Barrett's esophagus is a condition resulting from chronic gastro-esophageal reflux disease with a documented risk of esophageal adenocarcinoma. Current strategies for improved survival in patients with Barrett'...Barrett's esophagus is a condition resulting from chronic gastro-esophageal reflux disease with a documented risk of esophageal adenocarcinoma. Current strategies for improved survival in patients with Barrett's adenocarcinoma focus on detection of dysplasia. This can be obtained by screening programs in high-risk cohorts of patients and/or endoscopic biopsy surveillance of patients with known Barrett's esophagus (BE). Several therapies have been developed in attempts to reverse BE and reduce cancer risk. Aggressive medical management of acid reflux, lifestyle modifications, antireflux surgery, and endoscopic treatments have been recommended for many patients with BE. Whether these interventions are cost-effective or reduce mortality from esophageal cancer remains controversial. Current treatment requires combinations of endoscopic mucosal resection techniques to eliminate visible lesions followed by ablation of residual metaplastic tissue. Esophagectomy is currently indicated in multifocal high-grade neoplasia or mucosal Barrett's carcinoma which cannot be managed by endoscopic approach.展开更多
Chloroplasts are organelles found in plant cells that conduct photosynthesis. The subchloroplast locations of proteins are correlated with their functions. With the availability of a great number of protein data, it i...Chloroplasts are organelles found in plant cells that conduct photosynthesis. The subchloroplast locations of proteins are correlated with their functions. With the availability of a great number of protein data, it is highly desired to develop a com- putational method to predict the subchloroplast locations of chloroplast proteins. In this study, we proposed a novel method to predict subchloroplast locations of proteins using tripeptide compositions. It first used the binomial distribution to optimize the feature sets. Then the support vector machine was selected to perform the prediction of subchloroplast locations of proteins. The proposed method was tested on a reliable and rigorous dataset including 259 chloroplast proteins with sequence identity ≤ 25%. In the jack-knife cross-validation, 92.21% envelope proteins, 93.20% thylakoid mem- brane, 52.63% thylakoid lumen and 85.00% stroma can be correctly identified. The overall accuracy achieves 88.03% which is higher than that of other models. Based on this method, a predictor called ChloPred has been built and can be freely available from http：//cobi.uestc.edu.cn/people/hlin/tools/ChloPred/. The predictor will provide important information for theoretical and experimental research of chloroplast proteins.展开更多
文摘Barrett's esophagus is a condition resulting from chronic gastro-esophageal reflux disease with a documented risk of esophageal adenocarcinoma. Current strategies for improved survival in patients with Barrett's adenocarcinoma focus on detection of dysplasia. This can be obtained by screening programs in high-risk cohorts of patients and/or endoscopic biopsy surveillance of patients with known Barrett's esophagus (BE). Several therapies have been developed in attempts to reverse BE and reduce cancer risk. Aggressive medical management of acid reflux, lifestyle modifications, antireflux surgery, and endoscopic treatments have been recommended for many patients with BE. Whether these interventions are cost-effective or reduce mortality from esophageal cancer remains controversial. Current treatment requires combinations of endoscopic mucosal resection techniques to eliminate visible lesions followed by ablation of residual metaplastic tissue. Esophagectomy is currently indicated in multifocal high-grade neoplasia or mucosal Barrett's carcinoma which cannot be managed by endoscopic approach.
文摘Chloroplasts are organelles found in plant cells that conduct photosynthesis. The subchloroplast locations of proteins are correlated with their functions. With the availability of a great number of protein data, it is highly desired to develop a com- putational method to predict the subchloroplast locations of chloroplast proteins. In this study, we proposed a novel method to predict subchloroplast locations of proteins using tripeptide compositions. It first used the binomial distribution to optimize the feature sets. Then the support vector machine was selected to perform the prediction of subchloroplast locations of proteins. The proposed method was tested on a reliable and rigorous dataset including 259 chloroplast proteins with sequence identity ≤ 25%. In the jack-knife cross-validation, 92.21% envelope proteins, 93.20% thylakoid mem- brane, 52.63% thylakoid lumen and 85.00% stroma can be correctly identified. The overall accuracy achieves 88.03% which is higher than that of other models. Based on this method, a predictor called ChloPred has been built and can be freely available from http：//cobi.uestc.edu.cn/people/hlin/tools/ChloPred/. The predictor will provide important information for theoretical and experimental research of chloroplast proteins.
