Fosscomm 2022

An interactive web app for the identification of robust genes-level biomarkers for complex diseases.
2022-11-19, 13:00–13:30 (Europe/Athens), Room I

The rapid technological developments of the last two decades in biomedical field have led the scientific community to achieve biological and medical discoveries at an ever-increasing pace. The emerging computational web app tools for biomedical data mining offer not only a platform to explore systematically the complexity of a particular disease, leading to the biomarker’s identification of complex human diseases, but also a user-friendly and simple platform for both the modeler and experimentalist. Towards this, we present an open-source RShiny interactive web app which offers a flexible platform for the identification of dominant genes in a single-cell RNA-sequencing dataset which operate as disease biomarkers. It contains an extensive and customized framework with a broad range of operation modes at all stages of feature selection analysis, enabling so a case-specific approach. The operation modes also include an enrichment analysis of the exported dominant genes.

We developed a biomarker application that uses multiple feature selection methods in order to uncover possible biomarkers through in silico cell RNA sequencing. It is an open and free software which offers a flexible platform for the identification of dominant genes in a single-cell RNA-sequencing (scRNA-seq) dataset which operates as disease biomarkers. It includes three different types of gene selection methods, exploiting the statistical aspect, the machine-leaning aspect along with state-of-the-art feature selection methods tailored for scRNA-seq data. The feature selection operation modes include 16 different methodologies, covering a broad range of such approaches. The extracted gene list is further examined for enrichment in various biological and pharmacological features including (i) pathway terms, (ii) gene ontology (GO) terms of molecular function, biological processes, and cellular components, (iii) disease terms, (iv) drug substances based on the EnrichR tool. Snapshot of KEGG pathway maps are exported in png format by highlighting the exported genes biomarkers.

(Online Talk)

Petros Paplomatas was born in Katerini, Greece in 1987. He received his diploma in 2011 from the Biomedical Sciences Department of the School of Health Sciences of the International Hellenic University-IHU. He received his M.Sc. diploma in Biomedical and Molecular Sciences in the Diagnosis and Treatment of Diseases in 2021 from the Department of Medicine at Democritus University of Thrace. He received his second Master of Science in Bioinformatics and Neuroinformatics in 2023 from the Hellenic Open University. He is now a Ph.D. candidate at Ionian University's department of informatics. His research interests are in the fields of bioinformatics and systems biology. His research focuses on developing novel artificial intelligence or feature selection algorithms for the identification of potential biomarkers for early diagnosis and the development of web interactive applications for this purpose.