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Publications grouped by topic
This page lists selected publications co-authored by the members of the BioIntelligence Lab, including their publications at various institutions and the ones written before joining the lab. Please see the papers for more information.
Topic categories:
Biomedicine
K. Buza, L. Peska, J. Koller (2020): Modified linear regression predicts drug-target interactions accurately, PLoS ONE 15(4): e0230726.
K. Buza (2020): ASTERICS: Projection-based Classification of EEG with Asymmetric Loss Linear Regression and Genetic Algorithm, 14th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza, T. Horváth (2019): The role of warping window size in case of EEG classification, [pdf] [Slides] Clinical Neurophysiology, Volume 130, Issue 7, page e34
K. Buza, T. Horváth (2019): EEG Classification: The Warping Window Size Does Not Matter That Much, XI. Dubrovnik Conference on Cognitive Science (DUCOG), poster
A. Szenkovits, R. Meszlényi, K. Buza, N. Gaskó, R.I. Lung, M. Suciu (2018): Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data, in U. Stanczyk, B. Zielosko, L.C. Jain: Advances in Feature Selection for Data and Pattern Recognition, Springer
K. Buza, L. Peska (2017): ALADIN: A New Approach for Drug-Target Interaction Prediction, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Springer. [Supplementary experiments - XLSX] [Paper] [Poster] [Slides]
L. Peska, K. Buza, J. Koller (2017): Drug-target interaction prediction: A Bayesian ranking approach [Preprint] [Supplementary material], Computer Methods and Programs in Biomedicine, Vol. 152, pp. 15-21
Regina J. Meszlényi, Krisztian Buza, Zoltán Vidnyánszky (2017): Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture, Frontiers in Neuroinformatics, Vol. 11
Regina J. Meszlényi, Petra Hermann, Krisztian Buza, Viktor Gál, Zoltán Vidnyánszky (2017): Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping, Frontiers in Neuroscience, Vol. 11
Regina Meszlényi, Ladislav Peska, Viktor Gal, Zoltán Vidnyánszky, Krisztian Buza (2016): A model for classification based on the functional connectivity pattern dynamics of the brain, The Third European Network Intelligence Conference (ENIC 2016)
Regina Meszlényi, Ladislav Peska, Viktor Gal, Zoltán Vidnyánszky, Krisztian Buza (2016): Classification of fMRI data using Dynamic Time Warping based functional connectivity analysis, 24th European Signal Processing Conference (EUSIPCO)
Rodica Ioana Lung, Mihai Suciu, Regina Meszlényi, Krisztian Buza, Noémi Gaskó (2016): Community structure detection for the functional connectivity networks of the brain, 14th International Conference on Parallel Problem Solving from Nature
K. Buza, N.Á. Varga (2016): ParkinsoNET: Estimation of UPDRS Score using Hubness-aware Feed-Forward Neural Networks, Applied Artificial Intelligence, special issue on Intelligent methods applied to healthcare information systems
K. Buza (2016): Drug-Target Interaction Prediction with Hubness-aware Machine Learning, 11th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza (2016): Classification of Gene Expression Data: A Hubness-aware Semi-Supervised Approach,[Audio Slides] Computer Methods and Programs in Biomedicine
K. Buza, J. Koller (2016): Classification of Electroencephalograph Data: A Hubness-aware Approach, Acta Polytechnica Hungarica
K. Buza, J. Koller, K. Marussy (2015): PROCESS: Projection-Based Classification of Electroencephalograph Signals [paper] [poster], ICAISC, LNCS Vol. 9120, pp. 91-100, Springer.
K. Buza (2015): Semi-supervised Naive Hubness-Bayesian k-Nearest Neighbor for Gene Expression Data, Proceedings of the 9th International Conference on Computer Recognition Systems (CORES), Springer. CORES Award
K. Buza, B. Wilczynski, N. Dojer (2015): RECORD: Reference-Assisted Genome Assembly for Closely Related Genomes, International Journal of Genomics
K. Buza, N. A. Varga (2015): Machine Learning for the Estimation of UPDRS score, VII. Dubrovnik Conference on Cognitive Science (DUCOG), poster
K. Marussy, K. Buza (2014): PROGRESS: Projection-Based Gene Expression Classification, Innovations in Medicine Conference (poster)
K. Marussy (2014): The Curse of Intrinsic Dimensionality in Genome Expression Classification, Students' Scientific Conference, Budapest University of Technology and Economics, Second prize
L.A. Laviolette, J. Wilson, J. Koller, C. Neil, P. Hulick, T. Rejtar, B. Karger, B.T. Teh, O. Iliopoulos (2013): Human folliculin delays cell cycle progression through late S and G2/M-phases: effect of phosphorylation and tumor associated mutations, PLoS One, 8:(7), e66775.
