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Ontologies and data integration
Biological ontologies are directed acyclic graphs of controlled vocabularies. They are designed to capture biological concepts and descriptions in a way that can be easily categorised and analysed with computers. When categorised in this way, it is possible to gain added value from holistic and integrated analysis.

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.

By: rogerglewis

rogerglewis — Tue, 10 Sep 2019 08:07:59 +0000

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.

By: rogerglewis

rogerglewis — Tue, 10 Sep 2019 08:05:35 +0000

Analyzing biological data to produce meaningful information involves writing and running software programs that use algorithms from graph theory, artificial intelligence, soft computing, data mining, image processing, and computer simulation. The algorithms in turn depend on theoretical foundations such as discrete mathematics, control theory, system theory, information theory, and statistics.

https://en.wikipedia.org/wiki/Bioinformatics

https://www.bitchute.com/video/Nr6AwHmufWRD/
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