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Bioinformatics and computational biology involved the analysis of biological data, particularly DNA, RNA, and protein sequences. The field of bioinformatics experienced explosive growth starting in the mid-1990s, driven largely by the Human Genome Project and by rapid advances in DNA sequencing technology. [citation needed]
Laboratory informatics is the specialized application of information technology aimed at optimizing and extending laboratory operations. [1] It encompasses data acquisition (e.g. through sensors and hardware [2] or voice [3] [4] [5]), instrument interfacing, laboratory networking, data processing, specialized data management systems (such as a chromatography data system), a laboratory ...
The Systems Biology Markup Language (SBML) is a representation format, based on XML, for communicating and storing computational models of biological processes. [1] It is a free and open standard with widespread software support and a community of users and developers.
A more restrictive definition views nanobiotechnology more specifically as the design and engineering of proteins that can then be assembled into larger, functional structures [2] [3] The implementation of nanobiotechnology, as defined in this narrower sense, provides scientists with the ability to engineer biomolecular systems specifically so ...
The terms computational biology and evolutionary computation have a similar name, but are not to be confused. Unlike computational biology, evolutionary computation is not concerned with modeling and analyzing biological data. It instead creates algorithms based on the ideas of evolution across species.
Modelling biological systems is a significant task of systems biology and mathematical biology. [a] Computational systems biology [b] [1] aims to develop and use efficient algorithms, data structures, visualization and communication tools with the goal of computer modelling of biological systems.
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence.
Other systems biology applications of machine learning include the task of enzyme function prediction, high throughput microarray data analysis, analysis of genome-wide association studies to better understand markers of disease, protein function prediction.