A New Category for Bioinformatics

By Matthew Dublin

An emerging branch of bioinformatics, bioimage informatics, is about to get its own category in Bioinformatics.

Bioimage informatics, which includes biological tissue labeling and automated microscopic imaging data, requires its own methods for management, visualization, and analysis.

Starting in the early 1990s, computer vision, image analysis, data mining, machine learning, and pattern recognition methods have been applied to microscopic images to extract biological data and create databases. Due to the growing amounts of bioimage data, which is increasingly integrated with genomic datasets, the bioinformatics community begun focusing on how best to deal with this new and challenging type of data.

In 2005, the first international workshop on Bioimage Informatics was held at Stanford University and in 2010, the Intelligent Systems for Molecular Biology conference added a submission track for bioimaging data analysis and visualization.

There have been a number of papers (Peng, 2008; Swedlow, et al., 2009; Shamir, et al., 2010; Danuser, 2011) that demonstrate how bioimage informatics techniques can create useful knowledge from image data. However, there are as yet no journals that specifically focus on this new breed of data. So the publishers of Bioinformatics have decided to meet this growing need for a place to publish papers on bioimage informatics.

As of February, the journal now includes a new paper submission category with the following description:

"Informatics methods for the acquisition, analysis, mining and visualization of images produced by modern microscopy, with an emphasis on the application of novel computing techniques to solve challenging and significant biological and medical problems at the molecular, sub-cellular, cellular, and super-cellular (organ, organism, and population) levels. This category also encourages large-scale image informatics methods/applications/software, various enabling techniques (e.g. cyber-infrastructures, quantitative validation experiments, pattern recognition, etc.) for such large-scale studies, and joint analysis of multiple heterogeneous datasets that include images as a component. Bioimage related ontology and databases studies, image-oriented large-scale machine learning, data mining, and other analytics techniques are also encouraged."