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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.
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.
Computational anatomy is the study of anatomical shape and form at the visible or gross anatomical scale of morphology. It involves the development of computational mathematical and data-analytical methods for modeling and simulating biological structures.
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. Data collection is a research component in all study fields, including physical and social sciences, humanities, [2] and business ...
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]
Genome informatics also includes the field of genome design. There still a lot more we can do and develop in Genome Informatics. Find a potential disease, searching a solution for a disease, or proving why people get sick for no reason. For genomic informatics there are several main applications for it, including: genome information analysis [8]
Protein function prediction methods are techniques that bioinformatics researchers use to assign biological or biochemical roles to proteins. These proteins are usually ones that are poorly studied or predicted based on genomic sequence data. These predictions are often driven by data-intensive computational procedures.
The term functional genomics is often used to refer to the many technical approaches to study an organism's genes and proteins, including the "biochemical, cellular, and/or physiological properties of each and every gene product" [2] while some authors include the study of nongenic elements in their definition. [3]