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on Neuroeconomics |
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Issue of 2017‒06‒25
two papers chosen by |
| By: | Maghsoodi, Masoume |
| Abstract: | The construction of genetic regulatory networks is understanding the relationship among genes or circuits which regulate the conditions of cells in response to internal or external stimuli. In fact, the objective is to understand the network of relationship among genes which determine which genes are responsible for activating other genes. The understanding of relationships may help to identify the genes which are involved in a disease and design the drugs. The most important limitations in gene regulatory network inference are low number of samples, noise penetration possibility, and large number of genes. There are different models to build gene regulatory network. This study used fuzzy hierarchical clustering method to infer gene regulatory network. Using clustering, the similar genes will be in a cluster. Many edges therefore will be removed. The final assessments showed that the genes clustering increased the efficiency of gene regulation network inference methods. |
| Keywords: | Principal Component Analysis, Head Cluster, Clustering, Gene Regulation Network. |
| JEL: | C0 C6 |
| Date: | 2016–06–01 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:79743&r=neu |
| By: | Andrea Civelli (University of Arkansas); Cary Deck (University of Arkansas and Economic Science Institute, Chapman University) |
| Abstract: | Raven’s Progressive Matrices are a broadly used tool for measuring cognitive ability. This paper develops and validates a set of nonverbal puzzles that can be viewed as an extension of or substitute for the well-known Ravens tasks. Specifically, we describe the characteristics of our puzzles and provide a calibration of the matrices in terms of response accuracy and response time as a function of these characteristics. Then we directly compare within-subject performance on our puzzles and Ravens tasks. Finally, we replicate a previous experimental paper, substituting our puzzles for the Ravens matrices, and show the two tools have similar predictive success. Our approach offers several benefits due to the relatively large number of novel puzzles of a given difficulty level that can be generated. |
| Keywords: | Cognitive Abilities Tests, Raven’s Matrices, Experimental Economics Tools |
| JEL: | C9 C90 C91 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:chu:wpaper:17-09&r=neu |