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Institution

Adnan Menderes University

EducationAydin, Turkey
About: Adnan Menderes University is a education organization based out in Aydin, Turkey. It is known for research contribution in the topics: Population & Medicine. The organization has 2371 authors who have published 5238 publications receiving 72363 citations.


Papers
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Journal ArticleDOI
TL;DR: Estimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture, and it is believed that Bayes Cπ has merit for routine applications.
Abstract: Two Bayesian methods, BayesCπ and BayesDπ, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability π that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls. Estimates of π from BayesCπ, in contrast to BayesDπ, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesCπ than for BayesDπ, and longest for our implementation of BayesA. Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesCπ has merit for routine applications.

1,006 citations

Journal ArticleDOI
Monika Böhm1, Ben Collen1, Jonathan E. M. Baillie1, Philip Bowles2  +240 moreInstitutions (95)
TL;DR: The results provide the first analysis of the global conservation status and distribution patterns of reptiles and the threats affecting them, highlighting conservation priorities and knowledge gaps which need to be addressed urgently to ensure the continued survival of the world’s reptiles.

720 citations

Journal ArticleDOI
TL;DR: The Common Core State Standards for Mathematics (CCSSM) was published in 2010 and includes a complete collection of standards that are published and reviewed as a ‘common core’ in which math skills have been extensively adopted as discussed by the authors.
Abstract: The Common Core State Standards for Mathematics (CCSSM) was published in 2010 and includes a complete collection of standards that are published and reviewed as a ‘common core’ in which math skills have been extensively adopted. The recommendations provided have been entirely or partially adapted by more than 47 states of the US. Authorities have commited and incredible amount of time, money and resources in creating these new standards and additional effort will be required to implement these standards The new math standards address two established issues in US education, the ordinary quality of mathematics learning and equal opportunity in U.S. schools. It is a fact that deprived students are most likely to have inexperienced or under qualified teachers, and children from impoverished families are much less likely to have the same kind of supports or enrichment opportunities than their more fortunate peers. It is important for the authorities to produce and adapt material for the development of children in such a way that it can clearly address the content and practice of math for the CCSSM and this material should be able to give learning and teaching methods which are in line with CCSSM. It is concluded from this research that there are challenges that have emerged for implementation of CCSSM in which basic challenges include issues of quality, equality, challenges for math teachers, and teaching CCSSM to disabled students.

717 citations

Journal ArticleDOI
TL;DR: Gene signatures have been developed as predictors of response to therapy and protein gene products that have direct roles in driving the biology and clinical behavior of cancer cells are potential targets for the development of novel therapeutics.
Abstract: Breast cancer is a heterogeneous complex of diseases, a spectrum of many subtypes with distinct biological features that lead to differences in response patterns to various treatment modalities and clinical outcomes. Traditional classification systems regarding biological characteristics may have limitations for patient-tailored treatment strategies. Tumors with similar clinical and pathological presentations may have different behaviors. Analyses of breast cancer with new molecular techniques now hold promise for the development of more accurate tests for the prediction of recurrence. Gene signatures have been developed as predictors of response to therapy and protein gene products that have direct roles in driving the biology and clinical behavior of cancer cells are potential targets for the development of novel therapeutics. The present review summarizes current knowledge in breast cancer molecular biology, focusing on novel prognostic and predictive factors.

674 citations

Journal ArticleDOI
TL;DR: A hybrid ARIMA and neural network model is proposed that is capable of exploiting the strengths of traditional time series approaches and artificial neural networks to provide a robust modeling framework capable of capturing the nonlinear nature of the complex time series and thus producing more accurate predictions.

550 citations


Authors

Showing all 2454 results

NameH-indexPapersCitations
Mustafa Sahin6977220729
Dorian J. Garrick5134810244
Ekrem Tatoglu461316659
Kubilay Güçlü40897365
Mehmet Turgut375035842
Mehmet Bilgen341383790
Haluk Ozen332143805
Afig Berdeli312043249
Alparslan Turan301754113
Sinan Akgöl301142412
Khawar Jabran29662625
Ethem Aktürk29947824
Köksal Alptekin281072605
Erdener Karadağ27922251
Yunus A. Cengel276211805
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202321
2022101
2021498
2020426
2019370