Alfredo Iacoangeli

This study investigates the role of nuclear-encoded mitochondrial genes in ALS, finding enrichment in mitochondrial translation pathways and altered co-expression patterns, but no clear link between genetic risk and tissue expression changes.

This paper evaluates combining expression-based surrogate variables and genotype-based principal components in differential expression analysis, finding the combined approach improves replicability and biological recall in ALS datasets.

The paper identifies seven sex-dependent differentially methylated positions in ALS blood samples, suggesting sex-specific biological mechanisms in the disease.

This paper conducts a meta-analysis to determine the lower limit of ATXN2 repeat expansions associated with ALS risk, finding that ≥30 repeats define significant risk.

The paper uses molecular dynamics simulations to analyze SOD1 variants and finds that structural stability and conformational rigidity correlate with clinical severity in ALS.

The paper identifies FGF14 GAA repeat expansions in ALS patients, showing enrichment compared to controls and suggesting a broader pathogenic role in motor neuron disease.

The paper quantifies exosomal HERV-K transcripts in ALS patients and controls, finding elevated pol expression that could serve as a biomarker for treatment monitoring.

This paper presents a drug repurposing pipeline that integrates knowledge graphs and network medicine, benchmarked across diseases and applied to ALS, identifying 77 prioritized candidate drugs.

The paper evaluates gene expression-based ALS subtypes across brain and blood tissues using machine learning, finding three subtypes with consistent classification in both tissues.

This paper shows that trustworthy agentic genome interpretation requires executing validated skills as code rather than relying on models to reason or retrieve information, ensuring deterministic and auditable results.

The paper generates isogenic iPSC lines with a C9ORF72 G4C2 repeat expansion to model ALS and FTD, showing both loss- and gain-of-function pathologies.

This study benchmarks TE detection tools on long-read data, showing significant variation in performance and highlighting tool-specific strengths and limitations.

This study compares Oxford Nanopore long-read sequencing with Illumina short-read sequencing and microarrays across 14 human genomes, finding LRS has competitive accuracy for SNVs and superior performance for structural variants.

The paper calculates the risk of developing rare neurodegenerative diseases following genetic screening, showing low risks for some variants and emphasizing the need for high sensitivity and specificity.

This paper develops a deep learning model using SNP microarray data to predict ALS risk with high accuracy across diverse populations and genotyping platforms.

This study uses polygenic and transcriptional risk scores to analyze psychiatric and cognitive symptoms in Parkinson's disease, finding distinct associations between genetic and transcriptional risks and symptom progression.

The paper investigates oligogenic risk in ALS using genome sequencing data, finding that multiple rare variants in known ALS genes increase disease risk but not clinical outcomes.

This study uses GWAS summary statistics to identify shared genetic loci across neuropsychiatric diseases and finds HLA region associations with potential shared causal variants.

The paper identifies sex-associated DNA methylation differences in ALS using a large-scale epigenome-wide association study and links them to survival outcomes.

The paper integrates inflammation, cell-type composition, and immune signatures in MDS to identify prognostic subgroups using MOFA, finding that high inflammation correlates with better prognosis while SRSF2 mutations are linked to poor outcomes.

This study uses GWAS and functional genomics to identify ALS risk genes, repurpose drugs, and improve risk prediction using polytranscriptomic scores.

The paper introduces predicTTE, a tool for time-to-event prediction in neurological diseases, demonstrating its utility in genetic discovery and personalized medicine.

The paper identifies sex-specific risk loci for ALS, including MEF2C, which is upregulated in female ALS motor neurons, and an X-chromosome locus at IL1RAPL2.

The paper introduces a method to compress genetic data using autoencoders that leverage linkage disequilibrium structure, achieving high reconstruction accuracy while reducing data size.

This study analyzes genetic and clinical data from PLS patients to assess the role of genetic testing and identify potential disease-associated variants.

This study investigates the genetic relationship between depression symptoms and Alzheimer's disease using GWAS and finds significant genetic correlations but no causal link, suggesting proxy cases may drive the overlap.

Mutafy identifies high-quality mutant protein structures from the PDB for given human genes, annotates them with biomedical data, and provides interactive visualization.

The paper analyzes CAV1 and CAV2 expression in ALS brain tissue and finds higher expression in ALS patients, associated with increased survival and slower disease progression.

The paper evaluates bioinformatics tools for detecting human endogenous retroviral insertions in short-read genome sequencing data, finding performance variability across datasets and recommending a consensus approach.

This study uses molecular dynamics and clinical data to show structural and dynamic differences between WTL and MBR SOD1 variants, linking these to distinct ALS clinical outcomes.