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This paper aims at the comparison of the network power for Pearson-Hamming networks built using the chi-squared functional set, and Bayes-Hamming networks built using the hyperbolic functional set. To configure these networks a correlation matrix of biometric data is calculated. At the nest step the data are sorted. Low-correlated data are converted with Pearson-Hamming networks, high-correlated data...
In series of papers, the authors introduced the concepts of multidimensional intuitionistic fuzzy sets and logic. Here, the concept of a multidimensional intuitionistic fuzzy quantifier is introduced. Three groups of these quantifiers are described and some of their basic properties are studied.
Bagging ensemble techniques have been utilized effectively by practitioners in the field of bioinformatics to alleviate the problem of class imbalance and to improve the performance of classification models. However, many previous works have used bagging only with a single arbitrary number of iterations. In this study, we raise the question of what is the impact of altering the number of iterations/ensembles...
The key challenges in integration of biomedical data is to enable semantic interoperability between different types of medical records, healthcare information, sensing data and lifelog data. For the interoperability between data, it is common way to follow worldwide information standards and standards terminologies such as HL7/SNOMED CT. For the collection of data from different data sources we suggest...
Fuzzy extractors and neural networks used for identification by handwriting dynamics should be used in tandem with another methods. Bayesian statistical inference networks or other networks able to consider a complete matrix of correlation coefficients may become a method that complete them. The paper proposes a simple correlation metric that takes into account correlation coefficients of biometric...
Multi-instance learning studies problems in which labels areassigned to bags that contain multiple instances. In these settings, the relations between instances and labels are usually ambiguous. Incontrast, multi-task learning focuses on the output space in whichan input sample is associated with multiple labels. In real world, asample may be associated with multiple labels that are derived fromobserving...
Class imbalance is a significant challenge that practitioners in the field of bioinformatics are faced with on a daily basis. It is a phenomenon that occurs when number of instances of one class is much greater than number of instances of the other class(es) and it has adverse effects on the performance of classification models built on this skewed data. Random Forest as a robust classifier has been...
Gene sets have been widely used on genome-scale data for various purposes. Ideally, gene sets should have multiple scales that can explain biological processes in different scales and depth, which is often missing from most popular algorithmically defined gene sets. We propose a principled way to generate multiscale gene sets based on protein-protein interaction (PPI) networks and techniques from...
The cancer Biomedical Informatics Grid (caBIGtrade) was initiated by the US National Cancer Institute in 2004 to address the need for interoperable information systems to enable molecular medicine for oncology. With the successful completion of the pilot phase of caBIGtrade in 2007, the NCI is in the process of expanding the program into the broader biomedical research and care delivery arena. To...
The Mouse Resource Browser MRB (http://bioit.fleming.gr/mrb) is an easy-to-use database for searching and retrieving mouse resource information. Currently, MRB hosts a list of 200 mouse resources which are divided in 33 different categories. Apart from core information such as URL(s), contact information and free text descriptions of domain content, MRB holds valuable technical information for each...
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