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Bioinformatics is the name given to these mathematical and computing approaches used to glean understanding of biological processes. Common activities in bioinformatics include mapping and analyzing DNA and protein sequences, aligning DNA and protein sequences to compare them, and creating and viewing 3-D models of protein structures.
The pace of scientific and technological progress in business informatics is quite rapid; therefore, subjects taught are under permanent reconsideration and revision. [5] In its evolution, the business informatics discipline is fairly young. Therefore, significant hurdles have to be overcome in order to further establish its vision. [6]
In the past few decades, leaps in genomic research have led to massive amounts of biological data. As a result, bioinformatics was created as the convergence of genomics, biotechnology, and information technology, while concentrating on biological data. Biological data has also been difficult to define, as bioinformatics is a wide-encompassing ...
Biological database design, development, and long-term management is a core area of the discipline of bioinformatics. [3] Data contents include gene sequences, textual descriptions, attributes and ontology classifications, citations, and tabular data.
Genomic sequence alignment visualization is used in various applications, playing a crucial role in various areas of genomics and bioinformatics, enabling researchers to analyze, interpret, and extract valuable insights from genetic data. The applications of sequence alignment visualization are diverse and encompass a wide range of research ...
Biological computers use biologically derived molecules — such as DNA and/or proteins — to perform digital or real computations.. The development of biocomputers has been made possible by the expanding new science of nanobiotechnology.
Stopping propagations, i.e. deciding how to cut edges in a graph so that some infectious condition (e.g. a disease, fire, computer virus, etc.) stops its spread. A bi-level genetic algorithm (i.e. a genetic algorithm where the fitness of each individual is calculated by running another genetic algorithm) was used due to the Σ P 2 -completeness ...
Most applications adopt one of two popular heuristic methods: k-means algorithm or k-medoids. Other algorithms do not require an initial number of groups, such as affinity propagation . In a genomic setting this algorithm has been used both to cluster biosynthetic gene clusters in gene cluster families(GCF) and to cluster said GCFs.