page_title: Bioimage informatics
section_title: Subcellular Location Analysis
hierarchical_section_title: Bioimage informatics / Important Problems / Subcellular Location Analysis
caption_attribution_description: English: Diagram of nearest neighbor pattern recognition in the context of subcellular location analysis.
context_page_description: Bioimage informatics is a subfield of bioinformatics and computational biology. It focuses on the use of computational techniques to analyze bioimages, especially cellular and molecular images, at large scale and high throughput. The goal is to obtain useful knowledge out of complicated and heterogeneous image and related metadata.
  Automated microscopes are able to collect large numbers of images with minimal intervention. This has led to a data explosion, which absolutely requires automatic processing. Additionally, and surprisingly, for several of these tasks, there is evidence that automated systems can perform better than humans. In addition, automated systems are unbiased, unlike human based analysis whose evaluation may be influenced by the desired outcome.
  There has been an increasing focus on developing novel image processing, computer vision, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems.
context_section_description: Subcellular location analysis was one of the initial problems in this field. In its supervised mode, the problem is to learn a classifier that can recognize images from the major cell organelles based on images.
  Methods used are based on machine learning, building a discriminative classifier based on numeric features computed from the image. Features are either generic features from computer vision, such as Haralick texture features or features specially designed to capture biological factors (e.g., co-localization with a nuclear marker being a typical example).
  For the basic problem of identifying organelles, very high accuracy values can be obtained, including better than ? results. These methods are useful in basic cell biology research, but have also been applied to the discovery of proteins whose location changes in cancer cells.
  However, classification into organelles is a limited form of the problem as many proteins will localize to multiple locations simultaneously (mixed patterns) and many patterns can be distinguished even though they are not different membrane-bound components. There are several unsolved problems in this area and research is ongoing.
caption: Subcellular Location Example. Examples of different patterns are mapped into a two-dimensional space by computing different image features. Image of unknown proteins are similarly mapped into this space and a nearest neighbor search or other classifier can be used for assigning a location to this unclassified protein.
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