AIMC Topic: Genotype

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Estimation of spatial demographic maps from polymorphism data using a neural network.

Molecular ecology resources
A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic ...

Characterizing feral swine movement across the contiguous United States using neural networks and genetic data.

Molecular ecology
Globalization has led to the frequent movement of species out of their native habitat. Some of these species become highly invasive and capable of profoundly altering invaded ecosystems. Feral swine (Sus scrofa × domesticus) are recognized as being a...

An initial exploration of machine learning for establishing associations between genetic markers and THC levels in Cannabis sativa samples.

Forensic science international. Genetics
Cannabis sativa, a globally commercialized plant used for medicinal, food, fiber production, and recreation, necessitates effective identification to distinguish legal and illegal varieties in forensic contexts. This research utilizes multivariate st...

Artificial intelligence in plant breeding.

Trends in genetics : TIG
Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scien...

Enhancing schizophrenia phenotype prediction from genotype data through knowledge-driven deep neural network models.

Genomics
This article explores deep learning model design, drawing inspiration from the omnigenic model and genetic heterogeneity concepts, to improve schizophrenia prediction using genotype data. It introduces an innovative three-step approach leveraging neu...

Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with hi...

Genome composition-based deep learning predicts oncogenic potential of HPVs.

Frontiers in cellular and infection microbiology
Human papillomaviruses (HPVs) account for more than 30% of cancer cases, with definite identification of the oncogenic role of viral and genes. However, the identification of high-risk HPV genotypes has largely relied on lagged biological explorati...

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

ACS nano
Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights in...

Machine learning for genomic and pedigree prediction in sugarcane.

The plant genome
Sugarcane (Saccharum spp.) plays a crucial role in global sugar production; however, the efficiency of breeding programs has been hindered by its heterozygous polyploid genomes. Considering non-additive genetic effects is essential in genome predicti...