2篇
您的检索式：作者名="Georgios Tsaousis"
|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Molecular predictive markers in tumors of the gastrointestinal tract显示文摘Gastrointestinal malignancies are among the leading causes of cancer-related deaths worldwide. Like all human malignancies they are characterized by accumulation of mutations which lead to inactivation of tumor suppressor genes or activation of oncogenes. Advances in Molecular Biology techniques have allowed for more accurate analysis of tumors' genetic profiling using new breakthrough technologies such as next generation sequencing(NGS), leading to the development of targeted therapeutical approaches based upon biomarker-selection. During the last 10 years tremendous advances in the development of targeted therapies for patients with advanced cancer have been made, thus various targeted agents, associated with predictive biomarkers, have been developed or are in development for the treatment of patients with gastrointestinal cancer patients. This review summarizes the advances in the field of molecular biomarkers in tumors of the gastrointestinal tract, with focus on the available NGS platforms that enable comprehensive tumor molecular profile analysis. | Eirini Papadopoulou Vasiliki Metaxa-Mariatou Georgios Tsaousis Nikolaos Tsoulos Angeliki Tsirigoti Chrisoula Efstathiadou Angela Apessos Konstantinos Agiannitopoulos Georgia Pepe Eugenia Bourkoula George Nasioulas | 2016 | World Journal of Gastrointestinal Oncology2016，8，11： | 0 |
| 2 | How Many 3D Structures Do We Need to Train a Predictor?显示文摘It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both α-helical and β-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology prediction for the two classes of transmembrane proteins. We show that the existing top-scoring algorithms for predicting the transmembrane segments of α-helical membrane proteins perform slightly better than that of β-barrel outer membrane proteins in all measures of accuracy. With the same rationale, a meta-analysis of the performance of the secondary structure prediction algorithms indicates that existing algorithmic techniques cannot be further improved by just adding more non-homologous sequences to the training sets. The upper limit for secondary structure prediction is estimated to be no more than 70% and 80% of correctly predicted residues for single sequence based methods and multiple sequence based ones, respectively. Therefore, we should concentrate our efforts on utilizing new techniques for the development of even better scoring predictors. | Pantelis G. Bagos Georgios N. Tsaousis Stavros J. Hamodrakas | 2009 | Genomics, Proteomics & Bioinformatics2009，7，3： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员