Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities

Zhai, Weiqi; Huang, Xiaodi; Shen, Nan; Zhu, Shanfeng

doi:10.1093/bib/bbad172

Cited by 5 publications

(5 citation statements)

References 38 publications

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“…The most important feature for both VP and predicting the genomic test results was the symptom similarity between the patient’ phenotypes and the disease phenotypes. This finding is in line with previous research that also suggested phenotype-driven approach to be effective in variant filtering and identification of the disease-causing variants 34 . To the best of our knowledge, the dataset that we used to evaluate our model is the largest real-world rare disease patient’s sequencing data that has been used for evaluation VP tools.…”

Section: Discussionsupporting

confidence: 92%

A variant prioritization tool leveraging multiple instance learning for rare Mendelian disease genomic testing

Kim,

Baek,

Han

et al. 2024

Preprint

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Background Genomic testing such as exome sequencing and genome sequencing is being widely utilized for diagnosing rare Mendelian disorders. Because of a large number of variants identified by these tests, interpreting the final list of variants and identifying the disease-causing variant even after filtering out likely benign variants could be labor-intensive and time-consuming. It becomes even more burdensome when various variant types such as structural variants need to be considered simultaneously with small variants. One way to accelerate the interpretation process is to have all variants accurately prioritized so that the most likely diagnostic variant(s) are clearly distinguished from the rest. Methods To comprehensively predict the genomic test results, we developed a deep learning based variant prioritization system that leverages multiple instance learning and feeds multiple variant types for variant prioritization. We additionally adopted learning to rank (LTR) for optimal prioritization. We retrospectively developed and validated the model with 5-fold cross-validation in 23,115 patients with suspected rare diseases who underwent whole exome sequencing. Furthermore, we conducted the ablation test to confirm the effectiveness of LTR and the importance of permutational features for model interpretation. We also compared the prioritization performance to publicly available variant prioritization tools. Results The model showed an average AUROC of 0.92 for the genomic test results. Further, the model had a hit rate of 96.8% at 5 when prioritizing single nucleotide variants (SNVs)/small insertions and deletions (INDELs) and copy number variants (CNVs) together, and a hit rate of 95.0% at 5 when prioritizing CNVs alone. Our model outperformed publicly available variant prioritization tools for SNV/INDEL only. In addition, the ablation test showed that the model using LTR significantly outperformed the baseline model that does not use LTR in variant prioritization (p=0.007). Conclusion A deep learning model leveraging multiple instance learning precisely predicted genetic testing conclusion while prioritizing multiple types of variants. This model is expected to accelerate the variant interpretation process in finding the disease-causing variants more quickly for rare genetic diseases.

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Section: Discussionsupporting

confidence: 92%

A variant prioritization tool leveraging multiple instance learning for rare Mendelian disease genomic testing

Kim,

Baek,

Han

et al. 2024

Preprint

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“…In addition to analyzing the properties of the variants, utilizing clinical phenotypes to help the diagnosis is crucial. As patients’ clinical phenotypes can be documented by the Human Phenotype Ontology (HPO) terminology [ 3 ], many computational approaches have been developed based on HPO terms to diagnose rare diseases [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. In addition, many rare diseases often present a characteristic pattern of facial features called “facial gestalt”.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis

Bhasin,

Knaus,

Incardona

et al. 2024

Genes

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Genomic variant prioritization is crucial for identifying disease-associated genetic variations. Integrating facial and clinical feature analyses into this process enhances performance. This study demonstrates the integration of facial analysis (GestaltMatcher) and Human Phenotype Ontology analysis (CADA) within VarFish, an open-source variant analysis framework. Challenges related to non-open-source components were addressed by providing an open-source version of GestaltMatcher, facilitating on-premise facial analysis to address data privacy concerns. Performance evaluation on 163 patients recruited from a German multi-center study of rare diseases showed PEDIA’s superior accuracy in variant prioritization compared to individual scores. This study highlights the importance of further benchmarking and future integration of advanced facial analysis approaches aligned with ACMG guidelines to enhance variant classification.

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“…In addition, Lu et al ( 12 ) investigated the MicroRNA-17’s functions as an oncogene by inhibiting Smad3 expression in carcinoma of the liver. A phenotype-driven paradigm for disease and gene prioritization via bidirectional optimum corresponding lexical commonalities was discovered by Zhai et al ( 13 ). A disease–gene association prediction algorithm that is interpretable from commencement to completion was proposed by Li et al ( 14 ).…”

Section: Introductionmentioning

confidence: 99%

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

Gao,

Kuang,

Duan

et al. 2023

Front. Med.

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Numerous studies have shown that miRNAs play a crucial role in the investigation of complex human diseases. Identifying the connection between miRNAs and diseases is crucial for advancing the treatment of complex diseases. However, traditional methods are frequently constrained by the small sample size and high cost, so computational simulations are urgently required to rapidly and accurately forecast the potential correlation between miRNA and disease. In this paper, the DEJKMDR, a graph convolutional network (GCN)-based miRNA-disease association prediction model is proposed. The novelty of this model lies in the fact that DEJKMDR integrates biomolecular information on miRNA and illness, including functional miRNA similarity, disease semantic similarity, and miRNA and disease similarity, according to their Gaussian interaction attribute. In order to minimize overfitting, some edges are randomly destroyed during the training phase after DropEdge has been used to regularize the edges. JK-Net, meanwhile, is employed to combine various domain scopes through the adaptive learning of nodes in various placements. The experimental results demonstrate that this strategy has superior accuracy and dependability than previous algorithms in terms of predicting an unknown miRNA-disease relationship. In a 10-fold cross-validation, the average AUC of DEJKMDR is determined to be 0.9772.

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Phen2Disease: a phenotype-driven model for disease and gene prioritization by bidirectional maximum matching semantic similarities

Cited by 5 publications

References 38 publications

A variant prioritization tool leveraging multiple instance learning for rare Mendelian disease genomic testing

A variant prioritization tool leveraging multiple instance learning for rare Mendelian disease genomic testing

Enhancing Variant Prioritization in VarFish through On-Premise Computational Facial Analysis

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

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