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Aristidis G. Vrahatis
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2020 – today
- 2024
- [j20]Aristidis G. Vrahatis
, Konstantinos Lazaros, Sotiris Kotsiantis
Graph Attention Networks: A Comprehensive Review of Methods and Applications. Future Internet 16(9): 318 (2024) - [j19]Konstantinos Lazaros, Dimitris E. Koumadorakis, Aristidis G. Vrahatis, Sotiris Kotsiantis:
A comprehensive review on zero-shot-learning techniques. Intell. Decis. Technol. 18(2): 1001-1028 (2024) - [j18]Gerasimos Grammenos, Aristidis G. Vrahatis
Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using an Explainable AI Approach. Inf. 15(5): 249 (2024) - [j17]Konstantinos Lazaros, Dimitris E. Koumadorakis
Graph neural network approaches for single-cell data: a recent overview. Neural Comput. Appl. 36(17): 9963-9987 (2024) - [j16]Emmanouil D. Oikonomou
How natural language processing derived techniques are used on biological data: a systematic review. Netw. Model. Anal. Health Informatics Bioinform. 13(1): 23 (2024) - [c32]Konstantinos Lazaros, Themis P. Exarchos, Ilias Maglogiannis, Panagiotis Vlamos, Aristidis G. Vrahatis:
Advancing ScRNA-Seq Data Integration via a Novel Gene Selection Method. AIAI (1) 2024: 31-41 - [c31]Andreas Avgoustis
Optimization of Healthcare Process Management Using Machine Learning. AIAI (1) 2024: 187-200 - [c30]Dimitris E. Koumadorakis, Georgios N. Dimitrakopoulos, Themis P. Exarchos, Panagiotis Vlamos, Aristidis G. Vrahatis:
Integrating Machine Learning and Biological Context for Single-Cell Gene Regulatory Network Inference. AIAI Workshops 2024: 250-260 - 2023
- [j15]Marios G. Krokidis, Georgios N. Dimitrakopoulos, Aristidis G. Vrahatis, Themis P. Exarchos, Panagiotis Vlamos:
Challenges and limitations in computational prediction of protein misfolding in neurodegenerative diseases. Frontiers Comput. Neurosci. 17 (2023) - [j14]Spyridon Doukakis, Aristidis G. Vrahatis, Themis P. Exarchos, Maria Hadjinicolaou, Panagiotis Vlamos, Chrystalla Mouza:
Design, Implementation, and Evaluation of Online Bioinformatics and Neuroinformatics Labs. Int. J. Online Biomed. Eng. 19(1): 21-37 (2023) - [j13]Konstantinos Lazaros
Methods for cell-type annotation on scRNA-seq data: A recent overview. J. Bioinform. Comput. Biol. 21(5): 2340002:1-2340002:34 (2023) - [j12]Aristidis G. Vrahatis, Konstantina Skolariki, Marios G. Krokidis, Konstantinos Lazaros, Themis P. Exarchos, Panagiotis Vlamos:
Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors 23(9): 4184 (2023) - [c29]Pantelis Papageorgiou, Aigli Korfiati, Aristotelis Misios, Aristidis G. Vrahatis, Alexandros A. Pittis, Bogdan Tanasa, Konstantinos Mavrommatis, Yilin Zhao, Tao Yang, Vassilis Pitsikalis:
pCCI: A machine learning patient-centric cell-cell interactions framework for cancer subtype classification. BIBM 2023: 910-913 - [c28]Georgios N. Dimitrakopoulos, Konstantinos Lazaros, Marios G. Krokidis, Themis P. Exarchos, Aristidis G. Vrahatis, Panagiotis Vlamos:
A Graph-Based Approach to Integrate Large-Scale Drug and Protein Data for Alzheimer's Disease Drug Repurposing. IEEE Big Data 2023: 4584-4587 - [c27]Marios G. Krokidis, Georgios N. Dimitrakopoulos, Themis P. Exarchos, Aristidis G. Vrahatis, Panagiotis Vlamos:
Advanced Big Data Analysis for Deciphering the Role of Protein Misfolding and Interactions in the Pathogenesis of Alzheimer's Disease. IEEE Big Data 2023: 4614-4617 - [c26]Aristidis G. Vrahatis, Konstantinos Lazaros, Petros Paplomatas
