ArticleGenome medicine2024
MAGPIE: accurate pathogenic prediction for multiple variant types using machine learning approach.
Article in Genome medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed.
- Advances and challenges of splicing prediction with AI.Nature genetics · 2026Review
- The Shape of Things to Come: α-Helical Membrane Protein Folding on the Ribosome.Chemical reviews · 2026Review
- A Narrative Review on Integrative Bioinformatics Approaches for microRNA Research in Familial Mediterranean Fever: Current Insights and Future Directions.Health science reports · 2026Article
- Molecular Mechanisms of CLCN5 Missense Mutations in Dent Disease Type 1: A Comprehensive Computational Analysis and Clinical Correlations in a Chinese Cohort.Journal of cellular and molecular medicine · 2026Article
- Harnessing artificial intelligence for genomic variant prediction: advances, challenges, and future directions.GigaScience · 2026Review
- Machine learning models incorporating genotype and ancestry improve severe asthma risk prediction.Scientific reports · 2025Article
- Discriminating activating, deactivating and resistance variants in protein kinases.Genome medicine · 2025Article
- Disease-Specific Prediction of Missense Variant Pathogenicity with DNA Language Models and Graph Neural Networks.Bioengineering (Basel, Switzerland) · 2025Article
- Utilizing protein structure graph embeddings to predict the pathogenicity of missense variants.NAR genomics and bioinformatics · 2025Article
- Leveraging transformers for semi-supervised pathogenicity prediction with soft labels.Journal of integrative bioinformatics · 2025Article
- Clinical Characteristics and Genetic Variants in Children withMedicina (Kaunas, Lithuania) · 2025Article
- Hydrophilic/Omniphobic Droplet Arrays for High-throughput and Quantitative Enzymology.Analytical chemistry · 2025Article
- DS-MVP: identifying disease-specific pathogenicity of missense variants by pre-training representation.Briefings in bioinformatics · 2025Article
- A graph neural network approach for accurate prediction of pathogenicity in multi-type variants.Briefings in bioinformatics · 2025Article
- Identification of Pathogenic Missense Mutations of NF1 Using Computational Approaches.Journal of molecular neuroscience : MN · 2024Article
- Genotypic and phenotypic spectrum of maple syrup urine disease in Zhejiang of China.QJM : monthly journal of the Association of Physicians · 2024Article
- Decoding Missense Variants by Incorporating Phase Separation via Machine Learning.Nature communications · 2024Article
- Correction: Genome Med 15, 115 & Genome Med 16, 3.Genome medicine · 2024Article
- Dinucleotide composition representation -based deep learning to predict scoliosis-associated Fibrillin-1 genotypes.Frontiers in genetics · 2024Article
- Case Report: Charcot-marie-tooth disease caused by aFrontiers in genetics · 2024Article
Corrections and comments
- Erratum issued
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying pathogenic variants from the vast majority of nucleotide variation remains a challenge. We present a method named Multimodal Annotation Generated Pathogenic Impact Evaluator (MAGPIE) that predicts the pathogenicity of multi-type variants. MAGPIE uses the ClinVar dataset for training and demonstrates superior performance in both the independent test set and multiple orthogonal validation datasets, accurately predicting variant pathogenicity. Notably, MAGPIE performs best in predicting the pathogenicity of rare variants and highly imbalanced datasets. Overall, results underline the robustness of MAGPIE as a valuable tool for predicting pathogenicity in various types of human genome variations. MAGPIE is available at https://github.com/shenlab-genomics/magpie .
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.