João Pedro de Magalhães

This study uses spike-in normalization to analyze age-related changes in gene expression across mouse cell types, finding decreased mRNA content in non-immune cells and increased in immune cells, along with reduced gene expression and shared regulatory processes between ageing and proliferation arrest.

The paper identifies two gene interaction mechanisms linking ageing and ageing-related diseases using network analysis and machine learning.

This study identifies genetic loci affecting lifespan dynamics, sex differences, and body weight trade-offs in mice using an actuarial approach.

The paper develops a toolkit to measure multiple ageing hallmarks in human blood and tissue samples, identifying age-related changes in immune cells, senescence, and mitochondrial function.

The paper evaluates the DrugAge database, finding poor reporting quality and inconsistent effect sizes in anti-aging preclinical studies across species.

The paper updates the DrugAge database with mouse lifespan data, finds weight loss correlates with lifespan extension in males, and highlights sex differences in drug responses.

This study uses machine learning to predict lifespan-extending compounds in mice by analyzing drug-target annotations, identifying key receptors and pathways associated with longevity.

The paper introduces Gen-DNA-TCN, a method for generating functional yeast promoter sequences using deep learning, and shows it produces diverse and functional sequences with similar transcription factor binding sites.

The paper identifies senescent cell heterogeneity by analyzing stress response enrichment in bulk RNA-seq data and proposes a model based on Stress Response Modules.

This study identifies age- and caloric restriction-associated miRNA expression changes in rat skeletal muscle and their functional impacts on muscle cell processes.

The paper assigns stemness scores to human tissues using machine learning and finds a general decline in stemness with age, except in the uterus, and links stemness to proliferation and senescence.

This study uses RNA sequencing to identify gene expression changes in aged muscle and how caloric restriction affects these changes, revealing key pathways and hub genes involved in sarcopenia.

The paper updates six databases in the Human Ageing Genomic Resources (HAGR) focusing on genes, drugs, and genetic variants related to ageing and longevity.

This paper identifies epigenetic loci with age-correlated variance, introduces a deep-learning model to predict age from variance, and proposes a theory of systemic ageing through feedback deregulation.

This study identifies a loss of cell identity in ageing and cancer using transcriptomic data, showing distinct patterns in gene expression associated with patient survival.

The paper uses machine learning to predict lifespan-extending compounds in C. elegans using biological features from DrugAge data, achieving 82% and 80% accuracy with GO terms and protein interactors.

The paper introduces m6A-TSHub, a platform that provides m6A methylation sites and mutations across 23 human tissues and 25 tumor conditions, enabling tissue-specific analysis of m6A regulation.

The paper identifies genes and enhancers associated with lifespan and body mass in great apes through evolutionary and molecular analysis.

The paper introduces scAgeCom, an atlas of age-related intercellular communication changes in 23 mouse tissues, using the scDiffCom package to analyze single-cell RNA-seq data from two consortia.

This paper uses machine learning to classify ageing-related genes as CR-related or not based on various predictive features, finding that curated knowledge features are most predictive.

This study presents the first genome annotation of a capuchin monkey and identifies genes under positive selection related to longevity, brain development, and adaptation to diverse environments.

This study identifies common ageing transcriptomic signatures across mammalian tissues using meta-analysis and machine learning, revealing shared functional changes and network properties.

GeneFriends 2021 updates co-expression networks for human and mouse genes using RNA-seq data from 46,475 and 34,322 samples, respectively.

SynergyAge curates genetic and lifespan data from animal models with multiple longevity-modulating interventions to explore synergistic and antagonistic gene interactions.

This study investigates factors influencing gene length in the human genome, finding correlations with SNPs, introns, protein size, and tissue-specific expression, and linking gene length to functional roles in development and disease.

The paper curates a database of senescence-associated genes and performs systems biology analyses to identify novel regulators and pathways of cellular senescence.

PICKER-HG is a web server that uses random forests to classify human genes into categories based on user-provided annotations and outputs classification rules.

The paper identifies 'unknown unknowns' in genetics, showing that larger, more diverse genes are more likely to be associated with traits, highlighting vast genetic information gaps.

This paper updates the Human Ageing Genomic Resources (HAGR), presenting its five databases and their functionalities for studying ageing and longevity.