WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394
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Article Real-Effort Tasks in Laboratory Experiments(Economic and Financial Research Assoc - Efad, 2023-09-30) Demirtas, Burak KaganLaboratory experiments used in economics are differentiated in terms of many technical features. One of these technical features is whether the experiment involves a real-effort task. A real-effort task can be defined as a task in which the experiment participants work on aAreal job during the experiment, spend time and effort, determine their performance level and as a result earn a certain amount of money. This study aims to examine real-effort tasks that are frequently used in experimental economics studies, and to discuss potential problems that researchers may face when conducting experiments with real- effort tasks. Within the scope of this review, real-effort tasks commonly used in the literature are categorized under four groups: real-effort tasks based on mathematical operations, puzzles, slider task, and word encryption tasks. Choice of the real-effort task is important for an experimental study because it may lead to misinterpretation of the findings. AAsAa result of the study, the learning effect, the boredom of the task and the abilities required by the task are seen as possible sources of measurement error. While the learning effect and boredom may cause problems especially in within-subject designs, it was found that differences in the abilities of participants may cause measurement errors especially in between-subject designs.Article Evaluation of Sub-Network Search Programs in Epilepsy-Related GWAS Dataset(Pamukkale Univ, 2022) Adanur Dedeturk, Beyhan; Bakir Gungor, Burcu; Dedeturk, Beyhan Adanur; Gungor, Burcu BakirThe active sub-network detection aims to find a group of interconnected genes of disease-related genes in a protein-protein interaction network. In recent years, several algorithms have been developed for this problem. In this study, the analysis of disease-specific sub-network identification programs is evaluated using epilepsy data set. Under the same conditions and with the same data set, 9 different programs are run and results of their Greedy algorithm, Genetic algorithm, Simulated Annealing Algorithm, MCC (Maximal Clique Centrality) algorithm, MCODE (Molecular Complex Detection) algorithm, and PEWCC (Protein Complex Detection using Weighted Clustering Coefficient) algorithm are shown. The top-scoring 5 modules of each program, are compared using fold enrichment analysis and normalized mutual information. Also, the identified subnetworks are functionally enriched using a hypergeometric test, and hence, disease-associated biological pathways are identified. In addition, running times and features of the programs are comparatively evaluated.