AIMC Topic: Genetic Markers

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Genomic Prediction for Quantitative Traits Is Improved by Mapping Variants to Gene Ontology Categories in Drosophila melanogaster.

Genetics
Predicting individual quantitative trait phenotypes from high-resolution genomic polymorphism data is important for personalized medicine in humans, plant and animal breeding, and adaptive evolution. However, this is difficult for populations of unre...

Powerful Tests for Multi-Marker Association Analysis Using Ensemble Learning.

PloS one
Multi-marker approaches have received a lot of attention recently in genome wide association studies and can enhance power to detect new associations under certain conditions. Gene-, gene-set- and pathway-based association tests are increasingly bein...

Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection.

IEEE/ACM transactions on computational biology and bioinformatics
Recently, feature selection and dimensionality reduction have become fundamental tools for many data mining tasks, especially for processing high-dimensional data such as gene expression microarray data. Gene expression microarray data comprises up t...

A tuberculosis ontology for host systems biology.

Tuberculosis (Edinburgh, Scotland)
A major hurdle facing tuberculosis (TB) investigators who want to utilize a rapidly growing body of data from both systems biology approaches and omics technologies is the lack of a standard vocabulary for data annotation and reporting. Lacking a mea...

De novo transcriptome assembly of pummelo and molecular marker development.

PloS one
Pummelo (Citrus grandis) is an important fruit crop worldwide because of its nutritional value. To accelerate the pummelo breeding program, it is essential to obtain extensive genetic information and develop relative molecular markers. Here, we obtai...

A flexible machine learning Mendelian randomization estimator applied to predict the safety and efficacy of sclerostin inhibition.

American journal of human genetics
Mendelian randomization (MR) enables the estimation of causal effects while controlling for unmeasured confounding factors. However, traditional MR's reliance on strong parametric assumptions can introduce bias if these are violated. We describe a ma...

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.

The plant genome
Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underly...

Identification and validation of tissue-based gene biomarkers for acute intestinal graft-versus-host disease(AIGVHD).

Frontiers in immunology
BACKGROUND: Acute intestinal graft-versus-host disease (AIGVHD) is a common complication of allogeneic hematopoietic stem cell transplantation (allo HSCT) with a high mortality rate. The primary aim of the present study is to identify tissue-based ge...

Classification accuracy of machine learning algorithms for Chinese local cattle breeds using genomic markers.

Yi chuan = Hereditas
Accurate breed classification is required for the conservation and utilization of farm animal genetic resources. Traditional classification methods mainly rely on phenotypic characterization. However, it is difficult to distinguish between the highly...