ReviewMolecular aspects of medicine2023
Phenotypic screening models for rapid diagnosis of genetic variants and discovery of personalized therapeutics.
Review in Molecular aspects of medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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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.
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Who cites it
14 citing papers in PubMed, 21 citations in OpenAlex.
- Aminoacyl-tRNA Synthetases: Variant Classification, Functional Assays, and Emerging Therapeutic Strategies.Journal of inherited metabolic disease · 2026Review
- The Grey Zone Project: Risk-Based Classification of ABCD1 Variants in X-Linked Adrenoleukodystrophy.Journal of inherited metabolic disease · 2026Article
- Dynamicasome-a molecular dynamics-guided and AI-driven pathogenicity prediction catalogue for all genetic mutations.Communications biology · 2025Article
- Investigation of a germline variant of uncertain significance (MRE11:c.1138C > T) identified by exome sequencing in a cancer-affected individual and co-segregation analysis in affected and unaffected family members.Molecular biology reports · 2025Article
- UtilizingJournal of developmental biology · 2025Review
- Functional analysis ofGenetics in medicine open · 2025Article
- Rapid whole genome sequencing in newborn screening for metabolic diseases.Frontiers in pediatrics · 2025Review
- Leaving no patient behind! Expert recommendation in the use of innovative technologies for diagnosing rare diseases.Orphanet journal of rare diseases · 2024Review
- Modelling phenotypes, variants and pathomechanisms of syndromic diseases in different systems.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2024Article
- CytoSIP: an annotated structural atlas for interactions involving cytokines or cytokine receptors.Communications biology · 2024Article
- A new Caenorhabditis elegans model to study copper toxicity in Wilson disease.Traffic (Copenhagen, Denmark) · 2024Article
- A humanized Caenorhabditis elegans model of hereditary spastic paraplegia-associated variants in KLC4.Disease models & mechanisms · 2023Article
- A humanizedbioRxiv : the preprint server for biology · 2023Article
- Clinical variants inGenetics in medicine open · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Precision medicine strives for highly individualized treatments for disease under the notion that each individual's unique genetic makeup and environmental exposures imprints upon them not only a disposition to illness, but also an optimal therapeutic approach. In the realm of rare disorders, genetic predisposition is often the predominant mechanism driving disease presentation. For such, mostly, monogenic disorders, a causal gene to phenotype association is likely. As a result, it becomes important to query the patient's genome for the presence of pathogenic variations that are likely to cause the disease. Determining whether a variant is pathogenic or not is critical to these analyses and can be challenging, as many disease-causing variants are novel and, ergo, have no available functional data to help categorize them. This problem is exacerbated by the need for rapid evaluation of pathogenicity, since many genetic diseases present in young children who will experience increased morbidity and mortality without rapid diagnosis and therapeutics. Here, we discuss the utility of animal models, with a focus mainly on C. elegans, as a contrast to tissue culture and in silico approaches, with emphasis on how these systems are used in determining pathogenicity of variants with uncertain significance and then used to screen for novel therapeutics.
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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.