AIMC Topic: Phenotype

Clear Filters Showing 311 to 320 of 918 articles

Construction of genetic classification model for coronary atherosclerosis heart disease using three machine learning methods.

BMC cardiovascular disorders
BACKGROUND: Although the diagnostic method for coronary atherosclerosis heart disease (CAD) is constantly innovated, CAD in the early stage is still missed diagnosis for the absence of any symptoms. The gene expression levels varied during disease de...

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Nature genetics
Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient pho...

PDGNet: Predicting Disease Genes Using a Deep Neural Network With Multi-View Features.

IEEE/ACM transactions on computational biology and bioinformatics
The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing efficient and accurate computing approaches to predict disease genes. However, owing to the sparsenes...

Network biology and artificial intelligence drive the understanding of the multidrug resistance phenotype in cancer.

Drug resistance updates : reviews and commentaries in antimicrobial and anticancer chemotherapy
Globally with over 10 million deaths per year, cancer is the most transversal disease across countries, cultures, and ethnicities, affecting both developed and developing regions. Tumorigenesis is dynamically altered by distinct events and can be let...

Marker effects and heritability estimates using additive-dominance genomic architectures via artificial neural networks in Coffea canephora.

PloS one
Many methodologies are used to predict the genetic merit in animals and plants, but some of them require priori assumptions that may increase the complexity of the model. Artificial neural network (ANN) has advantage to not require priori assumptions...

Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards.

PloS one
Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause prob...

PhenoApt leverages clinical expertise to prioritize candidate genes via machine learning.

American journal of human genetics
In recent years, exome sequencing (ES) has shown great utility in the diagnoses of Mendelian disorders. However, after rigorous filtering, a typical ES analysis still involves the interpretation of hundreds of variants, which greatly hinders the rapi...

Insights into cell classification based on combination of multiple cellular mechanical phenotypes by using machine learning algorithm.

Journal of the mechanical behavior of biomedical materials
Although cellular elastic property (CEP, also known as cellular elastic modulus) has been frequently reported as a biomarker to distinguish some cancerous cells from their benign counterparts, it cannot be adopted as a universal hallmark to be applie...

Evidence for distinct neuro-metabolic phenotypes in humans.

NeuroImage
Advances in magnetic resonance imaging have shown how individual differences in the structure and function of the human brain relate to health and cognition. The relationship between individual differences and the levels of neuro-metabolites, however...