AIMC Topic: Epistasis, Genetic

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Self-supervised deep learning of gene-gene interactions for improved gene expression recovery.

Briefings in bioinformatics
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to gain biological insights at the cellular level. However, due to technical limitations of the existing sequencing technologies, low gene expression values are often omitted, lead...

Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.

Journal of Alzheimer's disease : JAD
BACKGROUND: Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail t...

Accurately modeling biased random walks on weighted networks using node2vec.

Bioinformatics (Oxford, England)
MOTIVATION: Accurately representing biological networks in a low-dimensional space, also known as network embedding, is a critical step in network-based machine learning and is carried out widely using node2vec, an unsupervised method based on biased...

Deep learning of gene relationships from single cell time-course expression data.

Briefings in bioinformatics
Time-course gene-expression data have been widely used to infer regulatory and signaling relationships between genes. Most of the widely used methods for such analysis were developed for bulk expression data. Single cell RNA-Seq (scRNA-Seq) data offe...

Revisiting genome-wide association studies from statistical modelling to machine learning.

Briefings in bioinformatics
Over the last decade, genome-wide association studies (GWAS) have discovered thousands of genetic variants underlying complex human diseases and agriculturally important traits. These findings have been utilized to dissect the biological basis of dis...

Deep representation learning improves prediction of LacI-mediated transcriptional repression.

Proceedings of the National Academy of Sciences of the United States of America
Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of ...

Machine Learning to Identify Gene Interactions from High-Throughput Mutant Crosses.

Methods in molecular biology (Clifton, N.J.)
Advances in molecular genetics through high-throughput gene mutagenesis and genetic crossing have enabled gene interaction mapping across whole genomes. Detecting gene interactions in even small microbial genomes relies on measuring growth phenotypes...

Epistasis Analysis: Classification Through Machine Learning Methods.

Methods in molecular biology (Clifton, N.J.)
Complex disease is different from Mendelian disorders. Its development usually involves the interaction of multiple genes or the interaction between genes and the environment (i.e. epistasis). Although the high-throughput sequencing technologies for ...

Epistasis Detection Based on Epi-GTBN.

Methods in molecular biology (Clifton, N.J.)
Epistasis detection is a hot topic in bioinformatics due to its relevance to the detection of specific phenotypic traits and gene-gene interactions. Here, we present a step-by-step protocol to apply Epi-GTBN, a machine learning-based method based on ...

A Belief Degree-Associated Fuzzy Multifactor Dimensionality Reduction Framework for Epistasis Detection.

Methods in molecular biology (Clifton, N.J.)
Epistasis is a challenge in prediction, classification, and suspicion of human genetic diseases. Many technologies, methods, and tools have been developed for epistasis detection. Multifactor dimensionality reduction (MDR) is the method commonly used...