Research map: Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS

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  1. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology · Sue M. Richards · 2015 · 33500 citations · Cited by this paper
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  13. Accurate proteome-wide missense variant effect prediction with AlphaMissense · Jun Cheng · 2023 · 2246 citations · Cited by this paper
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  15. Ensembl 2022 · Fiona M Cunningham · 2021 · 2224 citations · Cited by this paper
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  17. Predicting the functional impact of protein mutations: application to cancer genomics · Boris A. Reva · 2011 · 2152 citations · Cited by this paper
  18. Information bottleneck-driven Gaussian PID enables modality-level interpretability in multimodal survival prediction · 2026 · Related
  19. Identifying a High Fraction of the Human Genome to be under Selective Constraint Using GERP++ · Eugene V. Davydov · 2010 · 1931 citations · Cited by this paper
  20. Comparison and integration of deleteriousness prediction methods for nonsynonymous SNVs in whole exome sequencing studies · C. Dong · 2014 · 1208 citations · Cited by this paper
  21. DANN: a deep learning approach for annotating the pathogenicity of genetic variants · Daniel X. Quang · 2014 · 1150 citations · Cited by this paper
  22. Disease variant prediction with deep generative models of evolutionary data · Jonathan Frazer · 2021 · 883 citations · Cited by this paper
  23. Genetic diagnosis of developmental disorders in the DDD study: a scalable analysis of genome-wide research data · Caroline Fiona Wright · 2014 · 808 citations · Cited by this paper
  24. A spectral approach integrating functional genomic annotations for coding and noncoding variants · Iuliana Ionita‐Laza · 2016 · 700 citations · Cited by this paper
  25. Distribution and clinical impact of functional variants in 50,726 whole-exome sequences from the DiscovEHR study · Frederick E. Dewey · 2016 · 609 citations · Cited by this paper
  26. AlphaFold2 and its applications in the fields of biology and medicine · Zhenyu Yang · 2023 · 605 citations · Cited by this paper
  27. Calibration of computational tools for missense variant pathogenicity classification and ClinGen recommendations for PP3/BP4 criteria · Vikas Rao Pejaver · 2022 · 598 citations · Cited by this paper
  28. FATHMM-XF: accurate prediction of pathogenic point mutations via extended features · Mark F. Rogers · 2017 · 561 citations · Cited by this paper
  29. Genome-wide prediction of disease variant effects with a deep protein language model · Nadav Brandes · 2023 · 476 citations · Cited by this paper
  30. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions · Max Schubach · 2023 · 454 citations · Cited by this paper
  31. Making new genetic diagnoses with old data: iterative reanalysis and reporting from genome-wide data in 1,133 families with developmental disorders · Caroline Fiona Wright · 2018 · 354 citations · Cited by this paper
  32. Can AlphaFold2 predict the impact of missense mutations on structure? · Gwen R. Buel · 2022 · 354 citations · Cited by this paper
  33. ClinPred: Prediction Tool to Identify Disease-Relevant Nonsynonymous Single-Nucleotide Variants · Najmeh Alirezaie · 2018 · 304 citations · Cited by this paper
  34. DEOGEN2: prediction and interactive visualization of single amino acid variant deleteriousness in human proteins · Daniele Raimondi · 2017 · 238 citations · Cited by this paper
  35. MVP predicts the pathogenicity of missense variants by deep learning · Hongjian Qi · 2021 · 218 citations · Cited by this paper
  36. MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning · Chang Li · 2022 · 193 citations · Cited by this paper
  37. ClinVar: updates to support classifications of both germline and somatic variants · Melissa Landrum · 2024 · 154 citations · Cited by this paper
  38. Performance Comparison of New Adjusted Min-Max with Decimal Scaling and Statistical Column Normalization Methods for Artificial Neural Network Classification · Saichon Sinsomboonthong · 2022 · 144 citations · Cited by this paper
  39. Analysis of AlphaMissense data in different protein groups and structural context · Hedvig Tordai · 2024 · 140 citations · Cited by this paper

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