Publications

§ International refereed journals (J)

J1. Krokidis, M. G., Exarchos, T. P., Vrahatis, A. G., Tzouvelekis, C., Drakoulis, D., Papavassileiou, F., & Vlamos, P. (2022). A Sensor-Based Platform for Early-Stage Parkinson’s Disease Monitoring. Advances in Experimental Medicine and Biology, 1424:23-29, DOI: 10.1007/978-3-031-31982-2_2

J2. Dimakopoulos, G. A., Vrahatis, A. G., Exarchos, T. P., Ntanasi, E., Yannakoulia, M., Kosmidis, M. H., ... & Vlamos, P. (2022). Application of Machine Learning Techniques in the HELIAD Study Data for the Development of Diagnostic Models in MCI and Dementia. Advances in Experimental Medicine and Biology, 1424:187-192, DOI: 10.1007/978-3-031-31982-2_20

J3.     Papikinos, T., Krokidis, M. G., Vrahatis, A., Vlamos, P., & Exarchos, T. P. (2022). Signature-Based Computational Drug Repurposing for Amyotrophic Lateral Sclerosis. Advances in Experimental Medicine and Biology, 1424:201-211
DOI: 10.1007/978-3-031-31982-2_22

J4. Paplomatas, P., Vlamos, P., & Vrahatis, A. G. (2022). A Comparison of the Various Methods for Selecting Features for Single-Cell RNA Sequencing Data in Alzheimer’s Disease. Advances in Experimental Medicine and Biology, 1424:241-246
DOI: 10.1007/978-3-031-31982-2_27 

J5. Cheirdaris, D., Krokidis, M. G., Kasti, M., Vrahatis, A. G., Exarchos, T., & Vlamos, P. (2022). Setting Up a Bio-AFM to Study Protein Misfolding in Neurodegenerative Diseases. Advances in Experimental Medicine and Biology, 1423:1-10, DOI: 10.1007/978-3-031-31978-5_1 

J6.     Krokidis, M. G., Exarchos, T. P., Avramouli, A., Vrahatis, A. G., & Vlamos, P. (2022). Computational and Functional Insights of Protein Misfolding in Neurodegeneration., Advances in Experimental Medicine and Biology, 1423:201-206, DOI: 10.1007/978-3-031-31978-5_18 

J7. Koumadorakis, D. E., Krokidis, M. G., Dimitrakopoulos, G. N., & Vrahatis, A. G. (2022). A Consensus Gene Regulatory Network for Neurodegenerative Diseases Using Single-Cell RNA-Seq Data. Advances in Experimental Medicine and Biology, 1423:215-224, DOI: 10.1007/978-3-031-31978-5_20

J8. Zoiros, A., & Vrahatis, A. (2022). Effective Preprocessing of Single-Cell RNA-Seq for Unravelling Alzheimer’s Disease Signatures. Advances in Experimental Medicine and Biology, 1423:251-256, DOI: 10.1007/978-3-031-31978-5_25 

J9. Aslanis, I., Krokidis, M. G., Dimitrakopoulos, G. N., & Vrahatis, A. G. (2023). Identifying Network Biomarkers for Alzheimer's Disease Using Single-Cell RNA Sequencing Data. Advances in Experimental Medicine and Biology, 1423, 207-214., DOI: 10.1007/978-3-031-31978-5_19

J10.  Barmpas, P., Tasoulis, S., Vrahatis, A. G., Anagnostou, P., Georgakopoulos, S., Prina, M., ... & Panagiotakos, D. (2022). Unsupervised Learning for Large Scale Data: The ATHLOS Project. In Statistical Modeling of Reliability Structures and Industrial Processes (pp. 55-76). CRC Press.

J11.  Vrahatis, A. G., Vlamos, P., Avramouli, A., Exarchos, T., & Gonidi, M. (2021). Emerging machine learning techniques for modelling cellular complex systems in Alzheimer’s disease. Advances in Experimental Medicine and Biology, 1338:199-208, DOI: 10.1007/978-3-030-78775-2_24.

