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The Uratim Manufacturing Ltd. was founded in 2005 by academic researchers for exploiting  new database search algorithms and newly identified biocatalysts. The spin-off develops protein-, genomic- and metagenomic databases and mathematical modelling tools, such as protein-ligand docking programs, metagenomic analysis tools, and brain MRI evaluation programs.  Uratim is located in Budapest, Hungary.

Our scientific breakthroughs:

 

 

  • Using our Budapest Amyloid Predictor, we have found numerous amyloid- and non-amyloid patterns among hexapeptides in: László Keresztes, Evelin Szögi, Bálint Varga, Viktor Farkas, András Perczel, Vince Grolmusz: Succinct Amyloid and Non-Amyloid Patterns in Hexapeptides,  ACS Omega Vol. 7, No. 40, 35532-35537 (2022), https://doi.org/10.1021/acsomega.2c02513
  • A highly generalized association rule miner tools is described in: Balázs Szalkai, Vince Grolmusz: SCARF: A Biomedical Association Rule Finding Webserver, Journal of Integrative Bioinformatics, Vol. 19, No. 1. pp. 20210035, (2022) (an invited paper), https://doi.org/10.1515/jib-2021-0035
  • Gaussian blurring is a well-established method for image data augmentation: it may generate a large set of images from a small set of pictures for training and testing purposes for Artificial Intelligence applications. When we apply AI for non-imagelike biological data, hardly any related method exists. We have introduced the “Newtonian blurring” in human braingraph (or connectome) augmentation: Started from a dataset of 1053 subjects from the public release of the Human Connectome Project, we first repeated a probabilistic weighted braingraph construction algorithm 10 times for describing the connections of distinct cerebral areas, then for every possible set of 7 of these graphs, deleted the lower and upper extremes, and averaged the remaining 7 − 2 = 5 edge-weights for the data of each subject. This way we augment the 1053 graph-set to 126,360 graphs: László Keresztes, Evelin Szögi, Bálint Varga, Vince Grolmusz: Introducing and Applying Newtonian Blurring: An Augmented Dataset of 126,000 Human Connectomes at braingraph.org,  Scientific Reports, 12:3102 (2022), https://doi.org/10.1038/s41598-022-06697-4
  • We have created one of the bests amyloid predictor in: Lászlo Keresztes, Evelin Szögi, Bálint Varga, Viktor Farkas, András Perczel and Vince Grolmusz: The Budapest Amyloid Predictor and its Applications, Biomolecules, 11(4) 500,  (2021) https://doi.org/10.3390/biom11040500
  • By applying the 1200 Subjects Release of the Human Connectome Project (HCP) and Support Vector Machines, we identify just 102 connections out of the total number of 1950 connections in the 83-vertex graphs of 1064 subjects, which—by a simple linear test—precisely, without any error determine the sex of the subject. Next, we re-scaled the weights of the edges—corresponding to the discovered fibers—to be between 0 and 1, and, very surprisingly, we were able to identify two graph edges out of these 102, such that, if their weights are both 1, then the connectome always belongs to a female subject, independently of the other edges. Similarly, we have identified 3 edges from these 102, whose weights, if two of them are 1 and one is 0, imply that the graph belongs to a male subject—again, independently of the other edges. We call the former 2 edges superfeminine and the first two of the 3 edges supermasculine edges of the human connectome. Even more interestingly, the edge, connecting the right Pars Triangularis and the right Superior Parietal areas, is one of the 2 superfeminine edges, and it is also the third edge, accompanying the two supermasculine connections if its weight is 0; therefore, it is also a “switching” edge.László Keresztes, Evelin Szögi, Bálint Varga, Vince Grolmusz: Identifying Super-Feminine, Super-Masculine and Sex-Defining Connections in the Human Braingraph, Cognitive Neurodynamics, Vol. 15. No. 6. pp. 949-959 (2021) https://doi.org/10.1007/s11571-021-09687-w
  • Sex differences were discovered in the human brain in: Balázs Szalkai, Bálint Varga, Vince Grolmusz: The Graph of our Mind; Brain Sciences, Vol. 11, No. 3. 342 (2021) https://doi.org/10.3390/brainsci11030342
  • The frequently appearing complete subgraphs were diiscovered and analyzed in the human connectome in: The Frequent Complete Subgraphs in the Human Connectome; Máté Fellner, Bálint Varga, Vince Grolmusz;   PLOS ONE  15(8): e0236883 (2020) https://doi.org/10.1371/journal.pone.0236883
  • We identified and analyzed the frequent neighborhoods of the human hippocampus, and found areas which are more and less likely to be connected in highly intelligent individuals: Good Neighbors, Bad Neighbors: The Frequent Network Neighborhood Mapping of the Hippocampus Enlightens Structural Factors of the Human Intelligence; Máté Fellner, Bálint Varga, Vince Grolmusz;  Scientific Reports  Vol. 10. 11967 (2020) https://doi.org/10.1038/s41598-020-68914-2
  • We have mapped the frequent neighbor sets of the human hippocampus , and found strong sex differences: The Frequent Network Neighborhood Mapping of the Human Hippocampus Shows Much More Frequent Neighbor Sets in Males Than in Females; Máté Fellner, Bálint Varga, Vince Grolmusz; PLOS ONE 15(1): e0227910 (2020). https://doi.org/10.1371/journal.pone.0227910
