AIMC Topic: Genome, Plant

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Challenges and Opportunities with CRISPR-Based Genome Editing in Legume Crops.

Functional & integrative genomics
Over the last couple of decades, tremendous progress has been made in legume genomics. Genomics information generated for legume crops is being explored through molecular breeding and transgenic approaches. However, the gap between knowledge generati...

Bayesian neural networks for genomic prediction: uncertainty quantification and SNP interpretation with SHAP and GWAS.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potenti...

MLDeCNV: A machine learning approach for predicting copy number variation types in plant genomes.

Computers in biology and medicine
Copy number variations (CNVs) play a crucial role in shaping genetic diversity and influencing various plant traits. However, existing methods for CNV characterization often face challenges due to the complexity and repetitive nature of plant genomes...

Impact of U2-type introns on splice site prediction in A. thaliana species using deep learning.

BMC bioinformatics
BACKGROUND: Splice site prediction in plant genomes poses substantial challenges that can be addressed using deep learning models. U2-type introns are especially useful for such studies given their ubiquity in plant genomes and the availability of ri...

Obscured-ensemble models for genomic prediction.

PloS one
Genomic Prediction (GP) uses dense whole-genome marker sets from lines of a crop to predict agronomic traits for untested genotypes. In recent years, deep learning (DL) approaches for genomic prediction have demonstrated state-of-the-art results. How...

Harnessing multi-omics and genome-editing technologies for climate-resilient agriculture: bridging AI-driven insights with sustainable crop improvement.

Plant molecular biology
Environmental challenges such as drought, salinity, heavy metal contamination, and nutrient deficiencies threaten global agricultural productivity and food security. These stressors drastically reduce crop yields, necessitating innovative solutions. ...

A comparative study highlights superiority of LSTM in crop genomic prediction.

Planta
We systematically evaluated three key determinants affecting prediction accuracy and the algorithm performance differences based on fifteen state-of-the-art GP methods, and found LSTM suitable for capturing additive and epistatic effects. Genomic pre...

Black pepper knowledge base (BlackPepKB): a centralized web resource for functional genomics of black pepper (Piper nigrum L.).

BMC genomics
BACKGROUND: Black pepper (Piper nigrum L.) is a highly valued spice crop with significant economic, medicinal, and cultural importance. While genomic and transcriptomic data for black pepper have rapidly accumulated in recent years, there is currentl...

Next-generation molecular breeding tools to harness higher genetic gains in sugarcane.

Planta
Next-generation molecular tools with AI integration can accelerate genetic gain in sugarcane by enhancing variation, accuracy, and efficiency, enabling rapid development of high-yielding, high-quality, and climate-resilient varieties. Enhancing genet...

Integrative genomics and genetics from evolutionary insights to precision breeding in peanuts (Arachis Hypogaea L.).

Functional & integrative genomics
Peanut (Arachis hypogaea L.), a globally important oilseed crop, increasingly challenged by rising edible oil demands as well as biotic and abiotic stresses. This review synthesizes recent advances in peanut genomics, evolutionary biology, and breedi...