J12.  Krokidis, M.G.; Dimitrakopoulos, G.N.; Vrahatis, A.G.; Tzouvelekis, C.; Drakoulis, D.; Papavassileiou, F.; Exarchos, T.P.; Vlamos, P. A Sensor-Based Perspective in Early-Stage Parkinson’s Disease: Current State and the Need for Machine Learning Processes. Sensors 2022, 22, 409. doi: 10.3390/s22020409

J13.  Constantinou, M.; Exarchos, T.; Vrahatis, A.G.; Vlamos, P. COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods. Int. J. Environ. Res. Public Health 2023, 20, 2035. doi: 10.3390/ijerph20032035

J14.  Paplomatas, P.; Krokidis, M.G.; Vlamos, P.; Vrahatis, A.G. An Ensemble Feature Selection Approach for Analysis and Modeling of Transcriptome Data in Alzheimer’s Disease. Appl. Sci. 2023, 13, 2353. doi: 10.3390/app13042353

J15.  Vrahatis, A.G.; Skolariki, K.; Krokidis, M.G.; Lazaros, K.; Exarchos, T.P.; Vlamos, P. Revolutionizing the Early Detection of Alzheimer’s Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning. Sensors 2023, 23, 4184. doi: 10.3390/s23094184

J16.  Krokidis, M.G.; Vrahatis, A.G.; Lazaros, K.; Vlamos, P. Exploring Promising Biomarkers for Alzheimer’s Disease through the Computational Analysis of Peripheral Blood Single-Cell RNA Sequencing Data. Appl. Sci. 2023, 13, 5553. doi: 10.3390/app13095553

J17.  Skolariki, K.; Vrahatis, A.G.; Krokidis, M.G.; Exarchos, T.P.; Vlamos, P. Assessing and Modelling of Post-Traumatic Stress Disorder Using Molecular and Functional Biomarkers. Biology 2023, 12, 1050. doi: 10.3390/biology12081050

J18.  Doukakis, S., Vrahatis, A. G., Exarchos, T., Hadjinicolaou, M., Vlamos, P., & Mouza, C. (2023). Design, Implementation, and Evaluation of Online Bioinformatics and Neuroinformatics Labs. International Journal of Online & Biomedical Engineering, 19(1).

J19.   Barmpas, P., Tasoulis, S., Vrahatis, A. G., Georgakopoulos, S. V., Anagnostou, P., Prina, M., ... & Panagiotakos, D. (2022). A divisive hierarchical clustering methodology for enhancing the ensemble prediction power in large scale population studies: the ATHLOS project. Health Information Science and Systems, 10(1), 1-14.

J20.   Vrahatis, A. G., Rapti, A., Sioutas, S., & Tsakalidis, A. (2017). PerSubs: A graph-based algorithm for the identification of perturbed subpathways caused by complex diseases. Advances in Experimental Medicine and Biology, 988:215-224, DOI: 10.1007/978-3-319-56246-9_17

J21.   Anagnostou, P., Tasoulis, S., Vrahatis, A. G., Georgakopoulos, S., Prina, M., Ayuso-Mateos, J. L., ... & Panagiotakos, D. (2021). Enhancing the human health status prediction: the athlos project. Applied Artificial Intelligence, 35(11), 834-856.

J22.  Vrahatis, A., Tasoulis, S., Georgakopoulos, S., & Plagianakos, V. (2020). Ensemble Classification through Random Projections for single-cell RNA-seq data. Information, 11(11), 502.

J23.   Vrahatis A.G., Vlamos P., Gonidi M., Avramouli A. (2020) Handling the cellular complex systems in Alzheimer’s disease through a graph mining approach, Advances in Experimental Medicine and Biology, 135-144, PMID: 34973018, DOI: 10.1007/978-3-030-78775-2_16

J24.  Georgakopoulos S., Tasoulis S., Mallis G., Vrahatis A., Plagianakos V. and Magglogiannis I., (2020) Change Detection and Convolution Neural Networks for Fall Recognition, Neural Computing and Applications, 1-14. https://doi.org/10.1007/s00521-020-05208-8. (PDF)

