A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data

Uppu, Suneetha; Krishna, Aneesh; Gopalan, Raj P.

doi:10.1109/tcbb.2016.2635125

Cited by 46 publications

(24 citation statements)

References 200 publications

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“…Artificial intelligence algorithms, especially machine learning, are being increasingly employed to examine biological data . We started utilizing these for pharmacometric analyses almost 10 years ago .…”

Section: Discussionmentioning

confidence: 99%

Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations

Hall

Pasipanodya

Swancutt

et al. 2017

CPT Pharmacometrics Syst. Pharmacol.

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The human species is becoming increasingly obese. Dapsone, which is extensively used across the globe for dermatological disorders, arachnid bites, and for treatment of several bacterial, fungal, and parasitic diseases, could be affected by obesity. We performed a clinical experiment, using optimal design, in volunteers weighing 44–150 kg, to identify the effect of obesity on dapsone pharmacokinetic parameters based on maximum‐likelihood solution via the expectation‐maximization algorithm. Artificial intelligence‐based multivariate adaptive regression splines were used for covariate selection, and identified weight and/or age as predictors of absorption, systemic clearance, and volume of distribution. These relationships occurred only between certain patient weight and age ranges, delimited by multiple hinges and regions of discontinuity, not identified by standard pharmacometric approaches. Older and obese people have lower drug concentrations after standard dosing, but with complex patterns. Given that efficacy is concentration‐dependent, optimal dapsone doses need to be personalized for obese patients.

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Section: Discussionmentioning

confidence: 99%

Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations

Hall

Pasipanodya

Swancutt

et al. 2017

CPT Pharmacometrics Syst. Pharmacol.

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“…Hence, genetic interaction analysis plays a crucial role in identifying genetic modifiers. A range of bioinformatics tools have been developed to predict bi-locus SNP interactions from Single Nucleotide Polymorphisms (SNP) array data of large populations [63,64]. These tools are designed to capture "statistical epistasis" for the traits that are common in the population.…”

Section: Gwas and Genetic Interactionsmentioning

confidence: 99%

“…For the last 12 years, GWAS has been the primary approach to study the common genetic control of various common/complex traits in populations. To identify possible statistical epistasis (SNP interactions) from GWAS, researchers have taken different approaches such as exhaustive, stochastic, heuristic, machine learning, and neural network approaches [63,64,77].…”

Section: Gwas and Genetic Interactionsmentioning

confidence: 99%

Genetic Modifiers and Rare Mendelian Disease

Rahit

Tarailo‐Graovac

2020

Genes

113

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Despite advances in high-throughput sequencing that have revolutionized the discovery of gene defects in rare Mendelian diseases, there are still gaps in translating individual genome variation to observed phenotypic outcomes. While we continue to improve genomics approaches to identify primary disease-causing variants, it is evident that no genetic variant acts alone. In other words, some other variants in the genome (genetic modifiers) may alleviate (suppress) or exacerbate (enhance) the severity of the disease, resulting in the variability of phenotypic outcomes. Thus, to truly understand the disease, we need to consider how the disease-causing variants interact with the rest of the genome in an individual. Here, we review the current state-of-the-field in the identification of genetic modifiers in rare Mendelian diseases and discuss the potential for future approaches that could bridge the existing gap.

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“…High‐dimensional feature selection is an active area of research in genomics . More generally, it has long been an active area of research among statisticians, particularly so for regression problems.…”

Section: Radiogenomic Modeling: Mechanistic Data‐driven and Machine mentioning

confidence: 99%

“…The main weakness of univariable filtering is that it does not account for interactions between features and thus is unable to explain non‐linear behavior between features, such as epistatic interactions . Epistatic interactions are difficult to find and interpret in genomics, as a statistical interaction does not necessarily imply a biological interacton . In the most extreme scenario, a variable may have no marginal effect (i.e., no independent effect or main effect) and only be detected through an interaction when combined with another variable.…”

Section: Radiogenomic Modeling: Mechanistic Data‐driven and Machine mentioning

confidence: 99%

Genomics models in radiotherapy: From mechanistic to machine learning

et al. 2020

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Machine learning (ML) provides a broad framework for addressing high‐dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.

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A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data

Cited by 46 publications

References 200 publications

Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations

Supervised Machine-Learning Reveals That Old and Obese People Achieve Low Dapsone Concentrations

Genetic Modifiers and Rare Mendelian Disease

Genomics models in radiotherapy: From mechanistic to machine learning

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