Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data

Alyousfi, Dareen; Baralle, Diana; Collins, Andrew

doi:10.1093/bib/bbaa029

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…Methods, using gene similarity, have been proposed successively to overcome this issue. For example, the Katz measure method [ 7 ], the gene-specific score method [ 8 ], the shortest path method [ 9 ] and the Endeavour method rely on the guilt-by-association concept [ 10 ]. These methods work under the hypothesis that genes with similar functions are more likely to be related to similar diseases.…”

Section: Introductionmentioning

confidence: 99%

Predicting disease genes based on multi-head attention fusion

Zhang

et al. 2023

BMC Bioinformatics

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Background The identification of disease-related genes is of great significance for the diagnosis and treatment of human disease. Most studies have focused on developing efficient and accurate computational methods to predict disease-causing genes. Due to the sparsity and complexity of biomedical data, it is still a challenge to develop an effective multi-feature fusion model to identify disease genes. Results This paper proposes an approach to predict the pathogenic gene based on multi-head attention fusion (MHAGP). Firstly, the heterogeneous biological information networks of disease genes are constructed by integrating multiple biomedical knowledge databases. Secondly, two graph representation learning algorithms are used to capture the feature vectors of gene-disease pairs from the network, and the features are fused by introducing multi-head attention. Finally, multi-layer perceptron model is used to predict the gene-disease association. Conclusions The MHAGP model outperforms all of other methods in comparative experiments. Case studies also show that MHAGP is able to predict genes potentially associated with diseases. In the future, more biological entity association data, such as gene-drug, disease phenotype-gene ontology and so on, can be added to expand the information in heterogeneous biological networks and achieve more accurate predictions. In addition, MHAGP with strong expansibility can be used for potential tasks such as gene-drug association and drug-disease association prediction.

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Section: Introductionmentioning

confidence: 99%

Predicting disease genes based on multi-head attention fusion

Zhang

et al. 2023

BMC Bioinformatics

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Identification of variants in genes associated with hypertrophic cardiomyopathy in Mexican patients

García-Vielma,

Lazalde-Córdova,

Arzola-Hernández

et al. 2023

Mol Genet Genomics

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The objective of this work was to identify genetic variants in Mexican patients diagnosed with hypertrophic cardiomyopathy (HCM). According to world literature, the genes mainly involved are MHY7 and MYBPC3, although variants have been found in more than 50 genes related to heart disease and sudden death, and to our knowledge there are no studies in the Mexican population. These variants are reported and classified in the ClinVar (PubMed) database and only some of them are recognized in the Online Mendelian Information in Men (OMIM). The present study included 37 patients, with 14 sporadic cases and 6 familial cases, with a total of 21 index cases. Next-generation sequencing was performed on a predesigned panel of 168 genes associated with heart disease and sudden death. The sequencing analysis revealed twelve (57%) pathogenic or probably pathogenic variants, 9 of them were familial cases, managing to identify pathogenic variants in relatives without symptoms of the disease. At the molecular level, nine of the 12 variants (75%) were single nucleotide changes, 2 (17%) deletions, and 1 (8%) splice site alteration. The genes involved were MYH7 (25%), MYBPC3 (25%) and ACADVL, KCNE1, TNNI3, TPM1, SLC22A5, TNNT2 (8%). In conclusion; we found five variants that were not previously reported in public databases. It is important to follow up on the reclassification of variants, especially those of uncertain significance in patients with symptoms of the condition. All patients included in the study and their relatives received family genetic counseling.

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AGCNAF: predicting disease-gene associations using GCN and multi-head attention to fuse the similarity features

Ma,

Qin,

Zhai

et al. 2024

Eng. Res. Express

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Association prediction between diseases and genes is a critical step in revealing the molecular mechanisms of diseases and developing drug treatment strategies. With the explosive growth of data in the biomedical field, how to effectively utilize these data for accurate prediction has become a hotspot and challenge in current research. To overcome the limitations of current prediction methods in dealing with complex biological network structures and feature extraction, this study proposes AGCNAF, a method that combines an unsupervised Graph Convolutional Network (GCN) and a multi-head attention mechanism. The metagraph-guided random walk strategy enables AGCNAF to capture local and high-order topological structures in the graph, while GCN is responsible for realizing deep feature extraction of these structures. By incorporating similarity features through the multi-attention mechanism, AGCNAF achieves effective integration of global and local features, which significantly improves the prediction performance. By incorporating similarity features through the multi-attention mechanism, AGCNAF achieves effective integration of global and local features, which significantly improves the prediction performance. By utilizing the machine learning binary classification model for prediction, the experimental results through five-fold cross-validation show that AGCNAF demonstrates significant advantages in prediction performance compared to existing methods, with its AUC and AUPR reaching 0.9686 and 0.9709, respectively, and the AUC up to 0.9812 under specific conditions. To verify the practical application value of AGCNAF, this study also conduct case studies on Alzheimer's disease, lung cancer, and breast cancer. The results further confirm the excellent performance of AGCNAF in identifying potential disease-gene associations, which opens up new possibilities for future disease-gene research.

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Essentiality-specific pathogenicity prioritization gene score to improve filtering of disease sequence data

Cited by 4 publications

References 21 publications

Predicting disease genes based on multi-head attention fusion

Predicting disease genes based on multi-head attention fusion

Identification of variants in genes associated with hypertrophic cardiomyopathy in Mexican patients

AGCNAF: predicting disease-gene associations using GCN and multi-head attention to fuse the similarity features

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