J25.  Vrahatis, A. G., Kotsireas, I. S., & Vlamos, P. (2020). A Systems Biology Approach for the Identification of Active Molecular Pathways During the Progression of Alzheimer’s Disease. Advances in Experimental Medicine and Biology, 1194:303-314. doi: 10.1007/978-3-030-32622-7_28. PMID: 32468546. (PDF)

J26.  Vrahatis, A. G., Kotsireas, I. S., & Vlamos, P. (2020). Detecting Common Pathways and Key Molecules of Neurodegenerative Diseases from the Topology of Molecular Networks. Advances in Experimental Medicine and Biology, 1194:409-421. doi: 10.1007/978-3-030-32622-7_38. (PDF)

J27.  Campbell-Tofte, J., Vrahatis, A., Josefsen, K., Mehlsen, J., & Winther, K. (2019). Investigating the aetiology of adverse events following HPV vaccination with systems vaccinology. Cellular and molecular life sciences, 76(1), 67-87. (PDF)

J28.  Dragomir, A., Vrahatis, A. G., & Bezerianos, A. (2018). A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework. IEEE journal of biomedical and health informatics, 23(1), 14-25. (PDF)

J29.  Drakopoulos, G., Kanavos, A., Karydis, I., Sioutas, S., & G Vrahatis, A. (2017). Tensor-based semantically-aware topic clustering of biomedical documents. Computation, 5(3), 34. (PDF)

J30.  Vrahatis, A. G., Dimitrakopoulou, K., Kanavos, A., Sioutas, S., & Tsakalidis, A. (2017). Detecting Perturbed Subpathways towards Mouse Lung Regeneration Following H1N1 Influenza Infection. Computation, 5(2), 20. (PDF)

J31.  Vrahatis, A. G., Dimitrakopoulou, K., Balomenos, P., Tsakalidis, A. K., & Bezerianos, A. (2016). CHRONOS: a time-varying method for microRNA-mediated subpathway enrichment analysis. Bioinformatics, 32(6), 884-892. (PDF) (SUPPLEMENTARY FILE) (R Bioconductor Package)

J32.  Vrahatis, A. G., Balomenos, P., Tsakalidis, A. K., & Bezerianos, A. (2016). DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq experiments. Bioinformatics, 32(24), 3844-3846. (PDF) (SUPPLEMENTARY FILE)(R Bioconductor Package)

J33.  Dimitrakopoulou, K., Vrahatis, A. G., & Bezerianos, A. (2015). Integromics network meta-analysis on cardiac aging offers robust multi-layer modular signatures and reveals micronome synergism. BMC genomics, 16(1), 147. (PDF) (MatLab Code)

J34.  Dimitrakopoulos, G., Vrahatis, A., Dimitrakopoulou, K., Tsakalidis, A., Sgarbas, K., & Bezerianos, A. (2013). Module-Based Cross-Tissue Pathway Identification in Aging. Transactions of Japanese Society for Medical and Biological Engineering, Volume 51, Issue Supplement https://doi.org/10.11239/jsmbe.51.R-170. (PDF)(Poster)

J35.  Dimitrakopoulou, K., Vrahatis, A. G., Wilk, E., Tsakalidis, A. K., & Bezerianos, A. (2013). OLYMPUS: An automated hybrid clustering method in time series gene expression. Case study: Host response after Influenza A (H1N1) infection. Computer methods and programs in biomedicine, 111(3), 650-661. (PDF)(MatLab Code)

§ Chapters in Books (B)

B1.        Gonidi, M., Kontara, N., Vrahatis, A. G., Exarchos, T. P., & Vlamos, P. (2023). Role of Buccal Cells in Neurodegeneration. Handbook of Computational Neurodegeneration, 245.

B2.        Vrahatis, A. G., & Vlamos, P. (2023). Detecting Active Molecular Subpathways Related to AlzheimerTs Disease: A Systems Biology Approach. Handbook of Computational Neurodegeneration, 91.

B3.        Koumadorakis, D. E., Dimitrakopoulos, G. N., Krokidis, M. G., & Vrahatis, A. G. (2023). Gene Regulatory Network Reconstruction Using Single-Cell RNA-Sequencing. Handbook of Computational Neurodegeneration, 181.

