Clinical assessment incorporating a personal genome

By Euan A. Ashley, Atul J. Butte, M. Wheeler, Rong Chen, Teri E. Klein, F. Dewey, Joel T. Dudley, K. Ormond, A. Pavlovic, Alexander A. Morgan and 21 more. 2010

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Citations in the corpus

Predicting Functional Effect of Human Missense Mutations Using PolyPhen-2 (2013, background): “…rare alleles that cause Mendelian disease (Bamshad et al., 2011), scanning for potentially medically actionable alleles in an individual’s genome (Ashley et al., 2010), and proﬁling the spectrum of rare variation uncovered by deep sequencing of large populations (Tennessen et al., 2012).”
An Evidence‐Based Framework for Evaluating Pharmacogenomics Knowledge for Personalized Medicine (2021, background): “Clinical annotations remain one of the most popular features on PharmGKB and are used by others to annotate the human genome, 4– 7 develop PGx testing panels, 8 and when reporting PGx testing results to clinicians and patients.”
Integrative omics for health and disease (2018, methodology): “In the genomics space, with an individual’s genotype and a data-base of results from GWAS, one can compute a polygenic risk score to assess an individual’s risk of disease 4,77 (for recent reviews on methodology, see REFS 22,78 ).”
Next-Generation Sequencing — An Overview of the History, Tools, and “Omic” Applications (2016, methodology): “Others have performed similar studies demonstrating that monitoring the longitudinal trends and changes within individuals is an important future protocol for the diagnosis, management, and treatment of disease [9, 81, 270].”
Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes (2012, methodology): “…to interpret and many complex diseases, such as diabetes, neurological disorders and cancer, likely involve a large number of different genes and biological pathways (Ashley et al., 2010; Grayson et al., 2011; Li et al., 2011), as well as environmental contributors that can be difficult to assess.”
Deleterious- and disease-allele prevalence in healthy individuals: insights from current predictions, mutation databases, and population-scale resequencing. (2012, background): “, 19) carried by each subject(18) but have also given us a glimpse of the likely complexity of the functional interpretation of such data.(19) Taken together, theoretical and experimental studies suggest that individuals typically carry hundreds of mildly disadvantageous variants and several severe ”
Mining electronic health records: towards better research applications and clinical care (2012, background): “A key obstacle in the use of genome data for decision making in the clinic is the billions of features that are contained in a single human genome 87 .”
A Review of Feature Selection Methods for Machine Learning-Based Disease Risk Prediction (2022, background): “Successful prediction of individual disease risk has the potential to aid in disease prevention, screening, and early treatment for high-risk individuals (Wray et al., 2007; Ashley et al., 2010; Manolio, 2013).”
Rare and Common Variants: Twenty arguments (2011, background): “gov">US Centers for Disease Control and Prevention (CDC) website for incidence data); and conditioning of the risk for one disease on another disease in the same individua”
The Emerging Paradigm of Network Medicine in the Study of Human Disease (2012, background): “68,69 Rapidly evolving methods of “systems pharmacology” 70 facilitate a more rational and efficient approach to identifying the most relevant “network-based” targets based on their presence in relevant disease modules (Figure 5B).”