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Institution

University of Ioannina

EducationIoannina, Greece
About: University of Ioannina is a education organization based out in Ioannina, Greece. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 7654 authors who have published 20594 publications receiving 671560 citations. The organization is also known as: Panepistimio Ioanninon.


Papers
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Journal ArticleDOI
TL;DR: The existing scientific data on this issue is reported with an effort to highlight the possible future implication of HSPs as tumor biomarkers or drug targets for improving prognosis and treatment of cancer patients around the world.

248 citations

Journal ArticleDOI
TL;DR: In this paper, the authors tried to reveal the way basic motives are linked to wine shopping behavior of consumers and the way wine purchase-relevant knowledge is stored and organized in their memory in relation to their personal values.

248 citations

Journal ArticleDOI
TL;DR: Networks of investigators have begun sharing best practices, tools and methods for analysis of associations between genetic variation and common diseases, and a Network of Investigator Networks has been set up to drive the process.
Abstract: Networks of investigators have begun sharing best practices, tools and methods for analysis of associations between genetic variation and common diseases. A Network of Investigator Networks has been set up to drive the process, sponsored by the Human Genome Epidemiology Network. A workshop is planned to develop consensus guidelines for reporting results of genetic association studies. Published literature databases will be integrated, and unpublished data, including 'negative' studies, will be captured by online journals and through investigator networks. Systematic reviews will be expanded to include more meta-analyses of individual-level data and prospective meta-analyses. Field synopses will offer regularly updated overviews.

247 citations

Journal ArticleDOI
01 Jul 2009
TL;DR: The performance of the SVM with respect to other state-of-the-art classifiers is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation and the classification of signals presenting very low signal-to-noise ratio is confirmed.
Abstract: In this study, heartbeat time series are classified using support vector machines (SVMs). Statistical methods and signal analysis techniques are used to extract features from the signals. The SVM classifier is favorably compared to other neural network-based classification approaches by performing leave-one-out cross validation. The performance of the SVM with respect to other state-of-the-art classifiers is also confirmed by the classification of signals presenting very low signal-to-noise ratio. Finally, the influence of the number of features to the classification rate was also investigated for two real datasets. The first dataset consists of long-term ECG recordings of young and elderly healthy subjects. The second dataset consists of long-term ECG recordings of normal subjects and subjects suffering from coronary artery disease.

247 citations

Journal ArticleDOI
TL;DR: Compared to cytology, the HC2 and PCR are substantially more sensitive for prevalent CIN2 or worse but significantly less specific; however, reduction of the incidence of or mortality from invasive cervical cancer among HPV screened subjects compared to cytologically screened subjects has not yet been demonstrated.

246 citations


Authors

Showing all 7724 results

NameH-indexPapersCitations
John P. A. Ioannidis1851311193612
Kay-Tee Khaw1741389138782
Elio Riboli1581136110499
Mercouri G. Kanatzidis1521854113022
Dimitrios Trichopoulos13581884992
Gyorgy Vesztergombi133144494821
Niki Saoulidou132106581154
Apostolos Panagiotou132137088647
Ioannis Evangelou131122582178
Ioannis Papadopoulos129120185576
Nikolaos Manthos129125681865
Panagiotis Kokkas128123481051
Costas Foudas128111283048
Zoltan Szillasi128121484392
Matthias Schröder126142182990
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202335
2022131
20211,222
20201,203
20191,125
20181,003