AIMC Topic: Models, Genetic

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The chronODE framework for modelling multi-omic time series with ordinary differential equations and machine learning.

Nature communications
Many genome-wide studies capture isolated moments in cell differentiation or organismal development. Conversely, longitudinal studies provide a more direct way to study these kinetic processes. Here, we present an approach for modeling gene-expressio...

AdaptiveGS: an explainable genomic selection framework based on adaptive stacking ensemble machine learning.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
We developed an adaptive and unified stacking genomic selection framework and designed a model interpretation strategy to identify the candidate significant SNPs of target traits. Genomic selection (GS) is an important technique in modern molecular b...

In silico prediction of variant effects: promises and limitations for precision plant breeding.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Sequence-based AI models show great potential for prediction of variant effects at high resolution, but their practical value in plant breeding remains to be confirmed through rigorous validation studies. Plant breeding has traditionally relied on ph...

Evaluating the representational power of pre-trained DNA language models for regulatory genomics.

Genome biology
BACKGROUND: The emergence of genomic language models (gLMs) offers an unsupervised approach to learning a wide diversity of cis-regulatory patterns in the non-coding genome without requiring labels of functional activity generated by wet-lab experime...

Multi-task genomic prediction using gated residual variable selection neural networks.

BMC bioinformatics
BACKGROUND: The recent development of high-throughput sequencing techniques provide massive data that can be used in genome-wide prediction (GWP). Although GWP is effective on its own, the incorporation of traditional polygenic pedigree information i...

Breeding perspectives on tackling trait genome-to-phenome (G2P) dimensionality using ensemble-based genomic prediction.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Trait Genome-to-Phenome (G2P) dimensionality and "breeding context" combine to influence the realised prediction skill of different whole genome prediction (WGP) methods. Theory and empirical evidence both suggest there is likely to be "No Free Lunch...

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...

Assessing simulation-based supervised machine learning for demographic parameter inference from genomic data.

Heredity
The ever-increasing availability of high-throughput DNA sequences and the development of numerous computational methods have led to considerable advances in our understanding of the evolutionary and demographic history of populations. Several demogra...