- Utilizing nucleic languages for genomic discovery
- Managing the Genetic Engineering Regulatory Maze
- Providing a Framework for Machine Language Labeling
- Creating Shared Controlled Variables
The Life Sciences domain is extensive, but in almost all cases, the need to manage complex genomic and proteomic data involves an effort that looks more taxonomic (organizational) than it does numeric (analytical). For this reason, knowledge graphs are becoming increasingly central to the various subdomains of life sciences, as they provide a balance between organizational management of symbols (sequences of genomes, proteins, enzymes, and so forth) while also allowing for operational processing of these sequences for a wide variety of tasks.
Utilizing Nucleic Languages For Genomic Discovery
One of the most significant discoveries of the twentieth century was the realization that one could represent DNA and RNA as a language of nucleic acids and codons (nucleic acid triples) that could in turn, be used to build proteins, enzymes, and other critical biological infrastructure. This, in essence, made biology computable and made the description of nucleic languages feasible. Knowledge Graphs are ideally suited for storing, searching, and manipulating such languages, making it easier to identify genomes for everything from bacteria to human beings.
Managing the Genetic Engineering Regulatory Maze
Genetic engineering has become a mainstay of manufacturing, from food production to specialized drugs and vaccines to enzymes for further production. Despite this, the regulatory environment for both genetic engineering and pharmaceutical processing is frequently slow, contradictory in places, and politically fraught with risk. Knowledge graphs are an ideal tool for managing test and test results, feedback, and reporting necessary to make discoveries in the life sciences.
Providing a Framework for Machine Language Labeling
A significant part of current research is built around the construction of large-scale machine-learning models. While it is possible to employ unsupervised learning for these, the downside this introduces is that it becomes far harder to determine why such models return what they do, which is unacceptable in a regulatory environment. Knowledge graphs can be used closely with machine learning algorithms of various sorts to make the labeling process both efficient and discoverable and to ascertain better than the significant results of such models.
Creating Controlled Variables
Ontologies are not just taxonomies – they are data models that can be used to associate (at an organizational level) calculated terms with specific identifiers. This can be used to associate biochemical properties with values (the atomic weight, density, even chemical structure) that can be referenced without having to do so in applications. This becomes very useful when dealing with analytics, where rules can force recalculation in a changing environment, and this in turn reduces the complexity of the applications working with this data.
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