Bogojeska, Aleksandra
Preferred name
Bogojeska, Aleksandra
Official Name
Bogojeska, Aleksandra
Main Affiliation
Email
aleksandra.bogojeska@finki.ukim.mk
4 results
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Item type:Publication, Inferring Cuisine - Drug Interactions Using the Linked Data Approach(Springer Nature, 2015-03-20); ; ; Food - drug interactions are well studied, however much less is known about cuisine - drug interactions. Non-native cuisines are becoming increasingly more popular as they are available in (almost) all regions in the world. Here we address the problem of how known negative food - drug interactions are spread in different cuisines. We show that different drug categories have different distribution of the negative effects in different parts of the world. The effects certain ingredients have on different drug categories and in different cuisines are also analyzed. This analysis is aimed towards stressing out the importance of cuisine - drug interactions for patients which are being administered drugs with known negative food interactions. A patient being under a treatment with one such drug should be advised not only about the possible negative food - drug interactions, but also about the cuisines that could be avoided from the patient's diet. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Protein Function Prediction by Clustering of Protein-Protein Interaction Network(Springer, Berlin, Heidelberg, 2011-09-14); ; ; The recent advent of high throughput methods has generated large amounts of protein-protein interaction network (PPIN) data. When studying the workings of a biological cell, it is useful to be able to detect known and predict still undiscovered protein complexes within the cell's PPINs. Such predictions may be used as an inexpensive tool to direct biological experiments. Because of its importance in the studies of protein interaction network, there are different models and algorithms in identifying functional modules in PPINs. In this paper, we present two representative methods, focusing on the comparison of their clustering properties in PPIN and their contribution towards function prediction. The work is done with PPIN data from the bakers’ yeast (Saccaromyces cerevisiae) and since the network is noisy and still incomplete, we use pre-processing and purifying. As a conclusion new progress and future research directions are discussed. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Next-generation DNA sequencing technology, challenges and bioinformatics approaches for sequence alignment(2010-09); ; Kocarev, LjupchoThe advent of high-throughput sequencing platforms brought bioinformatics to a new level. This, so called ‘next-generation’ sequencing technology opened the researching doors of every laboratory allowing accomplishment of previously unimaginable scale and expensive experiments. As a result, novel research areas have emerged providing huge amounts of new data ready to be analyzed. Parallel to this progress, a variety of sequencing tools designed for data analysis has been published. Sequence alignment takes the central challenge in data analysis, providing primary representative results for the experiments. Few alignment methods and diversity of tools have been published and developed in the last years. The main goal of all these alignment tools is to fit between performance and accuracy. In this review will be presented the new NGS technologies and platforms, the current alignment approaches applied in data analysis and described some commonly used implementations of the methods. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Observing Dynamical Processes in Multiplex Networks by Using Edge Correlation(2013); ; ; Kocarev, LjupchoThe recent emergence of the new representation of interconnected layers of networks, called multiplex networks, refocused the research in complex networks. The analysis of these networks requires redefinition of the dynamical processes and topological properties present in one-layered graphs. The main focus in this work is the relation between consensus and synchronization defined on multiplex graphs and edge correlation of the graph layers. We show numerically that the information of edge correlation between layers offers substantial insights in the complex multiplex structure, thus, contributing to simplified and faster analysis and design of multiplex graphs with desired consensus or synchronization properties.