# Challenging Scientific Categorizations Through Dispute Learning
## 3 Results. Standardization of a Dispute Learning System
### Decidable Graph of Knowledge Design (DGKD): Building and Applications
#### 3.4.2 Results Representing the DGKD: Disputable Knowledge and Disputed Knowledge
The main outcomes of this study on dispute analysis are summarized in Figure 7.  
This approach can potentially uncover alternative research areas, unexplored domains within existing fields, undiscovered research methods, and potential extensions of research domains. The transformation method for computing opposing categorization characteristics involves multiple strategic approaches, including:  
Figure 7: Exploring conflicting conditions and detecting disputable knowledge. * inversion (reversing hierarchical relationships while preserving essential information);
* expansion (introducing new conditions and dynamic relationships);
* hybridization (integrating different approaches with flexible structures);
* fusion (merging related categories and creating new categories, identifying unfound patterns). **An example of GRAPHYP's By-Product: The Case with Drug Repurposing**  
GRAPHYP's framework extends across a broad spectrum of scientific contexts, from analyzing traditional and evolving scientific classifications to examining temporal classification patterns. The system's practical applications are equally diverse, encompassing dynamic disease classification networks, knowledge organization through adaptive graphs, ecosystem interaction webs, adaptive care networks, and innovative validation methodologies. **Drug repurposing** presents itself as a particularly promising candidate for a minimum viable product (MVP). This application area offers rich opportunities that could benefit significantly from GRAPHYP's data representation and processing capabilities. The field is especially suitable because data can be readily gathered and processed using new adversarial mining techniques while allowing for human research and expert curation of these datasets. This application would directly demonstrate GRAPHYP's potential as an AI companion, supporting human experts in their final decision-making processes. By combining combinatorial synthesis with Laplacian matrix analysis, GRAPHYP's network structure could enhance drug repurposing efforts, providing a robust framework for discovering novel therapeutic applications. Transcriptomics research trend analysis offers another compelling application area, particularly for developing sample delimitation methodology. During our research for this article, we identified this as a valuable direction for GRAPHYP's future development, especially given the field's rich landscape of contrasting opinions about categorization types and research outcomes. * **DGKD Building: Final Conclusions on the Standardization Approach of DL**  
In the final approach to standardizing the principles of DGKD, we outline the building conditions for further applications of GRAPHYP: (1) initiating with a pilot project in a well-documented scientific domain where knowledge is inherently contested; (2) developing a minimum viable product (MVP) that leverages these guidelines; (3) conducting user testing to refine the interface and methods for representing disputes; and, subsequently, (4) defining advanced functionalities such as an automated dispute detection, resolution suggestions, and algorithms for cross-referencing disputed knowledge. The MVP should strive to balance technical feasibility with user value delivery. Key objectives include pattern recognition and comparison, scalability analysis (e.g., measuring complexity across different scales), identifying optimal categorization levels, examining the network properties of categories, and assessing the depth and breadth of categorization--all while providing decision support. Additionally, the MVP would test its effectiveness in fields like scientific literature analysis and knowledge graph navigation, including dynamic query refinement, hierarchical knowledge representation, and research network mapping. One promising test domain in biology is **spatial transcriptomics in cancer research**, which combines multidisciplinary approaches in biology, exhibits identifiable growth patterns in scientific production and involves practically debated applications.