DGL-Ke

Zheng, Da; Song, Xiang; Ma, Chao; Tan, Zeyuan; Ye, Zihao; Dong, Jin Song; Xiong, Hao; Zhang, Zheng; Karypis, George

doi:10.1145/3397271.3401172

Cited by 109 publications

(22 citation statements)

References 7 publications

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“…We use TransE_L2 to train the model [ 26 ] for each node in the COVID‐19 knowledge graph. Specifically, given a candidate drug x , we predicted the existential probability (i.e., the embedding score) for x based on the embeddings of x and SARS‐CoV‐2, denoted as h ( x ) and h ( y ) respectively.…”

Section: Methodsmentioning

confidence: 99%

Drug Repurposing for the Treatment of COVID‐19: A Knowledge Graph Approach

Yan

et al. 2021

Advanced Therapeutics

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Identifying effective drug treatments for COVID‐19 is essential to reduce morbidity and mortality. Although a number of existing drugs have been proposed as potential COVID‐19 treatments, effective data platforms and algorithms to prioritize drug candidates for evaluation and application of knowledge graph for drug repurposing have not been adequately explored. A COVID‐19 knowledge graph by integrating 14 public bioinformatic databases containing information on drugs, genes, proteins, viruses, diseases, symptoms and their linkages is developed. An algorithm is developed to extract hidden linkages connecting drugs and COVID‐19 from the knowledge graph, to generate and rank proposed drug candidates for repurposing as treatments for COVID‐19 by integrating three scores for each drug: motif scores, knowledge graph PageRank scores, and knowledge graph embedding scores. The knowledge graph contains over 48 000 nodes and 13 37 000 edges, including 13 563 molecules in the DrugBank database. From the 5624 molecules identified by the motif‐discovery algorithms, ranking results show that 112 drug molecules had the top 2% scores, of which 50 existing drugs with other indications approved by health administrations reported. The proposed drug candidates serve to generate hypotheses for future evaluation in clinical trials and observational studies.

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Section: Methodsmentioning

confidence: 99%

Drug Repurposing for the Treatment of COVID‐19: A Knowledge Graph Approach

Yan

et al. 2021

Advanced Therapeutics

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“… 30 We used a knowledge graph embedding scheme called TransE to generate vector representations for the elements of the knowledge graph. 31 , 32 We set the size of embeddings to 400 and learning rate to 0.001, and used 400 negative instances per positive instance to train the TransE model.…”

Section: Methodsmentioning

confidence: 99%

A deep learning approach to identify missingis-arelations in SNOMED CT

Abeysinghe

Zheng

Bernstam

et al. 2022

Journal of the American Medical Informatics Association

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Objective SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT. Materials and Methods Our focus is to identify missing is-a relations between concept-pairs exhibiting a containment pattern (ie, the set of words of one concept being a proper subset of that of the other concept). We use hierarchically related containment concept-pairs as positive instances and hierarchically unrelated containment concept-pairs as negative instances to train a model predicting whether an is-a relation exists between 2 concepts with containment pattern. The model is a binary classifier leveraging concept name features, hierarchical features, enriched lexical attribute features, and logical definition features. We introduce a cross-validation inspired approach to identify missing is-a relations among all hierarchically unrelated containment concept-pairs. Results We trained and applied our model on the Clinical finding subhierarchy of SNOMED CT (September 2019 US edition). Our model (based on the validation sets) achieved a precision of 0.8164, recall of 0.8397, and F1 score of 0.8279. Applying the model to predict actual missing is-a relations, we obtained a total of 1661 potential candidates. Domain experts performed evaluation on randomly selected 230 samples and verified that 192 (83.48%) are valid. Conclusions The results showed that our deep learning approach is effective in uncovering missing is-a relations between containment concept-pairs in SNOMED CT.

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“…For this setting, we choose ComplEx and Ro-tatE as our experimental models. This choice is mainly influenced by their superior expressive power (Sun et al 2019) and availability in the DGL-KE toolkit (Zheng et al 2020), which we use throughout these experiments.…”

Section: Static Transductivementioning

confidence: 99%

PubGraph: A Large Scale Scientific Temporal Knowledge Graph

Ahrabian¹,

Du²,

Myloth³

et al. 2023

Preprint

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Research publications are the primary vehicle for sharing scientific progress in the form of new discoveries, methods, techniques, and insights. Publications have been studied from the perspectives of both content analysis and bibliometric structure, but a barrier to more comprehensive studies of scientific research is a lack of publicly accessible large-scale data and resources. In this paper, we present PubGraph, a new resource for studying scientific progress that takes the form of a large-scale temporal knowledge graph (KG). It contains more than 432M nodes and 15.49B edges mapped to the popular Wikidata ontology. We extract three KGs with varying sizes from PubGraph to allow experimentation at different scales. Using these KGs, we introduce a new link prediction benchmark for transductive and inductive settings with temporally-aligned training, validation, and testing partitions. Moreover, we develop two new inductive learning methods better suited to PubGraph, operating on unseen nodes without explicit features, scaling to large KGs, and outperforming existing models. Our results demonstrate that structural features of past citations are sufficient to produce high-quality predictions about new publications. We also identify new challenges for KG models, including an adversarial communitybased link prediction setting, zero-shot inductive learning, and large-scale learning.

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DGL-Ke

Cited by 109 publications

References 7 publications

Drug Repurposing for the Treatment of COVID‐19: A Knowledge Graph Approach

Drug Repurposing for the Treatment of COVID‐19: A Knowledge Graph Approach

A deep learning approach to identify missingis-arelations in SNOMED CT

PubGraph: A Large Scale Scientific Temporal Knowledge Graph

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