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Whenever the data can be treated as agnostic, the coding is simplified, as it only has to deal with one case (the data agnostic case) rather than multiple (PNG, PDF, etc.). When the data must be displayed or acted on, then it is interpreted based on the field definitions and formatting information, and returned to a data agnostic format as soon ...
dcGO is a comprehensive ontology database for protein domains. [1] As an ontology resource, dcGO integrates Open Biomedical Ontologies from a variety of contexts, ranging from functional information like Gene Ontology to others on enzymes and pathways, from phenotype information across major model organisms to information about human diseases and drugs.
Any research in life sciences is proposed to answer a scientific question we might have. To answer this question with a high certainty, we need accurate results. The correct definition of the main hypothesis and the research plan will reduce errors while taking a decision in understanding a phenomenon.
Bioinformatics uses biology, chemistry, physics, computer science, data science, computer programming, information engineering, mathematics and statistics to analyze and interpret biological data. The process of analyzing and interpreting data can sometimes be referred to as computational biology , however this distinction between the two terms ...
Content validity evidence involves the degree to which the content of the test matches a content domain associated with the construct. For example, a test of the ability to add two numbers should include a range of combinations of digits. A test with only one-digit numbers, or only even numbers, would not have good coverage of the content domain.
In data management and database analysis, a data domain is the collection of values that a data element may contain. The rule for determining the domain boundary may be as simple as a data type with an enumerated list of values. [1] For example, a database table that has information about people, with one record per person, might have a ...
Data-centric computing. Data-centric computing is an approach that merges innovative hardware and software to treat data, not applications, as the permanent source of value. [8] Data-centric computing aims to rethink both hardware and software to extract as much value as possible from existing and new data sources.
Manasseh Wepundi noted the difference between "the unit of analysis, that is the phenomenon about which generalizations are to be made, that which each 'case' in the data file represents and the level of analysis, that is, the manner in which the units of analysis can be arrayed on a continuum from the very small (micro) to very large (macro ...