DiabMet: A curated repository of patients-level metabolic networks and an SBML-based type 2 diabetes metabolic model

Website URL: https://metabolic-networks-features.biomath.work/ DiabMet: A curated repository of patients-level metabolic networks and an SBML-based type 2 diabetes metabolic model This repository contains metabolic network features extracted from sample patient data, including Type 2 Diabetes Mellitus (T2DM)-related tissue data tissue data. Each section includes features organized into the following schemes: MetGraph: Original features derived from metabolic networks. NDD (Node Distance Dependency): Features based on the distance relationships among metabolites. TM1: First-order random walk features originating from specific metabolites and their immediate neighbors. TM2: Second-order random walk features capturing broader metabolite neighborhood structures. NDD+TM1: Combined features incorporating both NDD and first-order random walks. NDD+TM1+TM2: Integrated features combining NDD, TM1, and TM2. Graph Embedding We employ several graph embedding algorithms, including: Graph2Vec GL2Vec FeatherGraph Netpro2Vec These embeddings are generated using MetGraph features as the primary input source. The features within are generated by combining four patient groups (100, 200, 300, 400), two threshold settings (1_80 and 2_80), three aggregation schemes (MeanSum, MinMax, MinSum), and three embedding dimensions (64, 128, 196). So, we have 72 features within FeatherGraph, GL2Vec, Graph2Vec, and several Netpro2vec in total.