K. Buza, J. Koller (2013): Speeding up the classification of biomedical signals via instance selection, [poster] [abstract], 5th Dubrovnik Conference on Cognitive Science, Learning & Perception,
5:(Suppl 1).
K. Buza, B. Wilczynski, N. Dojer (2013): A Simple and Effective Technique for Assisted Genome Assembly, 21st Annual International Conference on Intelligent Systems for Molecular Biology & 12th European Conference on Computational Biology, poster
M. Bence, J. Koller, M. Sasvari-Szekely, G. Keszler (2012): Transcriptional modulation of monoaminergic neurotransmission genes by the histone deacetylase inhibitor trichostatin A in neuroblastoma cells, Journal of Neural Transmission, 119:(1), pp. 17-24.
L.A. Laviolette, J. Wilson, J. Koller, M. Zimmer, O. Iliopoulos (2012): Human FLCN delays cell cycle progression through late S and G2/M-phases: Effect of phosphorylation and tumor-associated mutations, Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research, Cancer Research 72:(8 Suppl)
K. Buza, A. Nanopoulos, L. Schmidt-Thieme, J. Koller (2011): Fast Classification of Electrocardiograph Signals via Instance Selection, Proceedings of the First IEEE conference on Healthcare Informatics, Imaging and Systems Biology.
Hubness-aware machine learning
For an implementation of hubness-aware machine learning techniques, see the PyHubs software package.
K. Buza (2022): An Efficient Implementation of Hubness-Aware Weighting Using Cython, Slovenian KDD (SiKDD) Conference, Ljubljana, Slovenia
K. Buza, L. Peska (2017): Drug-target interaction prediction with Bipartite Local Models and hubness-aware regression, [Preprint] [Audio Slides] Neurocomputing, Volume 260, pp. 284-293
K. Buza, L. Peska (2017): ALADIN: A New Approach for Drug-Target Interaction Prediction, European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), Springer. [Supplementary experiments - XLSX] [Paper] [Poster] [Slides]
L. Peska, K. Buza, J. Koller (2017): Drug-target interaction prediction: A Bayesian ranking approach [Preprint] [Supplementary material], Computer Methods and Programs in Biomedicine, Vol. 152, pp. 15-21
D. Neubrandt, K. Buza (2017): Projection-based Person Identification, Proceedings of the 10th International Conference on Computer Recognition Systems (CORES), Springer. [ipynb] [Ipython Notebook in HTML]
K. Buza, N.Á. Varga (2016): ParkinsoNET: Estimation of UPDRS Score using Hubness-aware Feed-Forward Neural Networks, Applied Artificial Intelligence, special issue on Intelligent methods applied to healthcare information systems
K. Buza (2016): Person Identification Based on Keystroke Dynamics: Demo and Open Challenge , 28th International Conference on Advanced Information Systems Engineering (CAiSE'16) Forum
See also: Person Identification Challenge
K. Buza, D. Neubrandt (2016): How You Type Is Who You Are, 11th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza (2016): Drug-Target Interaction Prediction with Hubness-aware Machine Learning, 11th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza (2016): Classification of Gene Expression Data: A Hubness-aware Semi-Supervised Approach,[Audio Slides] Computer Methods and Programs in Biomedicine
K. Buza, J. Koller (2016): Classification of Electroencephalograph Data: A Hubness-aware Approach, Acta Polytechnica Hungarica
N. Tomasev, K. Buza, D. Mladenic (2016): Correcting the Hub Occurrence Prediction Bias in Many Dimensions, Computer Science and Information Systems
K. Buza, D. Neubrandt (2016): A New Proposal for Person Identification Based on the Dynamics of Typing: Preliminary Results, Theoretical and Applied Informatics, Vol. 28, No. 1-2
K. Buza, A. Nanopoulos, G. Nagy (2015): Nearest Neighbor Regression in the Presence of Bad Hubs [Preprint] [Audio Slides], Knowledge-Based Systems, Volume 86, pp. 250-260
