AIMC Topic: Phenotype

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Morphological traits and machine learning for genetic lineage prediction of two reef-building corals.

PloS one
Integrating multiple lines of evidence that support molecular taxonomy analysis has proven to be a robust method for species delimitation in scleractinian corals. However, morphology often conflicts with genetic approaches due to high phenotypic plas...

Generative prediction of causal gene sets responsible for complex traits.

Proceedings of the National Academy of Sciences of the United States of America
The relationship between genotype and phenotype remains an outstanding question for organism-level traits because these traits are generally . The challenge arises from complex traits being determined by a combination of multiple genes (or loci), whi...

Toward a general framework for AI-enabled prediction in crop improvement.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
A theoretical framework for AI and ensembled prediction for crop improvement is introduced and demonstrated using the logistic map. Symbolic/sub-symbolic AI-based prediction can increase predictive skill with increase in system complexity. The curse ...

Generative AI for predictive breeding: hopes and caveats.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Among the broad area of artificial intelligence (AI), generative AI algorithms have emerged as a revolutionary technology able to produce highly realistic 'synthetic' data, akin to standard simulation but with fewer contraints. The main focus of gene...

In situ foliar augmentation of multiple species for optical phenotyping and bioengineering using soft robotics.

Science robotics
Precision agriculture aims to increase crop yield while reducing the use of harmful chemicals, such as pesticides and excess fertilizer, using minimal, tailored interventions. However, these strategies are limited by factors such as sensor quality, w...

What does evolution make? Learning in living lineages and machines.

Trends in genetics : TIG
How does genomic information unfold, to give rise to self-constructing living organisms with problem-solving capacities at all levels of organization? We review recent progress that unifies work in developmental genetics and machine learning (ML) to ...

PhenoDP: leveraging deep learning for phenotype-based case reporting, disease ranking, and symptom recommendation.

Genome medicine
BACKGROUND: Current phenotype-based diagnostic tools often struggle with accurate disease prioritization due to incomplete phenotypic data and the complexity of rare disease presentations. Additionally, they lack the ability to generate patient-cente...

Epistasis regulates genetic control of cardiac hypertrophy.

Nature cardiovascular research
Although genetic variant effects often interact nonadditively, strategies to uncover epistasis remain in their infancy. Here we develop low-signal signed iterative random forests to elucidate the complex genetic architecture of cardiac hypertrophy, u...

Powdery mildew resistance prediction in Barley (Hordeum Vulgare L) with emphasis on machine learning approaches.

Scientific reports
By employing machine-learning models, this study utilizes agronomical and molecular features to predict powdery mildew disease resistance in Barley (Hordeum Vulgare L). A 130-line F8-F9 barley population caused Badia and Kavir to grow at the Gonbad K...

Predicting genetic merit in Harnali sheep using machine learning techniques.

Tropical animal health and production
Machine learning techniques offer promising avenues for enhancing animal breeding programs by leveraging genomic and phenotypic data to predict valuable traits accurately. In this study, we evaluated seven machine learning algorithms viz., K-nearest ...