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The use of biological entities as 'sensors' has long been of interest to engineers and biologists.
The emergence of sustainability initiatives has increased the use of biological entities as reducing agents in AuNP synthesis (i.e., green synthesis) to replace toxic chemicals.
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Fluorescence sensing using MOFs also proves useful for detection of biological entities.
Another important use was linking mentions of biological entities in text with the correct identifiers in biological databases, as well as linkage to the appropriate ontology terms.
Glyphs are stylized graphical symbols that impart information nonverbally and are used to portray different classes of biological entities e.g. protein, gene, pathogen etc. and the nature of the relationships between them.
Saric et al. focused on detecting the noun phrase (NP) of biological entities that use the active and passive voices and proposed the NLP based method for regulatory relationship extraction [17], [18].
Boolean network, first presented by Kauffman [14], is a logical modeling approach that uses binary representation for the state of biological entities.
The original model used bit strings to represent the receptors of biological entities.
An additional advantage of using cross-assembly is that the sequences of biological entities, which are shared between specific samples, are simultaneously assembled.
These patterns can be discovered, described, and interpreted using computer-driven algorithms, enabling the discovery and comparison of biological entities as well as approximate predictions of their functions.
This association information is useful in understanding the mechanisms of action of biological entities as a whole, especially those entities biological researchers are not familiar with.
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