 3-year case; AUC: 0.937 vs. 0.802 and 0.859 for the 5-year case). Thus, the proposed model provides a more accurate perspective, but with less predictor burden for clinical prognosis in the pancreatic carcinoma study. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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