AIMC Topic: Polymorphism, Single Nucleotide

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Protocol for Epistasis Detection with Machine Learning Using GenEpi Package.

Methods in molecular biology (Clifton, N.J.)
To develop medical treatments and prevention, the association between disease and genetic variants needs to be identified. The main goal of genome-wide association study (GWAS) is to discover the underlying reason for vulnerability to disease and uti...

Brief Survey on Machine Learning in Epistasis.

Methods in molecular biology (Clifton, N.J.)
In biology, the term "epistasis" indicates the effect of the interaction of a gene with another gene. A gene can interact with an independently sorted gene, located far away on the chromosome or on an entirely different chromosome, and this interacti...

PheMap: a multi-resource knowledge base for high-throughput phenotyping within electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Developing algorithms to extract phenotypes from electronic health records (EHRs) can be challenging and time-consuming. We developed PheMap, a high-throughput phenotyping approach that leverages multiple independent, online resources to s...

Prediction of survival rate and effect of drugs on cancer patients with somatic mutations of genes: An AI-based approach.

Chemical biology & drug design
The causal role of somatic mutation and its interrelationship with gene expression profile during tumor development has already been observed, which plays a major role to decide the cancer grades and overall survival. Accurate and robust prediction o...

Ensemble-Based Somatic Mutation Calling in Cancer Genomes.

Methods in molecular biology (Clifton, N.J.)
Identification of somatic mutations in tumor tissue is challenged by both technical artifacts, diverse somatic mutational processes, and genetic heterogeneity in the tumors. Indeed, recent independent benchmark studies have revealed low concordance b...

Convolutional Neural Network Visualization for Identification of Risk Genes in Bipolar Disorder.

Current molecular medicine
BACKGROUND: Bipolar disorder (BD) is a type of chronic emotional disorder with a complex genetic structure. However, its genetic molecular mechanism is still unclear, which makes it insufficient to be diagnosed and treated.

Using Machine Learning to Identify True Somatic Variants from Next-Generation Sequencing.

Clinical chemistry
BACKGROUND: Molecular profiling has become essential for tumor risk stratification and treatment selection. However, cancer genome complexity and technical artifacts make identification of real variants a challenge. Currently, clinical laboratories r...

Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Nucleic acids research
Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional fe...

Identification of disease-associated loci using machine learning for genotype and network data integration.

Bioinformatics (Oxford, England)
MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wid...