B4.        Vrahatis, A. G., Tasoulis, S. K., Maglogiannis, I., & Plagianakos, V. P. (2020). Recent Machine Learning Approaches for Single-Cell RNA-seq Data Analysis. In Advanced Computational Intelligence in Healthcare-7 (pp. 65-79). Springer, Berlin, Heidelberg. (PDF)

B5.        Vrahatis, A. G., & Vlamos, P. (2021). A Systems Biology Approach for Detecting Active Molecular Subpathways Related to Alzheimer’s Disease. In Handbook of Computational Neurodegeneration (pp. 1-19). Cham: Springer International Publishing.

B6.        Krokidis, M. G., Efraimidis, E., Cheirdaris, D., Vrahatis, A. G., & Exarchos, T. P. (2022). Protein Fold Recognition Exploited by Computational and Functional Approaches: Recent Insights. Handbook of Computational Neurodegeneration, 1-22.

 § Refereed conference proceedings (C)

C1.   Lazaros, K., Tasoulis, S., Vrahatis, A., & Plagianakos, V. (2022, December). Feature selection for high dimensional data using supervised machine learning techniques. In 2022 IEEE International Conference on Big Data (Big Data) (pp. 3891-3894). IEEE.

C2.   Dallas, I. L., Vrahatis, A. G., Tasoulis, S. K., & Plagianakos, V. P. (2021, November). Recent Dimensionality Reduction Techniques for High-Dimensional COVID-19 Data. In International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (pp. 227-241). Cham: Springer International Publishing.

C3.   Vrahatis, A. G., Lazaros, K., Paplomatas, P., Krokidis, M. G., Exarchos, T., & Vlamos, P. (2023, June). Applying SCALEX scRNA-Seq Data Integration for Precise Alzheimer’s Disease Biomarker Discovery. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 294-302). Cham: Springer Nature Switzerland.

C4.   Anagnostou, P., Barbas, P., Vrahatis, A. G., & Tasoulis, S. K. (2020, December). Approximate kNN Classification for Biomedical Data. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 3602-3607). IEEE.

C5.       Georgakopoulos, S. V., Tasoulis, S. K., Vrahatis, A. G., Moustakidis, S., Tsaopoulos, D. E., & Plagianakos, V. P. (2022, July). Deep Hybrid Learning for Anomaly Detection in Behavioral Monitoring. In 2022 International Joint Conference on Neural Networks (IJCNN) (pp. 1-9). IEEE.

C6.       Papadaki, E., Exarchos, T., Vlamos, P., & Vrahatis, A. (2022, September). A Hybrid Deep Learning model for predicting the early Alzheimer’s Disease stages using MRI. In Proceedings of the 12th Hellenic Conference on Artificial Intelligence (pp. 1-6).

C7.       Barbas, P., Vrahatis, A. G., & Tasoulis, S. K. (2021, December). RLAC: Random Line Approximation Clustering. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 985-993). IEEE.

C8.       Chatzilygeroudis, K. I., Vrahatis, A. G., Tasoulis, S. K., & Vrahatis, M. N. (2021, June). Feature Selection in Single-Cell RNA-seq Data via a Genetic Algorithm. In International Conference on Learning and Intelligent Optimization (pp. 66-79). Springer, Cham.

C9.       Krokidis, M. G., Dimitrakopoulos, G., Vrahatis, A. G., Exarchos, T. P., & Vlamos, P. (2021, December). Recent Dimensionality Reduction Techniques for Visualizing High-Dimensional Parkinson’s Disease Omics Data. In 2021 IEEE International Conference on Big Data (Big Data) (pp. 4460-4463). IEEE.

C10.    Anagnostou, P., Tasoulis, S., Vrahatis, A. G., Georgakopoulos, S., Prina, M., Ayuso-Mateos, J. L., ... & Panagiotakos, D. (2021). Enhancing the Human Health Status Prediction: The ATHLOS Project. Applied Artificial Intelligence, 35(11), 834-856.

