Editing Bioinformatics (section)

==Others==
===Literature analysis===
{{main|Text mining|Biomedical text mining}}

The enormous number of published literature makes it virtually impossible for individuals 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 [[protein]]s 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 automate the processing, quantification and analysis of large amounts of high-information-content [[medical imaging|biomedical imagery]]. Modern [[image analysis]] systems can improve an observer's [[accuracy]], [[Objectivity (science)|objectivity]], or speed. Image analysis is 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 [[Gene mapping|DNA mapping]], e.g. the [[Sulston score]]

===High-throughput single cell data analysis===
{{main|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.

===Ontologies and data integration===
Biological ontologies are [[directed acyclic graph]]s of [[controlled vocabularies]]. They create categories for biological concepts and descriptions so they can be easily analyzed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.{{Citation needed|date=June 2023}}

The [[OBO Foundry]] was an effort to standardise certain ontologies. One of the most widespread is the [[Gene ontology]] which describes gene function. There are also ontologies which describe phenotypes.