The biological sciences are quickly becoming data-centered sciences and, as a result, they are, more than ever, one of the key targets for data scientists, particularly from the viewpoints of Pattern Recognition (PR) and Machine Learning (ML). 

Nowhere else this is more evident than in the –omics sciences, spanning the fields of genomics, proteomics, metabolomics and transcriptomics. In all of these, the advances in data acquisition are creating a true deluge of information and, with it, new challenging scenarios related to data management in all forms, compounded by privacy and legal issues.
In this scenario, PR and ML methods hold the promise of opening new avenues for transforming raw and unstructured –omics information into usable knowledge for the biomedical and bioinformatics domains.

•    Applications of PR and ML methods to problems in the –omics sciences (including genomics, proteomics, metabolomics and transcriptomics).
•    Development of novel PR and ML methods suitable for application to –omics problems.
•    PR and ML-based methods for decision support in the –omics sciences.
•    PR and ML for data processing, preparation and transformation in -omics.
•    Scalable PR and ML models for Big Data problems in –omics, including online and stream learning, as well as parallel and high-performance computing approaches.

Alfredo Vellido, PhD. Dept. of Computer Science, Universitat Politécnica de Catalunya, BarcelonaTECH (UPC), Barcelona, Spain.

Jesús Giraldo, PhD. Institut de Neurociències and Unitat de Bioestadística, Universitat Autònoma de Barcelona (UAB), Cerdanyola del Vallès, Barcelona, Spain

René Alquézar, PhD. Dept. of Computer Science, Universitat Politécnica de Catalunya, BarcelonaTECH (UPC), Barcelona, Spain.