mlsanityData to 7 Oct 2026

PhenoLinker: Phenotype-Gene Link Prediction and Explanation using Heterogeneous Graph Neural Networks

arXiv 2402.01809 ↗First posted 2 Feb 2024q-bio.GN cs.LGcited by 1

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Abstract (arXiv)

The association of a given human phenotype to a genetic variant remains a critical challenge for biology. We present a novel system called PhenoLinker capable of associating a score to a phenotype-gene relationship by using heterogeneous information networks and a convolutional neural network-based model for graphs, which can provide an explanation for the predictions. This system can aid in the discovery of new associations and in the understanding of the consequences of human genetic variation.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 72 references that are in the map. First posted on arXiv 2 Feb 2024.

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References · 7

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2017Axiomatic Attribution for Deep Networkscited by 3,350
  4. 2020Captum: A unified and generic model interpretability library for PyTorchcited by 542
  5. 2021Graph Based Link Prediction between Human Phenotypes and Genescited by 5
  6. 2021Integrating Transductive And Inductive Embeddings Improves Link Prediction Accuracycited by 2
  7. 2024Introducing New Node Prediction in Graph Mining: Predicting All Links from Isolated Nodes with Graph Neural Networkscited by 1

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