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[12] Logical circuits can be built with slime moulds. [13] Distributed systems experiments have used them to approximate motorway graphs. [14] The slime mould Physarum polycephalum is able to compute high-quality approximate solutions to the Traveling Salesman Problem, a combinatorial test with exponentially increasing complexity, in linear ...
[1] Image and signal processing allow extraction of useful results from large amounts of raw data. In the field of genetics, it aids in sequencing and annotating genomes and their observed mutations. Bioinformatics includes text mining of biological literature and the development of biological and gene ontologies to organize and query ...
As such, computational genomics may be regarded as a subset of bioinformatics and computational biology, but with a focus on using whole genomes (rather than individual genes) to understand the principles of how the DNA of a species controls its biology at the molecular level and beyond. With the current abundance of massive biological datasets ...
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 ...
[1] While each field is distinct, there may be significant overlap at their interface, [1] so much so that to many, bioinformatics and computational biology are terms that are used interchangeably. The terms computational biology and evolutionary computation have a similar name, but are not to be confused. Unlike computational biology ...
In 2021, a team led by biophysicist Sangram Bagh realized a study with E. coli to solve 2 x 2 maze problems to probe the principle for distributed computing among cells. [10] [11] In 2024, FinalSpark, a Swiss biocomputing startup, launched an online platform enabling global researchers to conduct experiments remotely on biological neurons in vitro.
Machine learning in environmental metagenomics can help to answer questions related to the interactions between microbial communities and ecosystems, e.g. the work of Xun et al., in 2021 [50] where the use of different machine learning methods offered insights on the relationship among the soil, microbiome biodiversity, and ecosystem stability.
Translational bioinformatics is a relatively young field within translational research. [5] [6] Google trends indicate the use of "bioinformatics" has decreased since the mid-1990s when it was suggested as a transformative approach to biomedical research. [6]