AIMC Topic: Genome-Wide Association Study

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Computer vision and machine learning for robust phenotyping in genome-wide studies.

Scientific reports
Traditional evaluation of crop biotic and abiotic stresses are time-consuming and labor-intensive limiting the ability to dissect the genetic basis of quantitative traits. A machine learning (ML)-enabled image-phenotyping pipeline for the genetic stu...

Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction.

Scientific reports
Multi-Instance (MI) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with multiple instances. Many studies in this literature attempted to find an appropriate ...

Bosco: Boosting Corrections for Genome-Wide Association Studies With Imbalanced Samples.

IEEE transactions on nanobioscience
In genome-wide association studies (GWAS), the acquired sequential data may exhibit imbalance structure: abundant control vs. limited case samples. Such sample imbalance issue is particularly serious when investigating rare diseases or common disease...

Robust differential expression analysis by learning discriminant boundary in multi-dimensional space of statistical attributes.

BMC bioinformatics
BACKGROUND: Performing statistical tests is an important step in analyzing genome-wide datasets for detecting genomic features differentially expressed between conditions. Each type of statistical test has its own advantages in characterizing certain...

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

IEEE/ACM transactions on computational biology and bioinformatics
In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing ...

An algorithm for direct causal learning of influences on patient outcomes.

Artificial intelligence in medicine
OBJECTIVE: This study aims at developing and introducing a new algorithm, called direct causal learner (DCL), for learning the direct causal influences of a single target. We applied it to both simulated and real clinical and genome wide association ...

A machine learning-based framework to identify type 2 diabetes through electronic health records.

International journal of medical informatics
OBJECTIVE: To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects wit...

ACID: Association Correction for Imbalanced Data in GWAS.

IEEE/ACM transactions on computational biology and bioinformatics
Genome-wide association study (GWAS) has been widely witnessed as a powerful tool for revealing suspicious loci from various diseases. However, real world GWAS tasks always suffer from the data imbalance problem of sufficient control samples and limi...

Using the Generalized Index of Dissimilarity to Detect Gene-Gene Interactions in Multi-Class Phenotypes.

PloS one
To find genetic association between complex diseases and phenotypic traits, one important procedure is conducting a joint analysis. Multifactor dimensionality reduction (MDR) is an efficient method of examining the interactions between genes in genet...