Main contributions to science, in inverse chronological order:

Contribution 6: Braingraphs

We have introduced the Frequent Network Neighborhood Mapping method, and applied it to the neighbors of the human hippocampus for sex- and intelligence differencies in [15,16]. We created the first consensus graph server (the Budapest Reference Connectome Server) [6,1]; we proved that women’s braingraphs had better connectivity properties than that of the men [2,5,8,14,17] at the first time in the literature. We have mapped the differences in the individual variability of the braingraphs in lobes and in smaller brain areas [3,13], also first in the literature. We also have recognized – first in the literature – that most probably there is a close connection between the number of occurrences of a certain edge in the braingraphs of different subjects and the time of its development [4,9,10,12]. We have shown that the women’s advantage in brain connectedness is due to sex, and not to the size of the brain [8]. We have made hundreds of braingraphs, computed by us, available for the community [11]. We have published hundreds of high resolution directed connectomes [10], first in the literature. In [12] we have shown the robustness of the Consensus Connectome Dynamics (CCD) phenomenon, and we have also given a probabilistic simulation that well-approximates the CCD phenomenon, also first in the literature.

17, The Frequent Complete Subgraphs in the Human Connectome; Máté Fellner, Bálint Varga, Vince Grolmusz;   PLOS ONE  15(8): e0236883 (2020)

16, 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)

15, 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).

14, The Frequent Subgraphs of the Connectome of the Human Brain, M. Fellner, B. Varga, V. Grolmusz; Cognitive Neurodynamics Vol. 13, No.5, pp. 453-460 (2019)

13, Comparing Advanced Graph-Theoretical Parameters of the Connectomes of the Lobes of the Human Brain, B. Szalkai, B. Varga, V. Grolmusz; Cognitive Neurodynamics, Vol. 12, No. 6, pp. 549-559 (2018),

12, The Robustness and the Doubly-Preferential Attachment Simulation of the Consensus Connectome Dynamics of the Human Brain, B Szalkai, B Varga, V Grolmusz,   Scientific Reports, Vol. 7, 16118, DOI: 10.1038/s41598-017-16326-0  (2017)

11, The Database of High Resolution Structural Connectomes and the Brain Graph Tools, Csaba Kerepesi, Balázs Szalkai,  Bálint Varga, Vince Grolmusz;  Cognitive Neurodynamics Vol. 11 No. 5, pp. 483-486  (2017) ; also in arXiv preprint arXiv:1610:02016 (2016)

10, High-Resolution Directed Human Connectomes and the Consensus Connectome Dynamics, Balázs Szalkai, Csaba Kerepesi, Bálint Varga, Vince Grolmusz; PLoS One, Vol. 14 No. 4,: e0215473 (2019)

9, The Dorsal Striatum and the Dynamics of the Consensus Connectomes in the Frontal Lobe of the Human Brain, C Kerepesi, B Varga, B Szalkai,  V Grolmusz; Neuroscience Letters, Vol. 673, (2018), pp. 51-55.2018

 8, Brain Size Bias Compensated Graph-Theoretical Parameters are Also Better in Women’s Structural Connectomes B. Szalkai, B. Varga, V. Grolmusz; Brain Imaging and Behavior Vol. 12, No. 3, pp. 663-673, (2018) , also an arXiv preprint arXiv:1512.01156  (2015)

 7, Mapping Correlations of Psychological and Connectomical Properties of the Dataset of the Human Connectome Project with the Maximum Spanning Tree Method B Szalkai, B Varga, V Grolmusz: Brain Imaging and Behavior, available online August 7, 2018,

6, Parameterizable Consensus Connectomes from the Human Connectome Project: The Budapest Reference Connectome Server v3.0 B Szalkai, C Kerepesi, B Varga, V Grolmusz, Cognitive Neurodynamics, 11(1), pp. 113-116, (2017)

5, The Graph of Our Mind, B Szalkai, B Varga, V Grolmusz: arXiv preprint arXiv:1603.00904 (2016)

