Diabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions. The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships. Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes. These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions.
Malick R.A.S., Munir S., Jami S.I., Rauf S., Ferretti S., Cherifi H. (2024). DbKB a knowledge graph dataset for diabetes: A system biology approach. DATA IN BRIEF, 52, 1-10 [10.1016/j.dib.2023.110003].
DbKB a knowledge graph dataset for diabetes: A system biology approach
Ferretti S.;
2024
Abstract
Diabetes has emerged as a prevalent disease, affecting millions of individuals annually according to statistics. Numerous studies have delved into identifying key genes implicated in the causal mechanisms of diabetes. This paper specifically concentrates on 20 functional genes identified in various studies contributing to the complexities associated with Type 2 diabetes (T2D), encompassing complications such as nephropathy, retinopathy, cardiovascular disorders, and foot ulcers. These functional genes serve as a foundation for identifying regulatory genes, their regulators, and protein-protein interactions. The current study introduces a multi-layer Knowledge Graph (DbKB based on MSNMD: Multi-Scale Network Model for Diabetes), encompassing biological networks such as gene regulatory networks and protein-protein interaction networks. This Knowledge Graph facilitates the visualization and querying of inherent relationships between biological networks associated with diabetes, enabling the retrieval of regulatory genes, functional genes, interacting proteins, and their relationships. Through the integration of biologically relevant genetic, molecular, and regulatory information, we can scrutinize interactions among T2D candidate genes [1] and ascertain diseased genes [2]. The first layer of regulators comprises direct regulators to the functional genes, sourced from the TRRUST database in the human transcription factors dataset, thereby forming a multi-layered directed graph. A comprehensive exploration of these direct regulators reveals a total of 875 regulatory transcription factors, constituting the initial layer of regulating transcription factors. Moving to the second layer, we identify 550 regulatory genes. These functional genes engage with other proteins to form complexes, exhibiting specific functions. Leveraging these layers, we construct a Knowledge Graph aimed at identifying interaction-driven sub-networks involving (i) regulating functional genes, (ii) functional genes, and (iii) protein-protein interactions.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
