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Graphlet Laplacians: graphlet-based neighbourhoods highlight topology-function and topology-disease relationships

https://doi.org/10.1101/460964
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Discovering genetic ancestry using spectral graph theory
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Network diffusion accurately models the relationship between structural and functional brain connectivity networks
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Gene Ontology: tool for the unification of biology
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Variations in DNA elucidate molecular networks that cause disease
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Mutational landscape and significance across 12 major cancer types
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Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes
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IntOGen-mutations identifies cancer drivers across tumor types
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Network-based stratification of tumor mutations
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A census of human cancer genes
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Network propagation: a universal amplifier of genetic associations
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Single-cell Co-expression Subnetwork Analysis
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Identifying potential cancer driver genes by genomic data integration
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Revealing the Hidden Language of Complex Networks
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Random Geometric Graphs
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Functional topology in a network of protein interactions
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Biological network comparison using graphlet degree distribution
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In silicogrouping of peptide/HLA class I complexes using structural interaction characteristics
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Drug-target interaction prediction from chemical, genomic and pharmacological data in an integrated framework
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Walking the interactome for candidate prioritization in exome sequencing studies of Mendelian diseases
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Topology-function conservation in protein–protein interaction networks
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BioGRID: a general repository for interaction datasets
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COXPRESdb in 2015: coexpression database for animal species by DNA-microarray and RNAseq-based expression data with multiple quality assessment systems
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Integrated interactions database: tissue-specific view of the human and model organism interactomes
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Expansion of the Gene Ontology knowledgebase and resources
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Modelling protein–protein interaction networks via a stickiness index
inferredcites →https://doi.org/10.1098/rsif.2009.0192openalex-snapshot
Systems-level cancer gene identification from protein interaction network topology applied to melanogenesis-related functional genomics data
inferredcites →https://doi.org/10.1101/027219openalex-snapshot
Laplacian eigenmaps and principal curves for high resolution pseudotemporal ordering of single-cell RNA-seq profiles
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Prediction drug-target interaction networks based on semi-supervised learning method
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Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
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Estimating Whole-Brain Dynamics by Using Spectral Clustering
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A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
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Emergence of Scaling in Random Networks
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Nonlinear Dimensionality Reduction by Locally Linear Embedding
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A global genetic interaction network maps a wiring diagram of cellular function
inferredcites →https://doi.org/10.1142/9789814295291_0020openalex-snapshot
GEOMETRIC EVOLUTIONARY DYNAMICS OF PROTEIN INTERACTION NETWORKS
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PATIENT-SPECIFIC DATA FUSION FOR CANCER STRATIFICATION AND PERSONALISED TREATMENT
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Modeling of Protein Interaction Networks
inferredcites →https://doi.org/10.1162/089976603321780317openalex-snapshot
Laplacian Eigenmaps for Dimensionality Reduction and Data Representation
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Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data
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Vicus: Exploiting local structures to improve network-based analysis of biological data
inferredcites →https://doi.org/10.1371/journal.pone.0076339openalex-snapshot
Going the Distance for Protein Function Prediction: A New Distance Metric for Protein Interaction Networks
inferredcites →https://doi.org/10.3102/10769986025001060openalex-snapshot
On the Adaptive Control of the False Discovery Rate in Multiple Testing With Independent Statistics
inferredcites →https://doi.org/10.4137/cin.s680openalex-snapshot
Uncovering Biological Network Function via Graphlet Degree Signatures
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A Local Learning Approach for Clustering

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.21203/rs.3.rs-6183264/v1openalex-snapshot
Brain Signal Analysis of Neurological Disorders Using Topological Graphs
inferred← citeshttps://doi.org/10.3390/e28101043openalex-snapshot
Preserving the Motif-Relevant Structure in Graph Neural Networks: An Entropy-Aware Study of Aggregation and Depth
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