  • Studying more than 400 subjects, we have identified the frequently appearing small subgraphs, and analyzed their frequencies in male and female braingraphs — first in the literature — in: Máté Fellner, Bálint Varga, Vince Grolmusz: The Frequent Subgraphs of the Connectome of the Human Brain, Cognitive Neurodynamics (2019) https://doi.org/10.1007 /s11571-019-09535-y     https://rdcu.be/bAHoe
  • Based on the Consensus Connectome Dynamics, we have generated and made public – first in the literature –  hundreds of directed human connectomes: Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince Grolmusz: High-Resolution Directed Human Connectomes and the Consensus Connectome Dynamics, PLOS ONE, Vol. 14 No. 4,: e0215473 (2019) https://doi.org/10.1371/journal.pone.0215473
  • We have considered the subgraphs, spanned by the lobes of the human brain. Next, we compared the deep graph-theoretical properties of these subgraphs in:  Balázs Szalkai, Bálint Varga, Vince Grolmusz: Comparing Advanced Graph-Theoretical Parameters of the Connectomes of the Lobes of the Human Brain, Cognitive Neurodynamics, Vol. 12, No. 6, pages 549-559 (2018), https://doi.org/10.1007/s11571-018-9508-y https://rdcu.be/8Gwh
  • We have developed and published the first ever automatically refreshed amyloid list from the PDB in: Kristóf Takács, Bálint Varga, Vince Grolmusz: PDB_Amyloid: An Extended Live Amyloid Structure List from the PDB, FEBS Open Bio, Vol. 9, No. 1. pp. 185-190,  2019. https://doi.org/10.1002/2211-5463.12524
  • The correlation between graph theoretical properties of the human connectome and psychological tests were examined in: Balázs Szalkai, Bálint Varga, Vince Grolmusz: Mapping Correlations of Psychological and Connectomical Properties of the Dataset of the Human Connectome Project with the Maximum Spanning Tree Method,  Brain Imaging and Behavior, available online August 7, 2018, https://doi.org/10.1007/s11682-018-9937-6 ,
  • We have developed a fast, comprehensive and functional gene finding tool in unknown species from metagenomic samples in: Balázs Szalkai, Vince Grolmusz: MetaHMM: A Webserver for Identifying Novel Genes with Specified Functions in Metagenomic Samples;  Genomics, available online May 23, 2018, https://doi.org/10.1016/j.ygeno.2018.05.016
  • We have examined the Consensus Connectome Dynamics phenomenon — which, by our hypothesis, describes the individual development of the axons of the human brain — restricted to the largest lobe, the frontal lobe of the brain. We have discovered the role of the dorsal striatum in this development, first in the literature, in: Csaba Kerepesi, Bálint Varga, Balázs Szalkai,  Vince Grolmusz:The Dorsal Striatum and the Dynamics of the Consensus Connectomes in the Frontal Lobe of the Human Brain,  Neuroscience Letters, Vol. 673, (2018), pp. 51-55.  https://doi.org/10.1016/j.neulet.2018.02.052
  • We have proved that the phenomenon of the Consensus Connectome Dynamics is independent from  the chosen set of subjects, therefore, it describes a biological property of the human brain. Additionally, we have described a random graph model, which reproduces the CCD phenomenon: Balázs Szalkai, Bálint Varga, Vince Grolmusz: The Robustness and the Doubly-Preferential Attachment Simulation of the Consensus Connectome Dynamics of the Human Brain,  Scientific Reports, Vol. 7, 16118, https://doi.org/10.1038/s41598-017-16326-0 (2017)
  • We have mapped the individual variability of the connections in the lobes of the human brain, first in the literature in: Csaba Kerepesi, Balázs Szalkai, Bálint Varga, Vince Grolmusz: Comparative Connectomics: Mapping the Inter-Individual Variability of Connections within the Regions of the Human Brain,  Neuroscience Letters Vol. 662, pp. 17-21, (2018) https://doi.org/10.1016/j.neulet.2017.10.003
  • We have developed a new, innovative data mining approach for finding combinatorial biomarkers in enormous datasets: Balázs Szalkai, Vince K. Grolmusz, Vince I. Grolmusz: Identifying Combinatorial Biomarkers by Association Rule Mining in the CAMD Alzheimer’s Database, Archives of Gerontology and Geriatrics (2017),  https://doi.org/10.1016/j.archger.2017.08.006
  • We have presented – first in the literature – a nearly perfect protein classification method by artificial neural networks in: Balázs Szalkai, Vince Grolmusz: Near Perfect Protein Multi-Label Classification with Deep Neural Networks, Methods (2017), https://doi.org/10.1016/j.ymeth.2017.06.034, and the related webserver: Balázs Szalkai, Vince Grolmusz: SECLAF: A Webserver and Deep Neural Network Design Tool for Biological Sequence Classification, Bioinformatics, Vol 34, No. 14, pp. 2487-2489 2018
  • We have shown – first in the literature – that in the consensus braingraph, if we consider the edges that are present in all graphs, then the edges that appear in less and less graphs, then, gradually, more and more edges will be seen. The surprising observation – called The Consensus Connectome Dynamics – is that the new edges appear as the branches of the growing  tree. We hypothesize that this extraordinary phenomenon is related to the individual development of the connections in the human brain: Csaba Kerepesi, Balázs Szalkai, Bálint Varga, Vince Grolmusz: How to Direct the Edges of the Connectomes: Dynamics of the Consensus Connectomes and the Development of the Connections in the Human Brain,  PLoS One 11(6): e0158680. , June 30, 2016 (also in arXiv:1509.05703).