N. Tomasev, K. Buza (2015): Hubness-aware kNN Classification of High-dimensional Data in Presence of Label Noise, [Preprint] [Audio Slides] Neurocomputing, Volume 160, pp. 157-172
K. Buza (2015): Semi-supervised Naive Hubness-Bayesian k-Nearest Neighbor for Gene Expression Data, Proceedings of the 9th International Conference on Computer Recognition Systems (CORES), Springer. CORES Award
K. Buza (2015): Hubness: An Interesting Property of Nearest Neighbor Graphs and its Impact on Classification, 9th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications, Invited talk
N. Tomasev, K. Buza, K. Marussy, P.B. Kis (2015): Hubness-aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series, In: U. Stanczyk, L. Jain (eds.), Feature selection for data and pattern recognition, Springer-Verlag
K. Marussy (2014): The Curse of Intrinsic Dimensionality in Genome Expression Classification, Students' Scientific Conference, Budapest University of Technology and Economics, Second prize
K. Buza, J. Koller (2013): Speeding up the classification of biomedical signals via instance selection, [poster] [abstract], 5th Dubrovnik Conference on Cognitive Science, Learning & Perception,
5:(Suppl 1).
K. Marussy, K. Buza (2013): Hubness-based indicators for semi-supervised time-series classification, 8th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications
K. Marussy (2012): A new approach for more accurate semi-supervised time-series classification, Students' Scientific Conference, Budapest University of Technology and Economics, Second prize
K. Buza, A. Nanopoulos, L. Schmidt-Thieme (2011): INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification, Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), LNCS Vol. 6635, pages 149-160, Springer. The original publication is available at www.springerlink.com.
K. Buza, A. Nanopoulos, L. Schmidt-Thieme, J. Koller (2011): Fast Classification of Electrocardiograph Signals via Instance Selection, Proceedings of the First IEEE conference on Healthcare Informatics, Imaging and Systems Biology.
K. Buza (2011): Fusion Methods for Time Series Classification, Peter Lang Verlag.
K. Buza, A. Nanopoulos, L. Schmidt-Thieme (2010): Time-Series Classification based on Individualised Error Prediction, 13th IEEE International Conference on Computational Science and Engineering (CSE-2010). Best Paper Award
Time series & tick data
K. Buza (2023): Sparsity-invariant Convolution for Forecasting Irregularly Sampled Time Series, 15th International Conference on Computational Collective Intelligence (ICCCI)
Ladislav Peska, Patrik Vesely, Tomas Skopal, Krisztian Buza (2022): Person Authentication using Visual Representations of Keyboard Typing Dynamics, to appear in the Proceedings of the 9th International Conference on Social Networks Analysis, Management and Security (SNAMS 2022)
K. Buza, M. Antal (2022): An Extension of Dynamic Time Warping for Images: Dynamic Image Warping, 14th Joint Conference on Mathematics and Computer Science, Cluj-Napoca
R. Berszán, K. Buza (2022): Automated Recognition of Emotions Based on Images of Facial Expressions, 13th Dubrovnik Conference on Cognitive Science (DUCOG), poster
K. Buza, M. Antal (2021): Convolutional neural networks with dynamic convolution for time series classification, 13th International Conference on Computational Collective Intelligence (ICCCI) [paper] [slides] [video]
M. Antal, K. Buza, N. Fejer (2021): SapiAgent: A Bot Based on Deep Learning to Generate Human-Like Mouse Trajectories, IEEE Access, vol. 9, pp. 124396-124408 [paper]
