Categorizing biological information can be subjective and ambiguous, which poses challenges for indexing potentially useful biological information for design. Therefore, we explored collective categorization to study the categorization task. After gathering 163 examples of biological transformation, we asked four participants to independently categorize the examples using self-selected approaches. A computational algorithm was used to quantify the relatedness between the groups that each participant created. The results confirmed that participants had different perspectives in interpreting and categorizing biological information. However, the collective categorization method could reveal meaningful semantics in biological information such as hierarchical, synonymous, or causal relations. The relations discovered could lead to developing formal representations or learning unique patterns in biological phenomena.