AIMC Topic: Genetic Association Studies

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A deep convolutional neural network approach for predicting phenotypes from genotypes.

Planta
Deep learning is a promising technology to accurately select individuals with high phenotypic values based on genotypic data. Genomic selection (GS) is a promising breeding strategy by which the phenotypes of plant individuals are usually predicted b...

Network-based association analysis to infer new disease-gene relationships using large-scale protein interactions.

PloS one
Protein-protein interactions integrated with disease-gene associations represent important information for revealing protein functions under disease conditions to improve the prevention, diagnosis, and treatment of complex diseases. Although several ...

Multi-label Deep Learning for Gene Function Annotation in Cancer Pathways.

Scientific reports
The war on cancer is progressing globally but slowly as researchers around the world continue to seek and discover more innovative and effective ways of curing this catastrophic disease. Organizing biological information, representing it, and making ...

Comparative analysis of weighted gene co-expression networks in human and mouse.

PloS one
The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-...

PSCA rs1045531 Polymorphism and the Risk of Prostate Cancer in a Chinese Population Undergoing Prostate Biopsy.

Technology in cancer research & treatment
BACKGROUND AND PURPOSE: This study explored the association between a single-nucleotide polymorphism of prostate stem cell antigen and prostate cancer in Chinese patients undergoing prostate biopsy.

Interactive phenotyping of large-scale histology imaging data with HistomicsML.

Scientific reports
Whole-slide imaging of histologic sections captures tissue microenvironments and cytologic details in expansive high-resolution images. These images can be mined to extract quantitative features that describe tissues, yielding measurements for hundre...

Predicting pathogenic genes for primary myelofibrosis based on a system‑network approach.

Molecular medicine reports
The aim of the present study was to predict pathogenic genes for primary myelofibrosis (PMF) using a system‑network approach by combining protein‑protein interaction (PPI) network and gene expression data with known pathogenic genes. PMF gene express...

Prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods.

BMC bioinformatics
BACKGROUND: The prediction of human gene-abnormal phenotype associations is a fundamental step toward the discovery of novel genes associated with human disorders, especially when no genes are known to be associated with a specific disease. In this c...

DRMDA: deep representations-based miRNA-disease association prediction.

Journal of cellular and molecular medicine
Recently, microRNAs (miRNAs) are confirmed to be important molecules within many crucial biological processes and therefore related to various complex human diseases. However, previous methods of predicting miRNA-disease associations have their own d...