Protein function prediction via graph kernels

By Karsten M. Borgwardt, Cheng Soon Ong, Stefan Schönauer, S. V. N. Vishwanathan, Alex Smola, Hans-Peter Kriegel. 2005

1,354 citations recorded by the source corpus.

Subfields: Occupational Therapy, Graph Learning, Computational Biology & Bioinformatics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Occupational Therapy

In Graph Learning

In Computational Biology & Bioinformatics

Citations in the corpus

Position-aware Graph Neural Networks (2019, background): “ommunication graphs from SNAP (Leskovec et al.,2007) with no node features. Each graph has 6 communities and each node is labeled with the community it belongs to. Protein. 1113 protein graphs from (Borgwardt et al., 2005). Each node is labeled with a functional role of the protein. Each node has a ”
Hierarchical Graph Representation Learning with Differentiable Pooling (2018, methodology): “For ENZYMES, COLLAB, and PROTEINS data sets, the number of hidden dimensions is set to 64.”
A simple yet effective baseline for non-attribute graph classification (2018, background): “ENZYMES and PROTEINS [BOS + 05] consist of graph representations of proteins.”
A Kernel Method for the Two-Sample-Problem (2006, methodology): “To studyMMD(2)u B on structured data, we obtained two datasets of protein grap hs (PROTEINS and ENZYMES) and used the graph kernel for proteins from [7] for table matchin g via the Hungarian method (the other tests were not applicable to this graph data).”
Graph Kernels (2008, positive): “Comparing these graphs via a modified random walk graph kernel and classifying them via a Support Vector Machine (SVM) led to function prediction accuracies competitive with state-of-the-art approaches (Borgwardt et al., 2005).”
A Comprehensive Survey on Graph Neural Networks (2019, neutral): “65 82 2 [26], [46], [52], [54], [96], [98] PROTEIN [123] 1113 39.”
On Valid Optimal Assignment Kernels and Applications to Graph Classification (2016, background): “[4, 23, 19, 24]: MUTAG, PTC-MR, NCI1 and NCI109 are graphs derived from small molecules, PROTEINS, D&D and ENZYMES represent macromolecules, and COLLAB and REDDIT are derived from social networks.”
The Multiscale Laplacian Graph Kernel (2016, methodology): “There is a wide range of problems in applied machine learning from web data mining (Inokuchi et al., 2003) to protein function prediction (Borgwardt et al., 2005) where the input space is a space of graphs.”
Long Range Graph Benchmark (2022, neutral): “Previously introduced ENZYMES and PROTEINS datasets [5], that use the 3D structure of the folded proteins to build a graph of amino acids, are notably different from those that we propose here.”
Using PPI network autocorrelation in hierarchical multi-label classification trees for gene function prediction (2013, methodology): “[49] uses graph kernels that measure the similarity between graphs and learns a support vector machine classifier for protein function prediction.”
IAM Graph Database Repository for Graph Based Pattern Recognition and Machine Learning (2008, methodology): “The protein data set consists of graphs representing proteins originally used in [3].”
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (2019, neutral): “rningintheareaof(supervised)graph classiﬁcation.Heretheaimistolearnamodelusingasetoflabeledgraphsto inferthelabelsofunlabeledgraphs.Possibleapplicationsincludeclassiﬁcationof moleculesorsocialnetworks[24,6,21,23].Recently,someconnectionstoneural networksforgraphs[22]anddimensionreductioninlinearprog”
Substructure Assembling Network for Graph Classification (2018, background): “Compared with ECC, SAN performs significantly better on MUTAG, ENZYMES, and D&D, and delivers similar performance on NCI1 and NCI109.”
Integrating structured biological data by Kernel Maximum Mean Discrepancy (2006, methodology): “We obtained 600 proteins modeled as graphs from Borgwardt et al. (2005), and randomly split these into two subsets A and B of 300 proteins each, such that 50 enzymes in each subset belong to each one of the six EC top level classes. We then computed MMD for all pairs of EC classes from subset A and ”
Relational Representation Learning for Dynamic (Knowledge) Graphs: A Survey (2019, background): “Common graph classiﬁcation benchmarks include COLLAB [240], PROTEINS [18] and D&D [62].”
Large Language Models on Graphs: A Comprehensive Survey (2023, neutral): “It denotes connecting text encoding (LLM) and structure encoding (GNN) with cross-modality contrastive learning: A similar philosophy is widely used in vision-language joint modality learning [96].”