A Scalable Similarity-Popularity Link Prediction Method

Kerrache, Said; Alharbi, Ruwayda; Benhidour, Hafida

doi:10.1038/s41598-020-62636-1

Cited by 21 publications

(14 citation statements)

References 50 publications

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“…This metric is also known as -precision 60 – 63 . precision@k is the recommended measure for link prediction algorithms 64 . It refers to the percentage of true positives among only the top ranked predicted links.…”

Section: Methodsmentioning

confidence: 99%

A novel graph mining approach to predict and evaluate food-drug interactions

Rahman

Vadrev

Magana-Mora

et al. 2022

Sci Rep

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Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. This study proposes FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions and 320 unique food items, composed of 563 unique compounds. The potential number of interactions is 87,192 and 92,143 for disjoint and joint versions of the graph. We defined several similarity subnetworks comprising food-drug similarity, drug-drug similarity, and food-food similarity networks. A unique part of the graph involves encoding the food composition as a set of nodes and calculating a content contribution score. To predict new FDIs, we considered several link prediction algorithms and various performance metrics, including the precision@top (top 1%, 2%, and 5%) of the newly predicted links. The shortest path-based method has achieved a precision of 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability, and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. FDMine is publicly available to support clinicians and researchers.

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Section: Methodsmentioning

confidence: 99%

A novel graph mining approach to predict and evaluate food-drug interactions

Rahman

Vadrev

Magana-Mora

et al. 2022

Sci Rep

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show abstract

“…This metric is also known as 𝑟-precision [63,64,65,66]. precision@k is the recommended measure for link prediction algorithms [67]. It refers to the percentage of true positives among only the top 𝑘 ranked predicted links.…”

Section: Performance Evaluationmentioning

confidence: 99%

FDMine: a graph mining approach to predict and evaluate food-drug interactions

Rahman¹,

Vadrev²,

Magana-Mora

et al. 2022

Preprint

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Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

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“…Due to their local nature, these algorithms can scale to very large networks, especially when executed on distributed architectures. Addiitonally, the library includes several state-of-the-art global link predictors, such as SBM ( Guimerà & Sales-Pardo, 2009 ), HRG ( Clauset, Moore & Newman, 2008 ), FBM ( Liu et al, 2013 ), HyperMap ( Papadopoulos et al, 2012 ; Papadopoulos, Psomas & Krioukov, 2015 ) and the popularity-similarity method proposed in Kerrache, Alharbi & Benhidour (2020) .…”

Section: Architecture and Functionalitiesmentioning

confidence: 99%

LinkPred: a high performance library for link prediction in complex networks

Kerrache

2021

PeerJ Computer Science

Self Cite

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The problem of determining the likelihood of the existence of a link between two nodes in a network is called link prediction. This is made possible thanks to the existence of a topological structure in most real-life networks. In other words, the topologies of networked systems such as the World Wide Web, the Internet, metabolic networks, and human society are far from random, which implies that partial observations of these networks can be used to infer information about undiscovered interactions. Significant research efforts have been invested into the development of link prediction algorithms, and some researchers have made the implementation of their methods available to the research community. These implementations, however, are often written in different languages and use different modalities of interaction with the user, which hinders their effective use. This paper introduces LinkPred, a high-performance parallel and distributed link prediction library that includes the implementation of the major link prediction algorithms available in the literature. The library can handle networks with up to millions of nodes and edges and offers a unified interface that facilitates the use and comparison of link prediction algorithms by researchers as well as practitioners.

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A Scalable Similarity-Popularity Link Prediction Method

Cited by 21 publications

References 50 publications

A novel graph mining approach to predict and evaluate food-drug interactions

A novel graph mining approach to predict and evaluate food-drug interactions

FDMine: a graph mining approach to predict and evaluate food-drug interactions

LinkPred: a high performance library for link prediction in complex networks

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