4, How to Direct the Edges of the Connectomes: Dynamics of the Consensus Connectomes and the Development of the Connections in the Human Brain C Kerepesi, B Szalkai, B Varga, V Grolmusz PLoS One 11(6): e0158680. , June 30, 2016

3, Comparative Connectomics: Mapping the Inter-Individual Variability of Connections within the Regions of the Human Brain C Kerepesi, B Szalkai, B Varga, V Grolmusz;  Neuroscience Letters Vol. 662, pp. 17-21, (2018),;

2, Graph Theoretical Analysis Reveals: Women’s Brains are Better Connected than Men’s, B Szalkai, B Varga, V Grolmusz PLoS One 10 (7), e0130045 (2015)

1, The Budapest Reference Connectome Server v2. 0 B Szalkai, C Kerepesi, B Varga, V Grolmusz, Neuroscience Letters 595, 60-62 (2015)

Contribution 5: Artificial intelligence methods in life sciences

1, SECLAF: A Webserver and Deep Neural Network Design Tool for Biological Sequence Classification, B Szalkai, V Grolmusz, Bioinformatics, Vol 34, No. 14, pp. 2487-2489 (2018)

2, MetaHMM: A Webserver for Identifying Novel Genes with Specified Functions in Metagenomic Samples, B Szalkai, V Grolmusz, Genomics, available online May 23, 2018,

3, Near Perfect Protein Multi-Label Classification with Deep Neural Networks, B Szalkai, V Grolmusz, Methods Vol. 132, pp. 50-56, (2018),,

4, The Metagenomic Telescope, B Szalkai, I Scheer, K Nagy, B G Vértessy, V Grolmusz, PLoS One, Vol. 9, No. 7, e101605 (2014).

Contribution 4: PageRank-based protein network analysis

 We have applied the PageRank of Google for the evaluation of protein-protein interaction networks, and introduced a modified version (the PageRank, divided by the degree of the node) [1,2,3] that is capable of identifying low-degree important network nodes.

4, When the Web meets the cell: using personalized PageRank for analyzing protein interaction networks, G Iván, V Grolmusz, Bioinformatics 27 (3), 405-407 (2011)

3, A Note on the PageRank of Undirected Graphs, V Grolmusz, Information Processing Letters 115, 633-634 (2015)

2, Identifying diabetes-related important protein targets with few interacting partners with the PageRank algorithm V Grolmusz, Royal Society Open Science 2 (4), 140252 (2015)

1, Equal opportunity for low-degree network nodes: a PageRank-based method for protein target identification in metabolic graphs, D Banky, G Ivan, V Grolmusz, PLoS One 8 (1), e54204 (2013)

Contribution 3: Data-mining in large biological databases and molecular modeling

 We applied data-mining methods for making discoveries in large biological databases. The discoveries involved combinatorial biomarker identification in Alzheimer’s disease {10], protein 3D structure analysis [1,2,4], the construction of the new protein-docking tool „Frigate” [3], the construction of the Metagenomic Telescope [6], and the identification of giant viruses in desert environments [5, 9]. In [2] we have shown that only four (spatial) points in the 3D structure of the large family of serine proteases determine the function of the enzyme in question.

10, Identifying combinatorial biomarkers by association rule mining in the CAMD Alzheimer’s database Balazs Szalkai, Vince K. Grolmusz, Vince I. Grolmusz, Archives of Gerontology and Geriatrics, Volume 73, November–December 2017, pp. 300-307

9, The “Giant Virus Finder” discovers an abundance of giant viruses in the Antarctic dry valleys, C Kerepesi,  V Grolmusz,  Archives of Virology, Vol. 162, No. 6, pp. 1671-1676 (2017)

8, Life without dUTPase, C Kerepesi, J E Szabó, V Papp-Kádár, O Dobay, D Szabó, V Grolmusz, B G Vertessy; Frontiers in Microbiology, Vol. 7, pp: 1768, (2016)

7, Nucleotide 9-mers Characterize the Type II Diabetic Gut Metagenome; B Szalkai, V Grolmusz, Genomics, Vol. 107  pp. 120-123 (2016),

6, The Metagenomic Telescope, B Szalkai, I Scheer, K Nagy, B G Vértessy, V Grolmusz, PLoS One, Vol. 9, No. 7, e101605 (2014).

