AIMC Topic: Genetic Association Studies

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Whole-genome deep-learning analysis identifies contribution of noncoding mutations to autism risk.

Nature genetics
We address the challenge of detecting the contribution of noncoding mutations to disease with a deep-learning-based framework that predicts the specific regulatory effects and the deleterious impact of genetic variants. Applying this framework to 1,7...

New Deep Learning Genomic-Based Prediction Model for Multiple Traits with Binary, Ordinal, and Continuous Phenotypes.

G3 (Bethesda, Md.)
Multiple-trait experiments with mixed phenotypes (binary, ordinal and continuous) are not rare in animal and plant breeding programs. However, there is a lack of statistical models that can exploit the correlation between traits with mixed phenotypes...

Building the drug-GO function network to screen significant candidate drugs for myasthenia gravis.

PloS one
Myasthenia gravis (MG) is an autoimmune disease. In recent years, considerable evidence has indicated that Gene Ontology (GO) functions, especially GO-biological processes, have important effects on the mechanisms and treatments of different diseases...

Adaptive multi-view multi-label learning for identifying disease-associated candidate miRNAs.

PLoS computational biology
Increasing evidence has indicated that microRNAs(miRNAs) play vital roles in various pathological processes and thus are closely related with many complex human diseases. The identification of potential disease-related miRNAs offers new opportunities...

Deep Collaborative Filtering for Prediction of Disease Genes.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate prioritization of potential disease genes is a fundamental challenge in biomedical research. Various algorithms have been developed to solve such problems. Inductive Matrix Completion (IMC) is one of the most reliable models for its well-est...

Interpretable genotype-to-phenotype classifiers with performance guarantees.

Scientific reports
Understanding the relationship between the genome of a cell and its phenotype is a central problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with great challenges for machine learning algorithms that limit their use i...

Retrospective Data Analysis of the Influence of Age and Sex on TPMT Activity and Its Phenotype-Genotype Correlation.

The journal of applied laboratory medicine
BACKGROUND: Therapeutic efficacy and toxicity of thiopurine drugs (used as anticancer and immunosuppressant agents) are affected by thiopurine S-methyltransferase (TPMT) enzyme activity. genotype and/or phenotype is used to predict the risk for adve...

Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs.

International journal of molecular sciences
Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNA...

Increased risk of group B Streptococcus causing meningitis in infants with mannose-binding lectin deficiency.

Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
OBJECTIVES: To evaluate the association of mannose-binding lectin (MBL) deficiency with susceptibility and clinical features of group B Streptococcus (GBS) causing meningitis in Chinese infants.

Identification of Novel Genes in Human Airway Epithelial Cells associated with Chronic Obstructive Pulmonary Disease (COPD) using Machine-Based Learning Algorithms.

Scientific reports
The aim of this project was to identify candidate novel therapeutic targets to facilitate the treatment of COPD using machine-based learning (ML) algorithms and penalized regression models. In this study, 59 healthy smokers, 53 healthy non-smokers an...