AIMC Topic: Phenotype

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ChronoRoot: High-throughput phenotyping by deep segmentation networks reveals novel temporal parameters of plant root system architecture.

GigaScience
BACKGROUND: Deep learning methods have outperformed previous techniques in most computer vision tasks, including image-based plant phenotyping. However, massive data collection of root traits and the development of associated artificial intelligence ...

Thousands of induced germline mutations affecting immune cells identified by automated meiotic mapping coupled with machine learning.

Proceedings of the National Academy of Sciences of the United States of America
Forward genetic studies use meiotic mapping to adduce evidence that a particular mutation, normally induced by a germline mutagen, is causative of a particular phenotype. Particularly in small pedigrees, cosegregation of multiple mutations, occasiona...

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...