5, Giant Viruses of the Kutch Desert, C Kerepesi, V Grolmusz; Archives of Virology,  Vol. 161, No.3 pp.721-724, (2016)

4, On the asymmetry of the residue compositions of the binding sites on protein surfaces, G Iván, Z Szabadka, V Grolmusz Journal of Bioinformatics and Computational Biology Vol. 7 No. 6, 931 (2009)

3, Discovery of novel MDR-Mycobacterium tuberculosis inhibitor by new FRIGATE computational screen; C Scheich, Z Szabadka, B Vértessy, V Pütter, V Grolmusz, M Schade, PloS One 6 (12), e28428 (2011)

2, Four spatial points that define enzyme families, G Iván, Z Szabadka, R Ördög, V Grolmusz, G Náray-Szabó; Biochemical and Biophysical Research Communications Vol. 383 No. 4, pp. 417-420 (2009)

1, A hybrid clustering of protein binding sites, G Iván, Z Szabadka, V Grolmusz, FEBS Journal Vol. 277 No. 6, pp. 1494-1502 (2010)

Contribution 2: Extremal set systems modulo composite numbers and their applications

 We have falsified a longtime conjecture of Frankl through a construction of  mod 6-restricted intersection set systems [1], and found numerous applications [2,3,4] for that surprising construction, involving very fast matrix multiplication and a very dense coding method, called „Hyperdense coding” [4].

5, k-wise Set-Intersections and k-wise Hamming-Distances  V Grolmusz, B Sudakov, J. Combin. Theory Ser. A 99 (2002), no. 1, 180–190.

4, Modular Representations of Polynomials: Hyperdense Coding and Fast Matrix Multiplication, V Grolmusz, , IEEE Transactions on Information Theory, 54 (8), 3687-3692 (2008)

3, Computing Elementary Symmetric Polynomials with a Subpolynomial Number of Multiplications, V Grolmusz, SIAM Journal on Computing 32 (6), 1475-1487 (2003)

2, Constructing set systems with prescribed intersection sizes, V Grolmusz, Journal of Algorithms 44 (2), 321-337 (2002)

1, Superpolynomial size set-systems with restricted intersections mod 6 and explicit Ramsey graphs, V Grolmusz, Combinatorica 20 (1), 71-86 (2000)

Contribution 1: Multiparty communication complexity and circuit complexity

In theoretical computer science, we invented new multiparty communication protocols and lower bounds to the size of Boolean circuits with modular gates. Several of our results appeared the most prestigious FOCS and STOC conferences.

6, Incomparability in Parallel Computation; V Grolmusz, P Ragde, Proceedings of the 28th Annual Symposium on Foundations of Computer Science (FOCS), Los Angeles 1987, pp. 89-98, also in Discrete Applied Mathematics, Vol. 29 (1990), No. 1. pp. 63–78.

5, Circuits and multi-party protocols, V Grolmusz, Computational Complexity 7 (1), 1-18 (1998)

4, A weight-size trade-off for circuits with mod m gates, V Grolmusz, Proceedings of the Twenty-Sixth Annual ACM Symposium on Theory of Computing pp. 68-74 (1994) (STOC 1994)

3, Lower Bounds for (MOD p, MOD m) Circuits, V Grolmusz, G Tardos, SIAM Journal on Computing 29 (4), 1209-1222 (2000), (also in FOCS’1998).

2, Separating the communication complexities of MOD m and MOD p circuits, V Grolmusz, Journal of Computer and Systems Sciences 51 (2) (1995) (also in FOCS’1992)

1, The BNS Lower-Bound for Multiparty Protocols Is Nearly Optimal, V Grolmusz, Information and Computation 112 (1), 51-54 (1994)