Bence Bolgár

Bence Bolgár
Budapest University of Technology and Economics · Department of Measurement and Information Systems

MD, PhD

About

29
Publications
7,266
Reads
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162
Citations
Citations since 2016
17 Research Items
142 Citations
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20162017201820192020202120220510152025
Introduction
Skills and Expertise

Publications

Publications (29)
Article
Full-text available
Background Autism spectrum disorder (ASD) is genetically and phenotypically heterogeneous. Former genetic studies suggested that both common and rare genetic variants play a role in the etiology. In this study, we aimed to analyze rare variants detected by next generation sequencing (NGS) in an autism cohort from Hungary.Methods We investigated the...
Data
Number of variants in the investigated genes. Table summarizes the number of different variant types, detected in the investigated genes. Table only includes rare variants, as defined in the Section “Materials and Methods.” n/a, not applicable. Next generation sequencing is not suitable for detection of repeat expansion events. Repeat expansions we...
Article
Full-text available
Background Computational fusion approaches to drug-target interaction (DTI) prediction, capable of utilizing multiple sources of background knowledge, were reported to achieve superior predictive performance in multiple studies. Other studies showed that specificities of the DTI task, such as weighting the observations and focusing the side informa...
Article
Full-text available
Comorbidity patterns have become a major source of information to explore shared mechanisms of pathogenesis between disorders. In hypothesis-free exploration of comorbid conditions, disease-disease networks are usually identified by pairwise methods. However, interpretation of the results is hindered by several confounders. In particular a very lar...
Data
Supplementary material. Detailed description of methods. (PDF)
Data
Comparison of different comorbid network approaches. (TIF)
Data
Bayesian direct multimorbidity map (BDMM) using multivariate depression analysis. (TIF)
Data
Bayesian direct multimorbidity map (BDMM) for depression, irritable bowel syndrome, chronic fatigue, fibromyalgia and migraine. (TIF)
Data
Comparison of different molecular- and epidemilogic level statistics. (TIF)
Data
Bayesian direct multimorbidity map (BDMM) with the alternative single binary depression indicator. (TIF)
Data
The results of text-mining for both corpuses, PMC and PubMed. (XLSX)
Data
The results of the classical statistical measures for all pairs of factors including sex and age. (XLSX)
Data
The Bayesian direct multimorbidity map (BDMM) results. (XLSX)
Data
Results of logistic regression. (XLSX)
Article
Full-text available
The low concordance between different variant calling methods still poses a challenge for the wide-spread application of next-generation sequencing in research and clinical practice. A wide range of variant annotations can be used for filtering call sets in order to improve the precision of the variant calls, but the choice of the appropriate filte...
Conference Paper
Distance Metric Learning (DML) can be a valuable tool in biomedical data analysis as it can incorporate both partial logical equivalences and multiple similarities, solving the multitask fusion problem by producing a unified view of the entities. However, many implementations are too slow to be applied on real-world data and can benefit from GPU ac...
Article
Rich dependency structures are often formed in genetic association studies between the phenotypic, clinical, and environmental descriptors. These descriptors may not be standardized, and may encompass various disease definitions and clinical endpoints which are only weakly influenced by various (e.g., genetic) factors. Such loosely defined complex...
Article
Full-text available
Despite famous serendipitous drug repositioning success stories, systematic projects have not yet delivered the expected results. However, repositioning technologies are gaining ground in different phases of routine drug development, together with new adaptive strategies. We demonstrate the power of the compound information pool, the ever-growing h...
Conference Paper
Full-text available
microRNAs form a complex regulatory network with thousands of target genes. This network is known to suffer specific, but largely elusive, genetic perturbations in various types of disease. Accurate prioritization of microRNAs for each disease type would elucidate those perturbations and so facilitate therapeutic and diagnostic design. The multiple...
Conference Paper
Full-text available
Despite the essential role of microRNAs in post-transcriptional regulation, their genes and related mechanisms are still elusive. There is a wide variety of experimental and in silico methods for target exploration, prediction and validation, but these methods are somewhat complementary and suffer from different biases. Because the profile of micro...
Book
Full-text available
The Bioinformatics book covers new topics in the rapidly expanding field of bioinformatics, from next-generation sequencing to drug discovery and metagenomics. The first two chapters overviews genetic measurement methods. The next four chapters discuss topics related to the effect of genetic variants from protein modeling to gene regulatory network...
Article
Full-text available
Movement disorders are a heterogeneous group of both common and rare neurological conditions characterized by abnormalities of motor functions and movement patterns. This work overviews recent successes and ongoing studies of repositioning relating to this disease group, which underscores the challenge of integrating the voluminous and heterogeneou...
Article
Full-text available
Drug repositioning, an innovative therapeutic application of an old drug, has received much attention as a particularly cost-effective strategy in drug R&D. Recent work has indicated that repositioning can be promoted by utilizing a wide range of information sources, including medicinal chemical, target, mechanism, main and side-effect-related info...

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