@article{MTMT:31964564,
	title = {Detecting Selection from Linked Sites Using an F -Model},
	url = {https://m2.mtmt.hu/api/publication/31964564},
	author = {Galimberti, Marco and Leuenberger, Christoph and Wolf, Beat and Szilágyi, Sándor Miklós and Foll, Matthieu and Wegmann, Daniel},
	doi = {10.1534/genetics.120.303780},
	journal-iso = {GENETICS},
	journal = {GENETICS},
	volume = {216},
	unique-id = {31964564},
	issn = {0016-6731},
	year = {2020},
	eissn = {1943-2631},
	pages = {1205-1215},
	orcid-numbers = {Galimberti, Marco/0000-0001-6052-156X; Leuenberger, Christoph/0000-0002-5006-6484; Wolf, Beat/0000-0002-9307-7212; Szilágyi, Sándor Miklós/0000-0002-5657-7365; Foll, Matthieu/0000-0001-9006-8436; Wegmann, Daniel/0000-0003-2866-6739}
}

@article{MTMT:31071739,
	title = {Impact of inflammation-mediated response on pan-coronary plaque vulnerability, myocardial viability and ventricular remodeling in the postinfarction period - the VIABILITY study Protocol for a non-randomized prospective clinical study},
	url = {https://m2.mtmt.hu/api/publication/31071739},
	author = {Morariu, Mirabela and Hodas, Roxana and Benedek, Theodora and Benedek, Imre-Sándor and Opincariu, Diana and Mester, Andras and Chitu, Monica and Kovacs, Istvan and Rezus, Ciprian and Pasaroiu, Dan and Mitra, Noemi and Szilágyi, Sándor Miklós and Georgescu, Dan and Rezus, Elena},
	doi = {10.1097/MD.0000000000015194},
	journal-iso = {MEDICINE},
	journal = {MEDICINE},
	volume = {98},
	unique-id = {31071739},
	issn = {0025-7974},
	abstract = {Introduction: While the role of inflammation in acute coronary events is well established, the impact of inflammatory-mediated vulnerability of coronary plaques from the entire coronary tree, on the extension of ventricular remodeling and scaring, has not been clarified yet.Materials and methods: The present manuscript describes the procedures of the VIABILITY trial, a descriptive prospective single-center cohort study. The main purpose of this trial is to assess the link between systemic inflammation, pan-coronary plaque vulnerability (referring to the plaque vulnerability within the entire coronary tree), myocardial viability and ventricular remodeling in patients who had suffered a recent ST-segment elevation acute myocardial infarction (STEMI). One hundred patients with STEMI who underwent successful revascularization of the culprit lesion in the first 12 hours after the onset of symptoms will be enrolled in the study. The level of systemic inflammation will be evaluated based on the serum biomarker levels (hs-CRP, matrix metalloproteinases, interleukin-6) in the acute phase of the myocardial infarction (MI) and at 1 month. Pan-coronary plaque vulnerability will be assessed based on serum biomarkers known to be associated with increased plaque vulnerability (V-CAM or I-CAM) and at 1 month after infarction, based on computed tomographic angiography analysis of vulnerability features of all coronary plaques. Myocardial viability and remodeling will be assessed based on 3D speckle tracking echocardiography associated with dobutamine infusion and LGE-CMR associated with post-processing imaging methods. The study population will be categorized in 2 subgroups: subgroup 1 -subjects with STEMI and increased inflammatory response at 7 days after the acute event (hs-CRP >= 3mg/dl), and subgroup 2 -subjects with STEMI and no increased inflammatory response at 7 days (hs-CRP<3mg/dl). Study outcomes will consist in the rate of post-infarction heart failure development and the major adverse events (MACE) rate.Conclusion: VIABILITY is the first prospective study designed to evaluate the influence of infarct-related inflammatory response on several major determinants of post-infarction outcomes, such as coronary plaque vulnerability, myocardial viability, and ventricular remodeling.},
	keywords = {Acute myocardial infarction; Ventricular Remodeling; Myocardial viability; Plaque vulnerability; inflammation status},
	year = {2019},
	eissn = {1536-5964},
	orcid-numbers = {Benedek, Imre-Sándor/0000-0003-0051-4047}
}

