Pattern Recognition for Bioinformatics and Digital Health

Pattern recognition has been offering essential tools for extracting meaningful information from biological and clinical data leading to frameworks that support clinical decision-making and research. In recent years, pattern recognition techniques are successfully applied to a wide variety of healthcare tasks, such as risk prediction, deciphering disease progression, patient subtyping, and medical text classification. Healthcare tasks pose numerous challenges for pattern recognition. The heterogeneous, high-dimensional, non-linear, temporal, and distributed nature of the patient and biological data complicate the traditional techniques. Such challenges inspire the pattern recognition domain to explore new ideas to solve specific challenges in the health domain.


Aim and Scope

We aim to pursue cutting-edge developments in pattern recognition for health. The scope includes but is not limited to

· Biomedical informatics

· Digital health

· Pervasive health

· Bioinformatics

· Computational biology

· Health informatics

· Cheminformatics

We will play a role in encouraging pattern recognition researchers to tackle the specific challenges in healthcare. The activities that will be organized will facilitate interdisciplinary collaborations. Our goal is to gather researchers from different domains and to enable the two-way transfer of knowledge between pattern recognition and health domains.