AIMC Topic: Genome-Wide Association Study
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A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 2, 2016
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
Nov 5, 2016
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
Oct 1, 2016
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
Sep 13, 2016
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
Aug 24, 2016
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...
Identification of New Fungal Peroxisomal Matrix Proteins and Revision of the PTS1 Consensus.
Traffic (Copenhagen, Denmark)
Aug 22, 2016
The peroxisomal targeting signal type 1 (PTS1) is a seemingly simple peptide sequence at the C-terminal end of most peroxisomal matrix proteins. PTS1 can be described as a tripeptide with the consensus motif [S/A/C] [K/R/H] L. However, this descripti...
Use of a Novel Nonparametric Version of DEPTH to Identify Genomic Regions Associated with Prostate Cancer Risk.
Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology
Aug 18, 2016
BACKGROUND: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of cont...
Random Bits Forest: a Strong Classifier/Regressor for Big Data.
Scientific reports
Jul 22, 2016
Efficiency, memory consumption, and robustness are common problems with many popular methods for data analysis. As a solution, we present Random Bits Forest (RBF), a classification and regression algorithm that integrates neural networks (for depth),...
A knowledge-based approach for predicting gene-disease associations.
Bioinformatics (Oxford, England)
Jun 9, 2016
MOTIVATION: Recent advances of next-generation sequence technologies have made it possible to rapidly and inexpensively identify gene variations. Knowing the disease association of these gene variations is important for early intervention to treat de...
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
Nucleic acids research
Apr 15, 2016
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of fun...
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