@article{MTMT:31056733,
	title = {Self-Tuning Possibilistic c-Means Clustering Models},
	url = {https://m2.mtmt.hu/api/publication/31056733},
	author = {Szilágyi, László and Lefkovits, Szidónia and Szilágyi, Sándor Miklós},
	doi = {10.1142/S0218488519400075},
	journal-iso = {INT J UNCERTAIN FUZZ},
	journal = {INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS},
	volume = {27},
	unique-id = {31056733},
	issn = {0218-4885},
	abstract = {The relaxation of the probabilistic constraint of the fuzzy c-means clustering model was proposed to provide robust algorithms that are insensitive to strong noise and outlier data. These goals were achieved by the possibilistic c-means (PCM) algorithm, but these advantages came together with a sensitivity to cluster prototype initialization. According to the original recommendations, the probabilistic fuzzy c-means (FCM) algorithm should be applied to establish the cluster initialization and possibilistic penalty terms for PCM. However, when FCM fails to provide valid cluster prototypes due to the presence of noise, PCM has no chance to recover and produce a fine partition. This paper proposes a two-stage c-means clustering algorithm to tackle with most problems enumerated above. In the first stage called initialization, FCM with two modifications is performed: (1) extra cluster added for noisy data; (2) extra variable and constraint added to handle clusters of various diameters. In the second stage, a modified PCM algorithm is carried out, which also contains the cluster width tuning mechanism based on which it adaptively updates the possibilistic penalty terms. The proposed algorithm has less parameters than PCM when the number of clusters is c > 2. Numerical evaluation involving synthetic and standard test data sets proved the advantages of the proposed clustering model.},
	keywords = {Clustering; c-means clustering; probabilistic partition; possibilistic partition; self-tuning algorithms; parameter reduction},
	year = {2019},
	eissn = {1793-6411},
	pages = {142-158},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111}
}

@inproceedings{MTMT:31121339,
	title = {Evolving Computationally Efficient Hashing for Similarity Search},
	url = {https://m2.mtmt.hu/api/publication/31121339},
	author = {Iclanzan, Dávid András and Szilágyi, Sándor Miklós and Szilágyi, László},
	booktitle = {Neural Information Processing},
	doi = {10.1007/978-3-030-04179-3_49},
	unique-id = {31121339},
	year = {2018},
	pages = {552-563}
}

@article{MTMT:30332375,
	title = {CoopRA Algorithm for Universal Characterization of the Experimental Evaluation Results of Cooperative Multiagent Systems},
	url = {https://m2.mtmt.hu/api/publication/30332375},
	author = {Iantovics, László Barna and Muaz, A. Niazi and Adrian, Gligor and Szilágyi, Sándor Miklós and Matthias, Dehmer and Frank, Emmert-Streib and Tokody, Dániel},
	journal-iso = {BRAIN BROAD RES ARTIF INTELL NEUROSCI},
	journal = {BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE},
	volume = {9},
	unique-id = {30332375},
	issn = {2067-3957},
	abstract = {Experimental evaluation of the cooperative multiagent systems (CMASs) provides an assessment way that should be analysed. In this paper, we propose an algorithm with acronym CoopRA that can make a deep performance characterization, based on different indicators, of the experimental evaluation results of a CMAS. This could lead to the formulation of helpful information in some decisions related to the performance of the studied CMASs. In order to validate the proposed algorithm, we performed a case study on a CMAS composed of simple reactive agents that operate by mimicking the problem/task solving of natural ants. We chose this type of cooperative multiagent system architecture, based on the fact that even in case of the cooperative multiagent systems composed of simple efficiently and flexibly cooperating agents could emerges an increased problem solving intelligence at the system’s level. The evaluation was performed for the Travelling Salesman Problem (TSP) solving that is a well-known NP-hard problem, having many real-life applications.},
	year = {2018},
	pages = {37-49},
	orcid-numbers = {Iantovics, László Barna/0000-0001-6254-9291; Tokody, Dániel/0000-0002-9984-0434}
}

@inproceedings{MTMT:3427617,
	title = {Automatic segmentation of low-grade brain tumor using a random forest classifier and Gabor features},
	url = {https://m2.mtmt.hu/api/publication/3427617},
	author = {Szabó, Zsófia and Kapás, Zoltán and Lefkovits, László and Győrfi, Ágnes and Szilágyi, Sándor Miklós and Szilágyi, László},
	booktitle = {14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2018)},
	unique-id = {3427617},
	abstract = {Computerized tumor detection and segmentation algorithms are developed to assist the work medical staff at the diagnosis or therapy planning. This paper presents a procedure trained to segment low-grade gliomas in multispectral volumetric MRI records. The proposed solution employs a random forest classifier based on 104 morphological and Gabor wavelet features. A neighborhood-based post-processing was designed to regularize the output of the classifier. The current version of our system was trained and tested using all 54 low-grade tumor volumes from the MICCAI BRATS 2016 database. The achieved accuracy is characterized by an overall mean Dice score of 83.8%, sensitivity >85%, and specificity >98%. The proposed method is likely to detect all gliomas of 2 cm diameter.},
	year = {2018},
	pages = {1106-1113}
}