Applying SCALEX scRNA-Seq Data Integration for Precise Alzheimer's Disease Biomarker Discovery. AIAI Workshops 2023: 294-302 - [i4]Konstantinos Lazaros, Dimitris E. Koumadorakis
Graph Neural Network approaches for single-cell data: A recent overview. CoRR abs/2310.09561 (2023) - 2022
- [j11]Petros Barmpas
A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project. Health Inf. Sci. Syst. 10(1): 6 (2022) - [j10]Marios G. Krokidis, Georgios N. Dimitrakopoulos
A Sensor-Based Perspective in Early-Stage Parkinson's Disease: Current State and the Need for Machine Learning Processes. Sensors 22(2): 409 (2022) - [c25]Konstantinos Lazaros, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos:
Feature Selection For High Dimensional Data Using Supervised Machine Learning Techniques. IEEE Big Data 2022: 3891-3894 - [c24]Spiros V. Georgakopoulos
Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring. IJCNN 2022: 1-9 - [c23]Eugenia Papadaki, Themis P. Exarchos, Panagiotis Vlamos, Aristidis G. Vrahatis:
A Hybrid Deep Learning model for predicting the early Alzheimer's Disease stages using MRI. SETN 2022: 38:1-38:6 - 2021
- [j9]Panagiotis Anagnostou
Enhancing the Human Health Status Prediction: The ATHLOS Project. Appl. Artif. Intell. 35(11): 834-856 (2021) - [c22]Petros T. Barbas, Aristidis G. Vrahatis, Sotiris K. Tasoulis:
RLAC: Random Line Approximation Clustering. IEEE BigData 2021: 985-993 - [c21]Marios G. Krokidis, Georgios N. Dimitrakopoulos, Aristidis G. Vrahatis, Themis P. Exarchos, Panagiotis Vlamos:
Recent Dimensionality Reduction Techniques for Visualizing High-Dimensional Parkinson's Disease Omics Data. IEEE BigData 2021: 4460-4463 - [c20]Ioannis L. Dallas, Aristidis G. Vrahatis, Sotiris K. Tasoulis, Vassilis P. Plagianakos:
Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data. CIBB 2021: 227-241 - [c19]Konstantinos I. Chatzilygeroudis, Aristidis G. Vrahatis, Sotiris K. Tasoulis, Michael N. Vrahatis:
Feature Selection in Single-Cell RNA-seq Data via a Genetic Algorithm. LION 2021: 66-79 - 2020
- [j8]Aristidis G. Vrahatis, Sotiris K. Tasoulis, Spiros V. Georgakopoulos
Ensemble Classification through Random Projections for Single-Cell RNA-Seq Data. Inf. 11(11): 502 (2020) - [j7]Spiros V. Georgakopoulos
Change detection and convolution neural networks for fall recognition. Neural Comput. Appl. 32(23): 17245-17258 (2020) - [c18]Panagiotis Anagnostou
Approximate kNN Classification for Biomedical Data. IEEE BigData 2020: 3602-3607 - [c17]Aristidis G. Vrahatis, Panagiotis Vlamos
Pathway Analysis for unraveling Complex Diseases: Current State and Future Perpectives. SEEDA-CECNSM 2020: 1-8 - [c16]Aristidis G. Vrahatis, Panagiotis Vlamos
Network Biomarkers for Alzheimer's Disease via a Graph-based Approach. SEEDA-CECNSM 2020: 1-7 - [p1]Aristidis G. Vrahatis, Sotiris K. Tasoulis, Ilias Maglogiannis
Recent Machine Learning Approaches for Single-Cell RNA-seq Data Analysis. Advanced Computational Intelligence in Healthcare (7) 2020: 65-79 - [i3]Panagiotis Anagnostou, Petros T. Barbas, Aristidis G. Vrahatis, Sotiris K. Tasoulis:
Approximate kNN Classification for Biomedical Data. CoRR abs/2012.02149 (2020)
2010 – 2019
- 2019
- [j6]Andrei Dragomir
A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework. IEEE J. Biomed. Health Informatics 23(1): 14-25 (2019) - [c15]Aristidis G. Vrahatis, Georgios N. Dimitrakopoulos, Sotiris K. Tasoulis, Vassilis P. Plagianakos:
Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces. BIBE 2019: 846-849 - [c14]Aristidis G. Vrahatis, Georgios N. Dimitrakopoulos, Sotiris K. Tasoulis, Spiros V. Georgakopoulos
