Karate Club

Rózemberczki, Benedek; Kiss, Oliver; Sarkar, Rik

doi:10.1145/3340531.3412757

Cited by 92 publications

(11 citation statements)

References 39 publications

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“…Here, we chose to focus only on potential gain-of-interaction edges and first filtered each patient specific-network accordingly. We then grabbed the largest connected component and ran the FEATHER 36 algorithm from the KarateClub NetworkX extension library 37 to generate an embedding for each network.…”

Section: Methodsmentioning

confidence: 99%

Splitpea: quantifying protein interaction network rewiring changes due to alternative splicing in cancer

Dannenfelser,

Yao

2023

Preprint

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Protein-protein interactions play an essential role in nearly all biological processes, and it has become increasingly clear that in order to better understand the fundamental processes that underlie disease, we must develop a strong understanding of both their context specificity (e.g., tissue-specificity) as well as their dynamic nature (e.g., how they respond to environmental changes). While network-based approaches have found much initial success in the application of protein-protein interactions (PPIs) towards systems-level explorations of biology, they often overlook the fact that large numbers of proteins undergo alternative splicing. Alternative splicing has not only been shown to diversify protein function through the generation of multiple protein isoforms, but also remodel PPIs and affect a wide range diseases, including cancer. Isoform-specific interactions are not well characterized, so we develop a computational approach that uses domain-domain interactions in concert with differential exon usage data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression project (GTEx). Using this approach, we can characterize PPIs likely disrupted or possibly even increased due to splicing events for individual patient samples relative to a normal tissue background.

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Section: Methodsmentioning

confidence: 99%

Splitpea: quantifying protein interaction network rewiring changes due to alternative splicing in cancer

Dannenfelser,

Yao

2023

Preprint

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“…We treated the network as an unweighted graph to generate the embedding vectors. 1 To select the node embedding method, we evaluated five types of embeddings: node2vec [15], RandNE [16], GLEE [17], NodeSketch [18], and DeepWalk [19] [20] based on the implementation by Rozemberczki et al [20].…”

Section: Methodsmentioning

confidence: 99%

Network-based anomaly detection algorithm reveals proteins with major roles in human tissues

Kagan,

Jubran,

Yeger-Lotem

et al. 2023

Preprint

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BackgroundAnomaly detection in graphs is critical in various domains, notably in medicine and biology, where anomalies often encapsulate pivotal information. Here, we focused on network analysis of molecular interactions between proteins, which is commonly used to study and infer the impact of proteins on health and disease. In such a network, an anomalous protein might indicate its impact on the organism’s health.ResultsWe propose Weighted Graph Anomalous Node Detection (WGAND), a novel machine learning-based method for detecting anomalies in weighted graphs. WGAND is based on the observation that edge patterns of anomalous nodes tend to deviate significantly from expected patterns. We quantified these deviations to generate features, and utilized the resulting features to model the anomaly of nodes, resulting in node anomaly scores. We created four variants of the WGAND methods and compared them to two previously-published (baseline) methods. We evaluated WGAND on data of protein interactions in 17 human tissues, where anomalous nodes corresponded to proteins with major roles in tissue contexts. In 13 of the tissues, WGAND obtained higher AUC and P@K than baseline methods. We demonstrate that WGAND effectively identified proteins that participate in tissue-specific processes and diseases.ConclusionWe present WGAND, a new approach to anomaly detection in weighted graphs. Our results underscore its capability to highlight critical proteins within protein-protein interaction networks. WGAND holds the promise to enhance our understanding of intricate biological processes and might pave the way for novel therapeutic strategies targeting tissue-specific diseases. Its versatility ensures its applicability across diverse weighted graphs, making it a robust tool for detecting anomalous nodes.

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“…This model offers community identification, node integration, and whole graph incorporation techniques, particularly data mining. [42]. The programming language embraces refinement, aggregation, interpolation, eigenvalues problems, algebraic equations, differential equations, and many other problems.…”

Section: Methodsmentioning

confidence: 99%

Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise

Okfalisa

Angraini

Novi

et al. 2021

J. Teknol. dan Sist. Komput

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The rural development measurement is undoubtedly not easy due to its particular needs and conditions. This study classifies village performance from social, economic, and ecological indices. One thousand five hundred ninety-one villages from the Community and Village Empowerment Office at Riau Province, Indonesia, are grouped into five village maturation classes: very under-developed village, under-developed village, developing village, developed village, and independent village. To date, Density-based spatial clustering of applications with noise (DBSCAN) is utilized in mining 13 of the villages’ attributes. Python programming is applied to analyze and evaluate the DBSCAN activities. The study reveals the grouping’s silhouette coefficient values at 0.8231, thus indicating the well-being clustering performance. The epsilon and minimum points values are considered in DBSCAN evaluation with percentage splits simulation. This grouping can be used as guidelines for governments in analyzing the distribution of rural development subsidies more optimal.

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Karate Club

Cited by 92 publications

References 39 publications

Splitpea: quantifying protein interaction network rewiring changes due to alternative splicing in cancer

Splitpea: quantifying protein interaction network rewiring changes due to alternative splicing in cancer

Network-based anomaly detection algorithm reveals proteins with major roles in human tissues

Identification of the distribution village maturation: Village classification using Density-based spatial clustering of applications with noise

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