@article{MTMT:3370526,
	title = {Review of Recent Trends in Measuring the Computing Systems Intelligence},
	url = {https://m2.mtmt.hu/api/publication/3370526},
	author = {Laszlo, Barna Iantovics and Adrian, Gligor and Muaz, A. Niazi and Anna, Iuliana Biro and Szilágyi, Sándor Miklós and Tokody, Dániel},
	journal-iso = {BRAIN BROAD RES ARTIF INTELL NEUROSCI},
	journal = {BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE},
	volume = {9},
	unique-id = {3370526},
	issn = {2067-3957},
	year = {2018},
	pages = {77-94},
	orcid-numbers = {Tokody, Dániel/0000-0002-9984-0434}
}

@inproceedings{MTMT:3344705,
	title = {Automatic brain tumor segmentation in multispectral MRI volumes using a random forest approach},
	url = {https://m2.mtmt.hu/api/publication/3344705},
	author = {Kapás, Zoltán and Lefkovits, László and Iclanzan, Dávid András and Győrfi, Ágnes and Iantovics, Barna László and Lefkovits, Szidónia and Szilágyi, Sándor Miklós and Szilágyi, László},
	booktitle = {Pacific Rim Symposium on Image and Video Technology (PSIVT 2017)},
	doi = {10.1007/978-3-319-75786-5_12},
	unique-id = {3344705},
	abstract = {The development of automatic tumor detection and segmentation 
		procedures enables the computers to preprocess huge sets of MRI 
		records and draw the attention of medical staff upon suspected 
		positive cases. This paper proposes a machine learning solution 
		based on binary decision trees and random forest technique, 
		trained to provide accurate segmentation of brain tumors from 
		multispectral MRI volumes. The current version of our system 
		was trained and tested using all 220 high-grade tumor volumes 
		from the MICCAI BRATS 2016 database. Image records were 
		preprocessed to attenuate the effect of relative intensities in 
		the MRI data, and to extend the feature set with neighborhood 
		information of each voxel. The output of the random forest is 
		also validated for each voxel, according to labels given to 
		neighbor voxels. The achieved accuracy is characterized by an 
		overall mean Dice score of 80.1%, sensitivity 83.1%, and 
		specificity 98.6%. The proposed method is likely to detect all 
		gliomas of 2 cm diameter.},
	year = {2018},
	pages = {137-149},
	orcid-numbers = {Lefkovits, Szidónia/0000-0002-7903-1111}
}

@inproceedings{MTMT:3344702,
	title = {A possibilistic c-means clustering model with cluster size estimation},
	url = {https://m2.mtmt.hu/api/publication/3344702},
	author = {Szilágyi, László and Szilágyi, Sándor Miklós},
	booktitle = {Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications},
	unique-id = {3344702},
	abstract = {Most c-means clustering models have serious difficulties when 
		facing clusters of different sizes and severely outlier data. 
		The possibilistic c-means (PCM) algorithm can handle both 
		problems to some extent. However, its recommended 
		initialization using a terminal partition produced by the 
		probabilistic fuzzy c-means does not work when severe outliers 
		are present. This paper proposes a possibilistic c-means 
		clustering model that uses only two parameters independently of 
		the number of clusters, which is able to correctly handle the 
		above mentioned obstacles. Numerical evaluation involving 
		synthetic and standard test data sets prove the advantages of 
		the proposed clustering model.},
	year = {2018},
	pages = {661-668}
}

@article{MTMT:31965200,
	title = {Inference of Evolutionary Jumps in Large Phylogenies using Lévy Processes},
	url = {https://m2.mtmt.hu/api/publication/31965200},
	author = {Duchen, Pablo and Leuenberger, Christoph and Szilágyi, Sándor Miklós and Harmon, Luke and Eastman, Jonathan and Schweizer, Manuel and Wegmann, Daniel},
	doi = {10.1093/sysbio/syx028},
	journal-iso = {SYSTEMATIC BIOL},
	journal = {SYSTEMATIC BIOLOGY},
	volume = {66},
	unique-id = {31965200},
	issn = {1063-5157},
	year = {2017},
	eissn = {1076-836X},
	pages = {950-963}
}