Training School: Mastering Text-Mining and OMICS Tools
Using text mining tools for the thorough analysis of myeloid cell diseases publications
Graphic: © Mye-InfoBank
WG 2 members have been asked to upload a selection of their own papers and published papers by other groups that reflect the state-of-the art and their own interest in myeloid cells and diseases associated with chronic inflammation (infection, inflammation, cancer). These papers, plus additional papers to be added via related articles feature of PubMed (by the trainers), will serve as our data base. The original collection is about 600 articles and the expanded one is estimated to be around 6000 articles. In addition, genes will be extracted from the two aforementioned collection of articles and will serve as a dataset to be analyzed with different text mining and gene enrichment tools. Finally, a RNA seq data set from GEO database, related to the scientific interests of the COST Action that is expected to be produced by WG 1 will provide another dataset for auto ML prediction and further validation of novel biomarkers.
The main tools that will be demonstrated are UniReD, BioTextQuest, FLAME, Darling, and Arena 3D.
Hosts: Concepción Marañón Lizana and Jordi Muntane Relat
Trainers: Ioannis Iliopoulos, Georgios Pavlopoulos, Theodosios Theodosiou, Ismini Baltsavia and Costas Bouyioukos
Location: Centro Pfizer – Junta de Andalucía de Genómica e Investigación Oncológica (GENYO), P. T. Ciencias de la Salud, Avda. de la Ilustración 114, 18016 Granada, Spain (Map)
Contact: Annika Bruger (firstname.lastname@example.org)