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

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arxiv 2402.01809 v1 pith:3LRDFKYM submitted 2024-02-02 q-bio.GN cs.LG

classification q-bio.GNcs.LG
keywords explanationgeneticheterogeneoushumannetworksneuralphenolinkerphenotype-gene
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. G2PDiffusion: Cross-Species Genotype-to-Phenotype Prediction via Evolutionary Diffusion

    cs.LG 2025-02 conditional novelty 6.0 of 10

    G2PDiffusion is a diffusion model that generates morphological insect images from DNA barcodes, evolutionary alignments, and latitude/longitude, with inference-time guidance toward DNA-image alignment.

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