Biology subjects

Furlong, L. I.

Publications and source records attributed to Furlong, L. I..

4 recordsLinked to original sources

bioRxiv2026

DISGENET: Accelerating Data-Driven Discovery in Disease Genomics and Therapeutic Development

Precision medicine and therapeutic development rely on a comprehensive understanding of genotype-phenotype relationships, yet this information remains fragmented across diverse sources. DISGENET, established over 15 years ago, addresses this challenge by systematically integrating gene-disease, variant-disease, and disease-disease associations from authoritative databases and the literature. This major upgrade expands coverage with chemical and pharmacological annotations and integrates biobank and clinical data. An advanced natural language processing (NLP) pipeline captures emerging evidence with full provenance and contextual details, key to streamlining data-driven insights. DISGENET supports diverse users through multiple tools, including an intuitive web interface, a REST API, an R package, a Cytoscape app, and an AI assistant for natural language queries. Quarterly updates ensure data currency, while a sustainable freemium model provides free academic access and supports ongoing development. DISGENET aims to accelerate data-driven discoveries and advance precision medicine and drug development. The platform is accessible at https://www.disgenet.com. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/697749v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@1cf5b9aorg.highwire.dtl.DTLVardef@87004aorg.highwire.dtl.DTLVardef@1243b2borg.highwire.dtl.DTLVardef@1a8adab_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIDISGENET accelerates precision medicine via comprehensive genotype-phenotype data C_LIO_LIAccess to gene, variant, disease, and drug data in a single platform C_LIO_LIUp-to-date evidence with provenance and full context C_LIO_LISuite of tools to serve diverse research and clinical communities C_LIO_LISustainable freemium model ensures ongoing platform innovation. C_LI

bioinformatics↗

bioRxiv2020

Behavioural and transcriptomic characterization of the comorbidity between Alzheimer s disease and Major Depression

Major Depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimers disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown. In this regard, we investigated the cognitive and emotional responses in 3- and 6-month-old in APP/PSEN1-Tg mutant mice, before {beta}-amyloid plaques were detected. Then, we studied the deregulation of genes and pathways in prefrontal cortex, striatum, hippocampus and amygdala, using transcriptomic and functional data analysis. The results demonstrated that depressive-like and anxiety-like behaviours, as well as memory impairments are already present at 3-month-old together with the deregulation of several genes and gene sets, including components of the circadian rhythms, electronic transport chain and neurotransmission. Finally, DisGeNET GSEA provides translational support for common depregulated gene sets related to MD and AD. Altogether, the results demonstrate that MD could be an early manifestation of AD.

neuroscience↗

bioRxiv2018

Capturing variation impact on molecular interactions: the IMEx Consortium mutations data set

The current wealth of genomic variation data identified at the nucleotide level has provided us with the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that amino acid variation of a molecules sequence has on its molecular interactions is a key step towards connecting a full mechanistic characterization of nonsynonymous variation to cellular phenotype. Here we present an open access resource created by IMEx database curators over 14 years, featuring 28,000 annotations fully describing the effect of individual point sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data content is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website at www.ebi.ac.uk/intact/resources/datasets#mutationDs and is being enhanced with every monthly release.

bioinformatics↗

bioRxiv2018

Targeting comorbid diseases via network endopharmacology

The traditional drug discovery paradigm has shaped around the idea of \"one target, one disease\". Recently, it has become clear that not only it is hard to achieve single target specificity but also it is often more desirable to tinker the complex cellular network by targeting multiple proteins, causing a paradigm shift towards polypharmacology (multiple targets, one disease). Given the lack of clear-cut boundaries across disease (endo)phenotypes and genetic heterogeneity across patients, a natural extension to the current polypharmacology paradigm is targeting common biological pathways involved in diseases, giving rise to \"endopharmacology\" (multiple targets, multiple diseases). In this study, leveraging powerful network medicine tools, we describe a recipe for first, identifying common pathways pertaining to diseases and then, prioritizing drugs that target these pathways towards endopharmacology. We present proximal pathway enrichment analysis (PxEA) that uses the topology information of the network of interactions between disease genes, pathway genes, drug targets and other proteins to rank drugs for their interactome-based proximity to pathways shared across multiple diseases, providing unprecedented drug repurposing opportunities. As a proof of principle, we focus on nine autoimmune disorders and using PxEA, we show that many drugs indicated for these conditions are not necessarily specific to the condition of interest, but rather target the common biological pathways across these diseases. Finally, we provide the high scoring drug repurposing candidates that can target common mechanisms involved in type 2 diabetes and Alzheimers disease, two phenotypes that have recently gained attention due to the increased comorbidity among patients.

systems biology↗