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Selection, Genetic

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Using deep learning to identify recent positive selection in malaria parasite sequence data.

Malaria journal
BACKGROUND: Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and ...

Disease variant prediction with deep generative models of evolutionary data.

Nature
Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational...

A Deep-Learning Approach for Inference of Selective Sweeps from the Ancestral Recombination Graph.

Molecular biology and evolution
Detecting signals of selection from genomic data is a central problem in population genetics. Coupling the rich information in the ancestral recombination graph (ARG) with a powerful and scalable deep-learning framework, we developed a novel method t...

Robots as models of evolving systems.

Proceedings of the National Academy of Sciences of the United States of America
Experimental robobiological physics can bring insights into biological evolution. We present a development of hybrid analog/digital autonomous robots with mutable diploid dominant/recessive 6-byte genomes. The robots are capable of death, rebirth, an...

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Molecular plant
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by inco...

Deciphering signatures of natural selection via deep learning.

Briefings in bioinformatics
Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. However, it remains challenging to detect loci under complex spatially varying selection. We propose a deep learning...

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Bioinformatics (Oxford, England)
MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plan...

Construction and validation of a cuproptosis-related diagnostic gene signature for atrial fibrillation based on ensemble learning.

Hereditas
BACKGROUND: Atrial fibrillation (AF) is the most common type of cardiac arrhythmia. Nonetheless, the accurate diagnosis of this condition continues to pose a challenge when relying on conventional diagnostic techniques. Cell death is a key factor in ...

Tensor Decomposition-based Feature Extraction and Classification to Detect Natural Selection from Genomic Data.

Molecular biology and evolution
Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involve...

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers.

BMC genomic data
BACKGROUND: Genomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for ...