AIMC Topic: Polymorphism, Single Nucleotide

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Multiobjective multifactor dimensionality reduction to detect SNP-SNP interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Single-nucleotide polymorphism (SNP)-SNP interactions (SSIs) are popular markers for understanding disease susceptibility. Multifactor dimensionality reduction (MDR) can successfully detect considerable SSIs. Currently, MDR-based methods ...

Partitioned learning of deep Boltzmann machines for SNP data.

Bioinformatics (Oxford, England)
MOTIVATION: Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been ap...

CMDR based differential evolution identifies the epistatic interaction in genome-wide association studies.

Bioinformatics (Oxford, England)
MOTIVATION: Detecting epistatic interactions in genome-wide association studies (GWAS) is a computational challenge. Such huge numbers of single-nucleotide polymorphism (SNP) combinations limit the some of the powerful algorithms to be applied to det...

A unified model based multifactor dimensionality reduction framework for detecting gene-gene interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Gene-gene interaction (GGI) is one of the most popular approaches for finding and explaining the missing heritability of common complex traits in genome-wide association studies. The multifactor dimensionality reduction (MDR) method has b...

Generalized Multifactor Dimensionality Reduction (GMDR) Analysis of Drug-Metabolizing Enzyme-Encoding Gene Polymorphisms may Predict Treatment Outcomes in Indian Breast Cancer Patients.

World journal of surgery
BACKGROUND: Prediction of response and toxicity of chemotherapy can help personalize the treatment and choose effective yet non-toxic treatment regimen for a breast cancer patient. Interplay of variations in various drug-metabolizing enzyme (DME)-enc...

Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction.

Bioinformatics (Oxford, England)
UNLABELLED: Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simu...

Testing the Relative Performance of Data Adaptive Prediction Algorithms: A Generalized Test of Conditional Risk Differences.

The international journal of biostatistics
Comparing the relative fit of competing models can be used to address many different scientific questions. In classical statistics one can, if appropriate, use likelihood ratio tests and information based criterion, whereas clinical medicine has tend...

A Novel Teaching-Learning-Based Optimization for Improved Mutagenic Primer Design in Mismatch PCR-RFLP SNP Genotyping.

IEEE/ACM transactions on computational biology and bioinformatics
Many single nucleotide polymorphisms (SNPs) for complex genetic diseases are genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in small-scale basic research studies. It is an essential work to design feasible ...