ResearchAI for biosignals & omics
Bioinformatics
Tools that move from high-throughput molecular data to insight into cellular mechanisms.
High-throughput experiments measure thousands of genes at once, and their interpretation requires both computing power and biological knowledge. We develop tools that help researchers move from raw molecular data to insight into cellular mechanisms.
Our work includes GOrevenge, a reverse-engineering method that uses the Gene Ontology to identify critical molecular players; the GRISSOM and HECTOR platforms for distributed processing of microarray data on grid infrastructure; KEGG Converter for modelling metabolic networks; and, more recently, deep learning for non-coding RNA classification.
- Gene Ontology–driven network analysis
- Grid platforms for microarray processing
- Deep learning for omics data
Related publications
Selected work from the lab in this area.
NCC—An Efficient Deep Learning Architecture for Non-Coding RNA Classification
Technologies, 13(5), p. 196.
Advancing ScRNA-Seq Data Integration via a Novel Gene Selection Method
In Artificial Intelligence Applications and Innovations. Springer Nature Switzerland, pp. 31–41.
Recent Machine Learning Approaches for Single-Cell RNA-seq Data Analysis
In Advanced Computational Intelligence in Healthcare-7. Springer Berlin Heidelberg, pp. 65–79.
ANTISOMA: A Computational Pipeline for the Reduction of the Aggregation Propensity of Monoclonal Antibodies
In GeNeDis 2018. Springer International Publishing, pp. 359–371.
An RNA-seq analysis from non-small cell lung cancer biopsies suggests an important role for aberrant alternative splicing in its pathophysiology
Health and Technology, 7(1), pp. 133–140.
Exploring the Molecular Determinants of Tumor-Stroma Interaction in Non-small Cell Lung Cancer Through the Utilization of RNA-seq Data from Lung Biopsies
In XIV Mediterranean Conference on Medical and Biological Engineering and Computing 2016. Springer International Publishing, pp. 495–499.
Intelligent identification of biomarkers for the study of obstructive nephropathy
Intelligent Decision Technologies, 7(1), pp. 11–22.
Intelligent Selection of Human miRNAs and Mouse mRNAs Related to Obstructive Nephropathy
In Artificial Intelligence Applications and Innovations. Springer Berlin Heidelberg, pp. 464–471.
Unifying the integration, analysis and interpretation of multi-omic datasets: Exploration of the disease networks of Obstructive Nephropathy in children
In 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp. 3716–3719.
GOrevenge: A Novel Generic Reverse Engineering Method for the Identification of Critical Molecular Players, Through the Use of Ontologies
IEEE Transactions on Biomedical Engineering, 58(12), pp. 3522–3527.
GRISSOM Platform: Enabling Distributed Processing and Management of Biological Data Through Fusion of Grid and Web Technologies
IEEE Transactions on Information Technology in Biomedicine, 15(1), pp. 83–92.
Applying Gap Statistic for Automated Unsupervised Estimation of Optimal Number of Clusters in Microarray Datasets
In Proceedings of the 7th International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2010). Palermo, Italy, 16–18 September.
Analysis of pediatric obstructive nephropathy using protein antibody arrays and computational techniques
In Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine. IEEE, pp. 1–5.
Delineation and interpretation of gene networks towards their effect in cellular physiology- A reverse engineering approach for the identification of critical molecular players, through the use of ontologies
In 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology. IEEE, pp. 6709–6712.
Enabling distributed Processing and Management of biological Data using the Grid and Web Technologies
In Healthgrid Applications and Core Technologies. IOS Press.
Using Grid Infrastructure for the Promotion of Biomedical Knowledge Mining
In XII Mediterranean Conference on Medical and Biological Engineering and Computing 2010. Springer Berlin Heidelberg, pp. 438–441.
KEGGconverter: a tool for the in-silico modelling of metabolic networks of the KEGG Pathways database
BMC Bioinformatics, 10(1).
HECTOR: Enabling Microarray Experiments over the Hellenic Grid Infrastructure
Journal of Grid Computing, 7(3), pp. 395–416.