José Juan Almagro Armenteros

This study identifies tissue-specific lncRNA responses to endurance exercise in rats, revealing sex differences and potential therapeutic implications.

A ProtT5-embedding, ConvNext-plus-attention ensemble predicts human pathogenic capacity from whole bacterial genomes, outperforming prior predictors on species absent from training and flagging influential proteins.

The paper identifies tissue-specific transcriptional regulatory programs in rats undergoing endurance exercise training by integrating chromatin accessibility, DNA methylation, and gene expression data.

The paper introduces CytoTRACE 2, a deep learning framework to assess cell potency from scRNA-seq data, showing improved performance across multiple datasets and applications.

The paper identifies 46,802 enhancers and 164 psychiatric disorder variants with regulatory effects in the developing cortex and organoids using massively parallel reporter assays and deep learning.

The paper processes single-nuclei multi-omics data from 388 human brains to identify regulatory elements and build cell-type networks, revealing changes in aging and neuropsychiatric disorders.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

This study examines how endurance exercise training affects rat subcutaneous white adipose tissue, revealing sex-specific molecular responses and differences in adiposity and metabolism.

GraphPart partitions biological sequences to avoid closely related sequences in different partitions, retaining more sequences than reduction methods while maintaining homology separation quality.

This study profiles multi-omic changes in multiple tissues of rats during endurance exercise training, identifying molecular adaptations and sex-specific responses across time.

Combining an ESM-1b encoder with a conditional random field decoder, DeepTMHMM classifies alpha-helical and beta-barrel transmembrane proteins and their topology, outperforming most compared tools, including on signal-peptide cleavage sites.

SignalP 6.0 predicts all five types of signal peptides with improved accuracy and applies to metagenomic data.

NetSolP predicts protein solubility in E. coli from sequence using transformers, achieving state-of-the-art performance with reduced dataset bias.

The paper develops a machine learning model to predict recombinant protein expression in Bacillus subtilis using deep protein representations, achieving moderate performance for prioritizing expression candidates.

This paper introduces a deep learning method using variational autoencoders to improve microbial genome binning from metagenomic data, achieving higher precision and completeness in reconstructed bins.

The paper introduces NetGPI, a new method for predicting GPI-anchored proteins using recurrent neural networks with attention mechanisms, achieving better performance than existing methods.

The paper analyzes properties of protein datasets for language modelling and evaluates n-gram and RNN models, finding that organism domain and data quality affect performance while completeness has little impact.

The paper introduces TargetP 2.0, a deep learning method to identify N-terminal sorting signals in proteins, revealing insights into the importance of the second residue in targeting peptides.