C11. Vrahatis, A. G., Vlamos, P., Avramouli, A., Exarchos, T., & Gonidi, M. (2020, September). Pathway Analysis for unraveling Complex Diseases: Current State and Future Perpectives. In 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1-8). IEEE. (PDF)

C12. Vrahatis, A. G., Vlamos, P., Gonidi, M., Sagiadinou, M., & Avramouli, A. (2020, September). Network Biomarkers for Alzheimer’s Disease via a Graph-based Approach. In 2020 5th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM) (pp. 1-7). IEEE. (PDF)

C13. Vrahatis A., Tasoulis S., Dimitrakopoulos G. and Plagianakos V. (2019) Visualizing High-Dimensional Single-Cell RNA-seq Data via Random Projections and Geodesic Distances. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-6). IEEE (PDF)

C14. Vrahatis, A. G., Dimitrakopoulos, G. N., Tasoulis, S. K., & Plagianakos, V. P. (2019, July). A single-cell Systems Biology approach for disease-specific subpathway extraction. In 2019 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB) (pp. 1-7). IEEE (PDF)

C15. Vrahatis, A., Dimitrakopoulos, G., Tasoulis, S., & Plagianakos, V. (2019, October). Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces. In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE) (pp. 846-849). IEEE. (PDF)

C16. Tasoulis, S. K., Mallis, G. I., Georgakopoulos, S. V., Vrahatis, A. G., Plagianakos, V. P., & Maglogiannis, I. G. (2019, May). Deep Learning and Change Detection for Fall Recognition. In International Conference on Engineering Applications of Neural Networks (pp. 262-273). Springer, Cham. (PDF)

C17. Georgakopoulos, S. V., Tasoulis, S. K., Vrahatis, A. G., & Plagianakos, V. P. (2019, April). Convolutional Neural Networks for Twitter Text Toxicity Analysis. In INNS Big Data and Deep Learning conference (pp. 370-379). Springer, Cham. (PDF)

C18. Vrahatis, A. G., Dimitrakopoulos, G. N., Tasoulis, S. K., Georgakopoulos, S. V., & Plagianakos, V. P. (2019, December). Single-cell regulatory network inference and clustering from high-dimensional sequencing data. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 2782-2789). IEEE. (PDF)

C19. Dimitrakopoulos, G. N., Vrahatis, A. G., Plagianakos, V., & Sgarbas, K. (2018, July). Pathway analysis using XGBoost classification in Biomedical Data. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence (p. 46). ACM. (PDF)

C20. Georgakopoulos, S. V., Tasoulis, S. K., Vrahatis, A. G., & Plagianakos, V. P. (2018). Convolutional Neural Networks for Toxic Comment Classification. In Proceedings of the 10th Hellenic Conference on Artificial Intelligence. ACM. (PDF)

C21. Tasoulis, S. K., Vrahatis, A. G., Georgakopoulos, S. V., & Plagianakos, V. P. (2018). Real Time Sentiment Change Detection of Twitter Data Streams. 2018 Innovations in Intelligent Systems and Applications (INISTA), Thessaloniki, 2018, pp. 1-6, doi: 10.1109/INISTA.2018.8466326. (PDF)

C22. Tasoulis, S. K., Vrahatis, A. G., Georgakopoulos, S. V., & Plagianakos, V. P. (2018, December). Visualizing High-dimensional single-cell RNA-sequencing data through multiple Random Projections. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 5448-5450). IEEE. (PDF)

C23. Tasoulis, S. K., Vrahatis, A. G., Georgakopoulos, S. V., & Plagianakos, V. P. (2018, December). Biomedical Data Ensemble Classification using Random Projections. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 166-172). IEEE. (PDF)

C24. Dimitrakopoulos, G. N., Kakkos, I., Vrahatis, A. G., Sgarbas, K., Li, J., Sun, Y., & Bezerianos, A. (2017, August). Driving Mental Fatigue Classification Based on Brain Functional Connectivity. In International Conference on Engineering Applications of Neural Networks (pp. 465-474). Springer, Cham. (PDF)

