Literature analysis

Literature analysis

Main articles: Text mining and Biomedical text mining

The growth in the number of published literature makes it virtually impossible to read every paper, resulting in disjointed sub-fields of research. Literature analysis aims to employ computational and statistical linguistics to mine this growing library of text resources. For example:

abbreviation recognition – identify the long-form and abbreviation of biological terms,

named entity recognition – recognizing biological terms such as gene names

protein-protein interaction – identify which proteins interact with which proteins from text

The area of research draws from statistics and computational linguistics.

High-throughput image analysis

Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research. Some examples are:

high-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology, Bioimage informatics)

morphometrics

clinical image analysis and visualization

determining the real-time air-flow patterns in breathing lungs of living animals

quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury

making behavioral observations from extended video recordings of laboratory animals

infrared measurements for metabolic activity determination

inferring clone overlaps in DNA mapping, e.g. the Sulston score

High-throughput single cell data analysis

Main article: Flow cytometry bioinformatics

Computational techniques are used to analyse high-throughput, low-measurement single cell data, such as that obtained from flow cytometry. These methods typically involve finding populations of cells that are relevant to a particular disease state or experimental condition.

Biodiversity Informatics

Main article: Biodiversity informatics

Biodiversity informatics deals with the collection and analysis of biodiversity data, such as taxonomic databases, or microbiome data. Examples of such analyses include phylogenetics, niche modelling, species richness mapping, or species identification tools.