K. Buza (2020): ASTERICS: Projection-based Classification of EEG with Asymmetric Loss Linear Regression and Genetic Algorithm, 14th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza, L. Peska (2019): Individualized Warping Window Size for Dynamic Time Warping, [Paper] [Poster] Time Series Workshop at the 36th International Conference on Machine Learning, Long Beach, USA
K. Buza, T. Horváth (2019): The role of warping window size in case of EEG classification, [pdf] [Slides] Clinical Neurophysiology, Volume 130, Issue 7, page e34
K. Buza, T. Horváth (2019): EEG Classification: The Warping Window Size Does Not Matter That Much, XI. Dubrovnik Conference on Cognitive Science (DUCOG), poster
K. Buza (2018): Time Series Classification and its Applications, invited tutorial at the 8th International Conference on Web Intelligence, Mining and Semantics. [Paper] [Slides]
A. Szenkovits, R. Meszlényi, K. Buza, N. Gaskó, R.I. Lung, M. Suciu (2018): Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data, in U. Stanczyk, B. Zielosko, L.C. Jain: Advances in Feature Selection for Data and Pattern Recognition, Springer
Regina J. Meszlényi, Krisztian Buza, Zoltán Vidnyánszky (2017): Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture, Frontiers in Neuroinformatics, Vol. 11
Regina J. Meszlényi, Petra Hermann, Krisztian Buza, Viktor Gál, Zoltán Vidnyánszky (2017): Resting State fMRI Functional Connectivity Analysis Using Dynamic Time Warping, Frontiers in Neuroscience, Vol. 11
D. Neubrandt, K. Buza (2017): Projection-based Person Identification, Proceedings of the 10th International Conference on Computer Recognition Systems (CORES), Springer. [ipynb] [Ipython Notebook in HTML]
K. Buza, Piroska B. Kis (2017): Towards Privacy-aware Keyboards, Proceedings of the 10th International Conference on Computer Recognition Systems (CORES), Springer. [video]
Regina Meszlényi, Ladislav Peska, Viktor Gal, Zoltán Vidnyánszky, Krisztian Buza (2016): A model for classification based on the functional connectivity pattern dynamics of the brain, The Third European Network Intelligence Conference (ENIC 2016)
Regina Meszlényi, Ladislav Peska, Viktor Gal, Zoltán Vidnyánszky, Krisztian Buza (2016): Classification of fMRI data using Dynamic Time Warping based functional connectivity analysis, 24th European Signal Processing Conference (EUSIPCO)
K. Buza (2016): Person Identification Based on Keystroke Dynamics: Demo and Open Challenge , 28th International Conference on Advanced Information Systems Engineering (CAiSE'16) Forum
See also: Person Identification Challenge
K. Buza, D. Neubrandt (2016): How You Type Is Who You Are, 11th International Symposium on Applied Computational Intelligence and Informatics, IEEE
K. Buza, J. Koller (2016): Classification of Electroencephalograph Data: A Hubness-aware Approach, Acta Polytechnica Hungarica
N. Tomasev, K. Buza, D. Mladenic (2016): Correcting the Hub Occurrence Prediction Bias in Many Dimensions, Computer Science and Information Systems
K. Buza, D. Neubrandt (2016): A New Proposal for Person Identification Based on the Dynamics of Typing: Preliminary Results, Theoretical and Applied Informatics, Vol. 28, No. 1-2
N. Tomasev, K. Buza (2015): Hubness-aware kNN Classification of High-dimensional Data in Presence of Label Noise, [Preprint] [Audio Slides] Neurocomputing, Volume 160, pp. 157-172
K. Buza, J. Koller, K. Marussy (2015): PROCESS: Projection-Based Classification of Electroencephalograph Signals [paper] [poster], ICAISC, LNCS Vol. 9120, pp. 91-100, Springer.
N. Tomasev, K. Buza, K. Marussy, P.B. Kis (2015): Hubness-aware Classification, Instance Selection and Feature Construction: Survey and Extensions to Time-Series, In: U. Stanczyk, L. Jain (eds.), Feature selection for data and pattern recognition, Springer-Verlag
K. Buza, G. Nagy, A. Nanopoulos (2014): Storage-Optimizing Clustering Algorithms for High-Dimensional Tick Data, Expert Systems with Applications, 41, pp. 4148-4157.
