bioRxiv Science⌕ Search

bioRxiv · 10.1101/2022.07.26.501105

REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data

Abstract

In the recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aim is to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ayuso Munoz, A., Ugarte Carro, E., Prieto Santamaria, L., Otero Carrasco, B., Menasalvas Ruiz, E., Perez Gallardo, Y., Rodriguez-Gonzalez, A.. 2022-07-27. REDIRECTION: Generating drug repurposing hypotheses using link prediction with DISNET data. https://doi.org/10.1101/2022.07.26.501105

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

Uncovering Viral Signals Across Cell Populations in Colorectal Cancer

Viral sequences have been detected in colorectal cancer, but their cellular and anatomical distribution remains poorly resolved. We aimed to establish a cell-type-resolved map of viral reference-associated RNA signals across colorectal cancer, tumour-adjacent normal tissue, and healthy colon. We repurposed human-unmapped reads from single-cell RNA sequencing libraries generated in ten publicly available studies. We retained cell-barcode and unique molecular identifier information, removed duplicate molecules, and aligned reads to a curated human-associated viral reference using three aligners. Viral signals were analysed across cell types, tissue conditions, and colorectal segments. We used participant-grouped repeated cross-validation to evaluate tissue-condition classification and screened an independent paired colorectal cancer proteomics cohort for compatible peptide matches. The study included 547 libraries from 112 participants, including 49 with matched colorectal cancer and tumour-adjacent normal samples, and approximately 415,000 cells spanning ten major cell populations. Viral reference-associated RNA signals differed across epithelial, immune, stromal, and vascular compartments, tissue conditions, and anatomical regions of the colon. Of 318 independent healthy-reference viral-load comparisons, seven involving six virus-cell-type groups remained significant after Benjamini-Hochberg correction, including plasma-cell HHV-7 and HHV-6B and mast-cell HCV-2. Cell-type-resolved viral features distinguished colorectal cancer, tumour-adjacent normal tissue, and healthy colon in participant-grouped validation; random forest achieved a macro-AUC of 0.957 (SD 0.028), with HERV-K113 as the leading quantitative feature. An independent proteomic screen identified peptide matches compatible with 20 candidate viral groups. We established a cell-type-resolved map of viral reference-associated RNA signals across colorectal tissue conditions and anatomical regions. By identifying candidate viral signals in epithelial and immune compartments, this map provides a basis for investigating virus-host pathways involved in tumour biology, immune regulation, and antigen-presentation states, and could help prioritise future combination strategies pairing virus-directed or virus-mimetic approaches with standard treatment or immune checkpoint blockade.

bioinformatics↗

Statistical significance testing for spatial domain detection in spatial transcriptomics

In spatial transcriptomics, domain-detection methods group platform-specific spatial measurement units--cells, bins or multicellular spots--into spatial domains for comparing molecular programs, cellular composition and interactions among tissue regions. However, the number of candidate domains detected in the same tissue can vary with user-specified parameters or model-selection criteria. Whether candidate domains should be merged can be framed as a test of a single-domain null hypothesis, under which each gene has constant mean expression across all spatial units in the combined region. However, testing candidate domains on the same gene-expression data used to detect them may induce post-selection bias and yield anti-conservative p-values, increasing the risk of oversegmentation. To address these challenges, we introduce SigDoTS (statistical Significance testing for Domain deTection in Spatial transcriptomics), a hypothesis-testing framework for spatial domain detection. SigDoTS uses hierarchical testing to determine which candidate domains to retain or merge, with adjustment for family-wise error control. It accounts for gene-gene covariance and spatial dependence and computes p-values by rerunning the upstream domain-detection pipeline on datasets simulated under the fitted single-domain null. In simulations, SigDoTS controlled the type I error rate at the root node under the single-domain null and improved recovery of the true spatial domains when multiple domains were present. In human dorsolateral prefrontal cortex, SigDoTS retained superficial and deep Layer 6 domains but merged Layer 3 candidate domains across different initial numbers of candidate domains. In ovarian and colorectal cancers, SigDoTS retained epithelial domains with different gene-expression programs and local immune environments, together with immune, stromal and vascular domains.

bioinformatics↗

ASD Neuro Agentic (ANA): An Agentic Framework for Feature Discovery and Cross Site Generalization in Autism fMRI Classification

Classifiers of autism spectrum disorder (ASD) from resting state functional MRI rarely generalize across sites, and many reported accuracies are inflated by data leakage.We show that an agentic artificial intelligence framework, ASD Neuro Agentic (ANA), whose language model agents propose features from the literature and researcher input while fixed tools perform every calculation, can search auditably for site independent classifiers while controlling the leakage. In 1,028 participants from 17 ABIDE I centers, ANA tested 71 pipelines and selected a ComBat harmonized ensemble of tangent space connectivity models from six parcellations and 98 public connectivity derivatives. In leave one center out testing, the ensemble reached an area under the curve of 0.814 (95% CI, 0.773 to 0.841), outperforming ASD DiagNet and ASD SAENet. Predictive connectivity was mainly reduced in ASD. Agentic AI offers a systematic and efficient approach to exploring, evaluating, and identifying pipelines as model selection grows complex.

bioinformatics↗