REMOD: A Tool for Analyzing and Remodeling the Dendritic Architecture of Neural Cells
Bozelos, P., Stefanou, S.S., Bouloukakis, G., Melachrinos, C., Poirazi, P.
Front. Neuroanat., 06 January 2016 | doi: http://dx.doi.org/10.3389/fnana.2015.00156

A. Oulas, N. Karathanasis, A. Louloupi, G. A. Pavlopoulos, P. Poirazi, K. Kalantidis, I. Iliopoulos
Prediction of miRNA Targets. In: Picardi E. (eds) RNA Bioinformatics: Methods in Molecular Biology, vol. 1269, pp. 207-229. Humana Press, New York, NY (2015). doi: https://doi.org/10.1007/978-1-4939-2291-8_13

Emerging applications of read profiles towards the functional annotation of the genome
Pundhir, S., Poirazi, P., Gorodkin, J.
Front Genet. 2015 May 19;6:188. doi: 10.3389/fgene.2015.00188. eCollection 2015.

A Simple Method to Simultaneously Detect and Identify Spikes from Raw Extracellular Recordings
Petrantonakis, P.C., and Poirazi, P.
Front. Neurosci., 02 December 2015 | doi: https://doi.org/10.3389/fnins.2015.00452

MiRduplexSVM: A High-Performing MiRNA-Duplex Prediction and Evaluation Methodology.
Karathanasis, N., Tsamardinos, I., Poirazi, P.
PLoS One. 2015 May 11;10(5):e0126151. doi: 10.1371/journal.pone.0126151

GluA2 mRNA distribution and regulation by miR-124 in hippocampal neurons
Ho, V.M., Dallalzadeh, L.O., Karathanasis, N., Keles, M.F., Vangala, S., Grogan, T., Poirazi, P., Martin, K.C.
Mol Cell Neurosci. 2014 Jul;61:1-12. doi: 10.1016/j.mcn.2014.04.006.

Don’t use a cannon to kill the … miRNA mosquito.
Karathanasis, N., Tsamardinos, I., Poirazi, P.
Bioinformatics. 2014 Apr 1;30(7):1047-8. doi: 10.1093/bioinformatics/btu100.

Improved grading and survival prediction of human astrocytic brain tumors by artificial neural network analysis of gene expression microarray data
Petalidis, L.P., Oulas, A., Backlund, M., Wayland, M.T., Liu, L., Plant, K., Happerfield, L., Freeman, T.C., Poirazi, P., Collins, V.P.
Mol Cancer Ther. 2008 May;7(5):1013-24. doi: 10.1158/1535-7163.MCT-07-0177

Modeling regulatory cascades using Artificial Neural Networks: the case of transcriptional regulatory networks shaped during the yeast stress response
Manioudaki, M.E., Poirazi, P.
Front Genet. 2013 Jun 20;4:110. doi: 10.3389/fgene.2013.00110

SVM-Based miRNA:miRNA* Duplex Prediction
Karathanassis N., Armen A., Tsamardinos I., Poirazi P.
IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE), Larnaca Cyprus, 11-13 November, 2012

A new microRNA target prediction tool identifies a novel interaction of a putative miRNA with CCND2
Oulas, A., Karathanasis, N., Louloupi, A., Iliopoulos, I., Kalantidis, K., Poirazi, P..
RNA Biol. 2012 Sep;9(9):1196-207. doi: 10.4161/rna.21725

A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities
Tzamali, E., Poirazi, P., Tollis, I.G., Reczko, M.
BMC Syst Biol. 2011 Oct 18;5:167. doi: 10.1186/1752-0509-5-167

Finding cancer-associated miRNAs: methods and tools
Oulas, A., Karathanasis, N., Louloupi, A., Poirazi, P.
Mol Biotechnol. 2011 Sep;49(1):97-107. doi: 10.1007/s12033-011-9416-4

MatureBayes: a probabilistic algorithm for identifying the mature miRNA within novel precursors
Gkirtzou, K., Tsamardinos, I., Tsakalides, P., Poirazi, P.
PLoS One. 2010 Aug 6;5(8):e11843. doi: 10.1371/journal.pone.0011843

Oulas A., and Poirazi P.
Utilization of SSCprofiler to Predict a New miRNA Gene. In: Wu W. (eds) MicroRNA and Cancer. Methods in Molecular Biology (Methods and Protocols), vol. 676, pp. 243-252. Humana Press, Totowa, NJ (2011). doi: https://doi.org/10.1007/978-1-60761-863-8_17 

Oulas A., Karathanasis, N. and Poirazi P.
Computational identification of miRNAs involved in cancer. In: Wu W. (eds.) MicroRNAs and Cancer. Methods in Molecular Biology (Methods and Protocols), vol. 676, pp. 23-41. Humana Press, Totowa, NJ (2011). doi: https://doi.org/10.1007/978-1-60761-863-8_2

Manioudaki M., Tzamali E., Reczko M. and Poirazi P.
Methods for structural inference and functional module identification in intracellular networks. In: Krawetz S. (eds) Bioinformatics for Systems Biology, pp. 517-539. Humana Press  (2009). doi: https://doi.org/10.1007/978-1-59745-440-7_27

Tzamali E., Poirazi, P. and Reczko M.
Dynamical modeling of gene and metabolic networks. In: Krawetz S. (eds) Bioinformatics for Systems Biology. Humana Press (2009). doi: https://doi.org/10.1007/978-1-59745-440-7_28

Prediction of novel microRNA genes in cancer-associated genomic regions–a combined computational and experimental approach
Oulas, A., Boutla, A., Gkirtzou, K., Reczko, M., Kalantidis, K., Poirazi, P.
Nucleic Acids Res. 2009 Jun;37(10):3276-87. doi: 10.1093/nar/gkp120

MicroRNAs and Cancer-the search begins!
Oulas, A., Reczko, M., Poirazi, P.
IEEE Trans Inf Technol Biomed. 2009 Jan;13(1):67-77. doi: 10.1109/TITB.2008.2007086

Use of artificial neural networks and a gamma-concept-based approach to model growth of and bacteriocin production by Streptococcus macedonicus ACA-DC 198 under conditions simulating Kasseri cheese technology.
Poirazi, P., Leroy, F., Georgalaki, M.D., Aktypis, A., De Vuyst, L., Tsakalidou, E.
Appl Environ Microbiol. 2007 Feb;73(3):768-76. doi: https://doi.org/10.1128/aem.01721-06

Individualized markers optimize class prediction of microarray data
Pavlidis, P., Poirazi, P.
BMC Bioinformatics. 2006 Jul 14;7:345. DOI: 10.1186/1471-2105-7-345