Alexandre Hild Aono

This study uses multiomic approaches to identify genetic variants and genes associated with sucrose accumulation in sugarcane, providing insights for breeding and biotechnology.

This study investigates how Sugarcane yellow leaf virus (SCYLV) affects sugarcane's transcriptomic defense responses to its aphid vector Melanaphis sorghi, identifying key gene pathways and regulatory hubs.

This study uses high-throughput phenotyping with UAVs and machine learning to assess soybean resistance to stink bugs and yield, finding that certain image features and models can predict traits but not resistance effectively.

The paper identifies three candidate genes for sugar accumulation in sugarcane using synteny analysis, BAC library screening, RNA-seq, and coexpression network analysis.

This study investigates the molecular response of Avicennia schaueriana to freezing stress, revealing differences between equatorial and subtropical populations.

This study uses genome-wide family prediction to identify markers associated with agronomic traits in Urochloa ruziziensis and links them to molecular mechanisms.

This paper develops the highest-density linkage map for hexaploid Urochloa humidicola, revealing genetic organization, apomixis, and polyploid origins using SNP markers.

The paper identifies molecular signatures associated with russet skin formation in Niagara grapevine by analyzing transcriptomic and histological differences between somatic variants.

This study identifies and characterizes 230 LRR-RLK genes in common bean, linking their expression patterns to anthracnose resistance.

The paper identifies SNPs and genes associated with stem volume in loblolly pine using GWAS and machine learning, revealing regulatory mechanisms and candidate genes for growth traits.

This paper develops a machine learning approach for predicting rubber tree stem circumference using molecular markers, achieving higher accuracy than traditional models in a single-environment scenario.

This paper develops a joint learning approach to improve genomic prediction in polyploid grasses, achieving over 50% accuracy improvements over traditional methods.

The paper compares cellulose degradation strategies in two Trichoderma harzianum strains using network analysis, revealing strain-specific regulatory differences involving XYR1 and CRE1.

The paper identifies gene expression patterns and regulatory mechanisms in cold-resistant rubber tree clones using RNA-seq and gene coexpression network analysis.

The paper was withdrawn due to changes in phenotypic focus, and no findings are presented.

This paper introduces an automated SNP-based method to identify contaminants in polyploid plant progenies using PCA, Mendelian segregation analysis, and clustering, validated on simulated and real data.

This study profiles the kinomes of sugarcane and sorghum, identifying PKs and their evolutionary relationships, and provides expression patterns and coexpression networks.

The paper uses machine learning to identify genomic regions associated with sugarcane brown rust resistance by analyzing SNP data from full-sib progeny.