  • We have shown – first in the literature – that much more bacterial taxa lack dUTPase genes than it was previously thought: Csaba Kerepesi, Judit E Szabó, Veronika Papp-Kádár, Orsolya Dobay, Dóra Szabó, Vince Grolmusz, Beata G Vertessy: Life without dUTPase, Frontiers in Microbiology, DOI: 10.3389/fmicb.2016.01768 (2016))
  • We have shown – first in the literature – that in numerous deep graph theoretical parameters, the female brain is better connected that the brain of males: Balázs Szalkai, Bálint Varga, Vince Grolmusz: Graph Theoretical Analysis Reveals: Women’s Brains are Better Connected than Men’s,  PLoS One, 10(7): e0130045. doi:10.1371/journal.pone.0130045, it is the journal version of arXiv preprint arXiv:1501.00727,  January 4, 2015
  • We have shown – first in the literature – that the better connectivity in female brains remains valid if we consider the braingraphs of large-brain females and small-brain males. That is, the female advantage depends on the sex, and not on the average brain size: Balázs Szalkai, Bálint Varga, Vince Grolmusz: Brain Size Bias Compensated Graph-Theoretical Parameters are Also Better in Women’s Structural Connectomes,  Brain Imaging and Behavior (2017) , also in  arXiv preprint arXiv:1512.01156  (2015)
  • We have developed – first in the literature – a parametrizable consensus -connectome webserver that can be applied for generating and visualizing the “average, healthy” connections of the brain. The server is the Budapest Reference Connectome Server : Balázs Szalkai, Csaba Kerepesi, Vince Grolmusz: The Budapest Reference Connectome Server v2.0, Neuroscience Letters, Vol. 595, 19 May 2015, Pages 60-62. DOI: http://dx.doi.org/10.1016/j.neulet.2015.03.071, and also: Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince Grolmusz: Parameterizable Consensus Connectomes from the Human Connectome Project: The Budapest Reference Connectome Server v3.0,  Cognitive Neurodynamics, (2016) http://dx.doi.org/10.1007/s11571-016-9407-z
  • We have shown – first in the literature- that in a large dataset of type-2 diabetic and healthy subjects, the nucleotide 9-mers of the gut-microbiome of the diabetic patients are characteristic: Balázs Szalkai,  Vince Grolmusz: Nucleotide 9-mers Characterize the Type II Diabetic Gut Metagenome, Genomics, Vol. 107 (2016) pp. 120-123, http://dx.doi.org/10.1016/j.ygeno.2016.02.007
  • We have developed – first in the literature – a method for finding low-degree important protein targets in protein-protein interaction networks: Vince Grolmusz: Identifying diabetes-related important protein targets with few interacting partners with the PageRank algorithm, Royal Society Open Science, 2:140252, (2015) http://dx.doi.org/10.1098/rsos.140252 & Dániel Bánky, Gábor Iván, Vince Grolmusz: Equal Opportunity for Low-Degree Network Nodes: A PageRank-Based Method for Protein Target Identification in Metabolic Graphs, PLoS ONE 8(1): e54204. doi:10.1371/journal.pone.0054204, published on January 29, 2013
  • We have shown – first in the literature – that in undirected graphs, the PageRank is usually not proportional to the degree; we also have given a necessary- and sufficient condition when it is: Vince Grolmusz:  A Note on the PageRank of Undirected Graphs,  Information Processing Letters, Vol. 115 (2015), pp. 633-634. http://dx.doi.org/10.1016/j.ipl.2015.02.015. Also in arXiv 1205.1960, May 10, 2012.
  • We have developed – first in the literature – The Metagenomic Telescope, i.e., a bioinformatics tool that is capable of assigning new probable functions to human proteins through two subsequent projections, applying extreme metagenomes: Balázs Szalkai, Ildikó Scheer, Kinga Nagy, Beáta G Vértessy, Vince Grolmusz, The Metagenomic Telescope, PLoS One, Vol. 9, No. 7, e101605, July 2014,  http://dx.doi.org/10.1371/journal.pone.0101605