K. Buza, G. I. Nagy, A. Nanopoulos (2014): Trend analysis and anomaly detection in time series of language usage, VI. Dubrovnik Conference on Cognitive Science (DUCOG), poster
K. Buza, G. I. Nagy, A. Nanopoulos (2014): Three Open Questions related to the Tick Data Decomposition Problem, Summit240 Conference, abstract
K. Buza, K. Marussy (2014): Aspects of Complexity in Data Analysis Tasks - Some Use-Cases, Workshop on Mining Complex Data, 25-27th October, 2014, Kosice, Slovakia
K. Marussy, K. Buza (2013): SUCCESS: A New Approach for Semi-Supervised Classification of Time-Series, ICAISC, LNCS Vol. 7894, pp. 437-447, Springer. The original publication is available at www.springerlink.com.
K. Buza, J. Koller (2013): Speeding up the classification of biomedical signals via instance selection, [poster] [abstract], 5th Dubrovnik Conference on Cognitive Science, Learning & Perception,
5:(Suppl 1).
K. Marussy, K. Buza (2013): Hubness-based indicators for semi-supervised time-series classification, 8th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications
G.I. Nagy, K. Buza (2012): Efficient Storage of Tick Data That Supports Search and Analysis, [paper] [presentation slides], 12th Industrial Conference on Data Mining, Berlin, LNCS Vol. 7377, pp. 38-51, Springer. Nominated for the Best Paper Award.The original publication is available at www.springerlink.com.
G.I. Nagy, K. Buza (2012): Clustering Algorithms for Storage of Tick Data, The 36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery August 1-3, 2012, Hildesheim, Germany
G.I. Nagy, K. Buza (2012): Partitional Clustering of Tick Data to Reduce Storage Space, IEEE 16th International Conference on Intelligent Engineering Systems
K. Marussy (2012): A new approach for more accurate semi-supervised time-series classification, Students' Scientific Conference, Budapest University of Technology and Economics, Second prize
K. Buza, A. Nanopoulos, L. Schmidt-Thieme (2011): INSIGHT: Efficient and Effective Instance Selection for Time-Series Classification, Proceedings of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), LNCS Vol. 6635, pages 149-160, Springer. The original publication is available at www.springerlink.com.
K. Buza, A. Nanopoulos, L. Schmidt-Thieme, J. Koller (2011): Fast Classification of Electrocardiograph Signals via Instance Selection, Proceedings of the First IEEE conference on Healthcare Informatics, Imaging and Systems Biology.
K. Buza (2011): Fusion Methods for Time Series Classification, Peter Lang Verlag.
K. Buza, A. Nanopoulos, L. Schmidt-Thieme (2010): Time-Series Classification based on Individualised Error Prediction, 13th IEEE International Conference on Computational Science and Engineering (CSE-2010). Best Paper Award
Web & text mining
K. Buza, T. Horváth (2019): Factorization Machines for Blog Feedback Prediction, Proceedings of the 11th International Conference on Computer Recognition Systems (CORES), Springer. [Paper] [Poster]
K. Marussy, L. Peška, K. Buza (2015): Recommendations of Unique Items Based on Bipartite Graphs, 9th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications
K. Buza, G. I. Nagy, A. Nanopoulos (2014): Trend analysis and anomaly detection in time series of language usage, VI. Dubrovnik Conference on Cognitive Science (DUCOG), poster
Krisztian Buza (2014): Feedback Prediction for Blogs, [paper] [Data used for the experiments in the paper] [presentation slides] The 36th Annual Conference of the German Classification Society on Data Analysis, Machine Learning and Knowledge Discovery August 1-3, 2012, Hildesheim, Germany
K. Buza, K. Marussy (2014): Aspects of Complexity in Data Analysis Tasks - Some Use-Cases, Workshop on Mining Complex Data, 25-27th October, 2014, Kosice, Slovakia
K. Buza, I. Galambos (2013): An Application of Link Prediction in Bipartite Graphs: Personalized Blog Feedback Prediction, 8th Japanese-Hungarian Symposium on Discrete Mathematics and Its Applications
K. Buza, A. Nanopoulos, T. Horváth, L. Schmidt-Thieme (2012): GRAMOFON: General Model-selection Framework based on Networks, Neurocomputing, Volume 75, Issue 1, pp. 163-170, Elsevier
S. Blohm, K. Buza, P. Cimiano, L. Schmidt-Thieme (2011): Relation Extraction for the Semantic Web with Taxonomic Sequential Patterns, in V. Sugumaran and J.A. Gulla: Applied Semantic Web Technologies, CRC Press, Taylor&Francis Group.
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