Single-cell regulatory network inference and clustering from high-dimensional sequencing data. IEEE BigData 2019: 2782-2789 - [c13]Aristidis G. Vrahatis, Georgios N. Dimitrakopoulos
A single-cell Systems Biology approach for disease-specific subpathway extraction. CIBCB 2019: 1-7 - [c12]Aristidis G. Vrahatis, Sotiris K. Tasoulis, Georgios N. Dimitrakopoulos
Visualizing High-Dimensional Single-Cell RNA-seq Data via Random Projections and Geodesic Distances. CIBCB 2019: 1-6 - [c11]Sotiris K. Tasoulis, Georgios I. Mallis, Spiros V. Georgakopoulos
Deep Learning and Change Detection for Fall Recognition. EANN 2019: 262-273 - [c10]Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos:
Convolutional Neural Networks for Twitter Text Toxicity Analysis. INNSBDDL 2019: 370-379 - 2018
- [c9]Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos
Biomedical Data Ensemble Classification using Random Projections. IEEE BigData 2018: 166-172 - [c8]Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos
Visualizing High-dimensional single-cell RNA-sequencing data through multiple Random Projections. IEEE BigData 2018: 5448-5450 - [c7]Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos
Real Time Sentiment Change Detection of Twitter Data Streams. INISTA 2018: 1-6 - [c6]Spiros V. Georgakopoulos
Convolutional Neural Networks for Toxic Comment Classification. SETN 2018: 35:1-35:6 - [c5]Georgios N. Dimitrakopoulos
Pathway analysis using XGBoost classification in Biomedical Data. SETN 2018: 46:1-46:6 - [i2]Spiros V. Georgakopoulos, Sotiris K. Tasoulis, Aristidis G. Vrahatis, Vassilis P. Plagianakos:
Convolutional Neural Networks for Toxic Comment Classification. CoRR abs/1802.09957 (2018) - [i1]Sotiris K. Tasoulis, Aristidis G. Vrahatis, Spiros V. Georgakopoulos, Vassilis P. Plagianakos:
Real Time Sentiment Change Detection of Twitter Data Streams. CoRR abs/1804.00482 (2018) - 2017
- [j5]Aristidis G. Vrahatis
Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection. Comput. 5(2): 20 (2017) - [j4]Georgios Drakopoulos
Tensor-Based Semantically-Aware Topic Clustering of Biomedical Documents. Comput. 5(3): 34 (2017) - [c4]Georgios N. Dimitrakopoulos
Driving Mental Fatigue Classification Based on Brain Functional Connectivity. EANN 2017: 465-474 - 2016
- [j3]Aristidis G. Vrahatis, Konstantina Dimitrakopoulou
CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis. Bioinform. 32(6): 884-892 (2016) - [j2]Aristidis G. Vrahatis, Panos Balomenos, Athanasios K. Tsakalidis, Anastasios Bezerianos:
DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments. Bioinform. 32(24): 3844-3846 (2016) - [c3]Georgios N. Dimitrakopoulos
Identifying disease network perturbations through regression on gene expression and pathway topology analysis. EMBC 2016: 5969-5972 - 2015
- [c2]Aristidis G. Vrahatis, Georgios N. Dimitrakopoulos
Identifying miRNA-mediated signaling subpathways by integrating paired miRNA/mRNA expression data with pathway topology. EMBC 2015: 3997-4000 - [c1]Georgios N. Dimitrakopoulos
Age-related subpathway detection through meta-analysis of multiple gene expression datasets. DSP 2015: 539-542 - 2013
- [j1]Konstantina Dimitrakopoulou
OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection. Comput. Methods Programs Biomed. 111(3): 650-661 (2013)
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last updated on 2024-10-23 21:23 CEST by the dblp team
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