Distinguishing enzyme structures from non-enzymes without alignments.

By Paul D. Dobson, Andrew J. Doig. 2003

758 citations recorded by the source corpus.

Subfields: Materials Chemistry, Molecular Biology, Evaluation & Benchmarks

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In Molecular Biology

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Citations in the corpus

Learning Convolutional Neural Networks for Graphs (2016, background): “D&D is a data set of 1178 protein structures (Dobson & Doig, 2003) classiﬁed into enzymes and non-enzymes.”
Discriminative Embeddings of Latent Variable Models for Structured Data (2016, neutral): “Also, in dataset D&D which consists of 82 different types of labels, our algorithm performs much better.”
Hierarchical Graph Representation Learning with Differentiable Pooling (2018, methodology): “For D&D and REDDIT-MULTI-12K, the number of hidden dimensions is set to 128.”
A simple yet effective baseline for non-attribute graph classification (2018, background): “DD [DD03] consists of graph representations of 1,178 proteins.”
Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs (2017, neutral): “D&D [12] is a database of protein structures (vertices are amino acids, edges indicate spatial closeness) classified as enzymes and non-enzymes.”
Propagation kernels: efficient graph kernels from propagated information (2014, background): “d&d consists of 1178 protein structures (Dobson and Doig 2003), where the nodes in each graph represent amino acids and two nodes are connected by an edge if they are less than 6 Ångstroms apart.”
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation (2022, methodology): “For unsupervised and semi-supervised learning, we use datasets from the benchmark TUDataset [27], including graph data for various social networks [2, 52] and biochemical molecules [9, 32].”
Fast subtree kernels on graphs (2009, background): “D&D is a dataset of 1178 protein structures [4].”
Graph Self-Supervised Learning: A Survey (2021, background): “81 4 2 [38], [70], [74], [75], [90], [98], [102] D&D [140] 1,178 284.”
Robust Spatial Filtering With Graph Convolutional Neural Networks (2017, background): “D&D graphs are protein structures that can be classified into enzymes or non-enzymes categories [46].”
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations (2019, background): “D&D (Shervashidze et al. 2011; Dob-son and Doig 2003) and PROTEINS (Dobson and Doig 2003; Borgwardt et al. 2005) are datasets containing proteins as graphs.”
Graph Contrastive Learning Automated (2021, background): “…of diverse nature from the benchmark TUDataset (Morris et al., 2020), including graph data for small molecules & proteins (Riesen & Bunke, 2008; Dobson & Doig, 2003), computer vision (Nene et al., 1996) and various relation networks (Yanardag & Vishwanathan, 2015; Rozemberczki et al., 2020) of…”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “Common graph classiﬁcation benchmarks include COLLAB [240], PROTEINS [18] and D&D [62].”
Discriminative Clustering by Regularized Information Maximization (2010, background): “D&D [16] contains N = 1178 protein structure graphs with binary ground truth labels indicating whether or not they function as enzymes.”