Single-omics analyses offer important signals, but the biology often lives in the interactions between layers: DNA, RNA, proteins, and metabolites.

Integration strategies should respect the statistical properties of each data type. Normalization, batch correction, and missing data handling are as important as the modeling itself.

The most successful multi-omics projects build iteratively: start with a core question, validate it experimentally, and expand the model as the evidence grows.