AIMC Topic: Phenotype

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Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis: Prognostic and Therapeutic Implications.

Heart failure clinics
Heart failure with preserved ejection fraction (HFpEF) is characterized by a high rate of hospitalization and mortality (up to 84% at 5 years), which are similar to those observed for heart failure with reduced ejection fraction (HFrEF). These epidem...

Using graph convolutional neural networks to learn a representation for glycans.

Cell reports
As the only nonlinear and the most diverse biological sequence, glycans offer substantial challenges for computational biology. These carbohydrates participate in nearly all biological processes-from protein folding to viral cell entry-yet are still ...

High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales.

Emerging topics in life sciences
Agriculture has benefited greatly from the rise of big data and high-performance computing. The acquisition and analysis of data across biological scales have resulted in strategies modeling inter- actions between plant genotype and environment, mode...

Challenges for FAIR-compliant description and comparison of crop phenotype data with standardized controlled vocabularies.

Database : the journal of biological databases and curation
Crop phenotypic data underpin many pre-breeding efforts to characterize variation within germplasm collections. Although there has been an increase in the global capacity for accumulating and comparing such data, a lack of consistency in the systemat...

Predicting candidate genes from phenotypes, functions and anatomical site of expression.

Bioinformatics (Oxford, England)
MOTIVATION: Over the past years, many computational methods have been developed to incorporate information about phenotypes for disease-gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a data...

Application of deep learning methods in biological networks.

Briefings in bioinformatics
The increase in biological data and the formation of various biomolecule interaction databases enable us to obtain diverse biological networks. These biological networks provide a wealth of raw materials for further understanding of biological system...

Categorization of birth weight phenotypes for inclusion in genetic evaluations using a deep neural network.

Journal of animal science
Birth weight (BW) serves as a valuable indicator of the economically relevant trait of calving ease (CE), and erroneous data collection for BW could impact genetic evaluations for CE. The objective of the current study was to evaluate the use of deep...

The Human Phenotype Ontology in 2021.

Nucleic acids research
The Human Phenotype Ontology (HPO, https://hpo.jax.org) was launched in 2008 to provide a comprehensive logical standard to describe and computationally analyze phenotypic abnormalities found in human disease. The HPO is now a worldwide standard for ...

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...