Machine learning applications on intratumoral heterogeneity in glioblastoma using single-cell RNA sequencing data.

Artificial intelligence is revolutionizing all fields that affect people's lives and health. One of the most critical applications is in the study of tumors. It is the case of glioblastoma (GBM) that has behaviors that need to be understood to develop effective therapies.

Inpactor2: a software based on deep learning to identify and classify LTR-retrotransposons in plant genomes.

LTR-retrotransposons are the most abundant repeat sequences in plant genomes and play an important role in evolution and biodiversity. Their characterization is of great importance to understand their dynamics. However, the identification and classification of these elements remains a challenge today.

Analysis of fruit ripening in Theobroma cacao pod husk based on untargeted metabolomics.

The pod husk of Theobroma cacao (CPH) plays an important agronomical role, as its appearance is used as indicator of ripening, guiding the farmers in the harvest process. Cacao harvesting is not a standardized practice because farmers harvest between six up to eight months from flowering, guided by pod's color and shape. The mixture of cacao beans from different ripening stages (RS), negatively affecting the quality and price of grain.

Automatic curation of LTR retrotransposon libraries from plant genomes through machine learning.

Transposable elements are mobile sequences that can move and insert themselves into chromosomes, activating under internal or external stimuli, giving the organism the ability to adapt to the environment. Annotating transposable elements in genomic data is currently considered a crucial task to understand key aspects of organisms such as phenotype variability, species evolution, and genome size, among others. Because of the way they replicate, LTR retrotransposons are the most common transposable elements in plants, accounting in some cases for up to 80% of all DNA information.

-mer-based machine learning method to classify LTR-retrotransposons in plant genomes.

Every day more plant genomes are available in public databases and additional massive sequencing projects (i.e., that aim to sequence thousands of individuals) are formulated and released.