C25. Dimitrakopoulos, G. N., Balomenos, P., Vrahatis, A. G., Sgarbas, K., & Bezerianos, A. (2016, August). Identifying disease network perturbations through regression on gene expression and pathway topology analysis. In Engineering in Medicine and Biology Society (EMBC), 2016 IEEE 38th Annual International Conference of the (pp. 5969-5972). IEEE. (PDF)

C26. Vrahatis, A. G., Dimitrakopoulos, G. N., Tsakalidis, A. K., & Bezerianos, A. (2015, August). Identifying miRNA-mediated signaling subpathways by integrating paired miRNA/mRNA expression data with pathway topology. In Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE (pp. 3997-4000). IEEE. (PDF)

C27. Dimitrakopoulos, G. N., Vrahatis, A. G., Balomenos, P., Sgarbas, K., & Bezerianos, A. (2015, July). Age-related subpathway detection through meta-analysis of multiple gene expression datasets. In Digital Signal Processing (DSP), 2015 IEEE International Conference on (pp. 539-542). IEEE. (PDF)

C28. Dimitrakopoulou, K., Vrahatis, A. G., Dimitrakopoulos, G. N., & Bezerianos, A. (2014). Aging Integromics: Module-Based Markers of Heart Aging from Multi-omics Data. In The 15th International Conference on Biomedical Engineering (pp. 104-107). Springer, Cham. (PDF)

C29. Vrahatis, A. G., Dimitrakopoulou, K., Dimitrakopoulos, G. N., Sgarbas, K. N., Tsakalidis, A. K., & Bezerianos, A. (2014). Network-based modular markers of aging across different tissues. In XIII Mediterranean Conference on Medical and Biological Engineering and Computing 2013 (pp. 1849-1852). Springer, Cham. (PDF)

§  Other conference publications

o   Abstract papers in International Conferences (A)

A1.        Aristidis G. Vrahatis, et al., Cardiac aging signatures in the form of network communities, Biology of Ageing Conference, Singapore, 22-24 October 2015. (Poster)

A2.        K. Dimitrakopoulou, Aristidis G. Vrahatis, A Bezerianos. Inferring systems-level cardiac aging biomarkers through integromics network analysis. Cardiovascular research 103, S12-S12 (PDF)

A3.        Georgios Dimitrakopoulos, Konstantina Dimitrakopoulou, Aristidis G. Vrahatis, Κyriakos Ν. Sgarbas, Anastasios Bezerianos. An integrative meta-analysis method to reveal age-related cross-tissue pathways. 27th International Mammalian Genome Conference, 15 -18 September 2013, Salamanca, Spain (Link)(Poster)

A4.        Aristidis G. Vrahatis, et al., SubPathTimer: a time-varying subpathway enrichment analysis method. ECCB'14 13th European Conference on Computational Biology. 2014, Strasbourg (Poster)

o   Panhellenic Conferences (P)

P1.        Aristidis G. Vrahatis, Sotiris Tasoulis and Vassilis Plagianakos, Visualization of single-cell RNA-seq data through k Nearest Neighbors search in Random Projected Spaces, The 14th conference of the hellenic society for computational biology and bioinformatics (HSCBB19), 2019, Patras, Greece

P2.        Aristidis G. Vrahatis, Andrei Dragomir and Anastasios Bezerianos, Subpathway approaches on the road to Network Medicine, 7th Panhellenic Conference on Biomedical Technology, 2017, Athens, Greece (PDF)

P3.        Aristidis G. Vrahatis, et al., Structural and functional communities of longevity-associated genes in signaling pathways, 6th Panhellenic Conference on Biomedical Technology, 2015, Athens, Greece (PDF)

P4.        Georgios N. Dimitrakopoulos, Aristidis G. Vrahatis, Kyriakos Sgarbas and Anastasios Bezerianos, Gene expression trend analysis in Aging, 6th Panhellenic Conference on Biomedical Technology, 2015, Athens, Greece (PDF)

o   Package Tutorials (T)

T1.        CHRONOS Tutorial: Vrahatis AG, Dimitrakopoulou K, Balomenos P.

T2.        DEsubs Tutorial: Vrahatis, A.G. and Balomenos, P.