Quantitative evaluations of variations using the population mean as a baseline for bioinformatics interpretation

Area of Science:

• Quantitative analysis
• Statistical modeling
• Data science

Background:

• Traditional methods for evaluating data variations can be limited.
• A need exists for robust quantitative evaluation models using population mean as a baseline.

Purpose of the Study:

• To establish a quantitative evaluation model using the population mean as a baseline for variations.
• To describe variations derived from different data types and systems using novel concepts.

Main Methods:

• Transformed observed datasets (measurement and relative data) to a 0-1.0 scale using the population mean.
• Applied different transformation methods for datasets of varying types and baselines.
• Introduced the 'middle compared index' (MCI) formula: [a/(a+b)+(1-b)/(2-a-b)-1]^1.7, where 'a' is the post-change value and 'b' is the pre-change value.
• Validated the MCI's quantitative evaluation capabilities using actual data.

Main Results:

• The MCI demonstrated validity, yielding 0 when pre- and post-magnitude values were equal, and 1 when values changed from 0 to 1.
• MCI values approximated 0.5 for intermediate changes (e.g., 0 to 0.5, 0.5 to 1.0).
• The MCI produced distinct values compared to absolute and ratio methods, confirming its independence.

Conclusions:

• The MCI functions effectively as an evaluation model with the population mean as a baseline.
• The MCI may offer a more reasonable evaluation than absolute or ratio methods.
• The MCI enhances understanding of quantitative variations in association measures.