Biology subjects

Sysoev, O.

Publications and source records attributed to Sysoev, O..

3 recordsLinked to original sources

bioRxiv2025

Regulation Flow Analysis discovers molecular mechanisms of action from large knowledge databases

Drug development is a long and expensive process, with only a small fraction of potential drugs being finally approved. The challenge of drug development is rooted in our limited understanding of biological systems and the disease processes that drugs are trying to modulate. We propose a novel method, called Regulation Flow Analysis (RFA), which is based on the principles of biological regulation, causal graphs, and graph flow. RFA is able to generate causal hypotheses of mechanism of action, using large Knowledge Graphs (KG) of molecular regulation. Our numerical experiments demonstrate that the generated hypotheses, in the form of regulation pathways, often summarize mechanisms of drug action in a manner both understandable and actionable. Thus, RFA can greatly improve our understanding of the biological processes underlying health and disease, and therefore substantially facilitate drug development. Our examples illustrate how RFA recovers known mechanisms of action and can be used for target and biomarker discovery and validation.

bioinformatics↗

bioRxiv2023

scDrugPrio: A framework for the analysis of single-cell transcriptomics to address multiple problems in precision medicine in immune-mediated inflammatory diseases

BackgroundIneffective drug treatment is a major problem for many patients with immune-mediated inflammatory diseases (IMIDs). Important reasons are the lack of systematic solutions for drug prioritisation and repurposing based on characterisation of the complex and heterogeneous cellular and molecular changes in IMIDs. MethodsHere, we propose a computational framework, scDrugPrio, which constructs network models of inflammatory disease based on single-cell RNA sequencing (scRNA-seq) data. scDrugPrio constructs detailed network models of inflammatory diseases that integrate information on cell type-specific expression changes, altered cellular crosstalk and pharmacological properties for the selection and ranking of thousands of drugs. ResultsscDrugPrio was developed using a mouse model of antigen-induced arthritis and validated by improved precision/recall for approved drugs, as well as extensive in vitro, in vivo, and in silico studies of drugs that were predicted, but not approved, for the studied diseases. Next, scDrugPrio was applied to multiple sclerosis, Crohns disease, and psoriatic arthritis, further supporting scDrugPrio through prioritisation of relevant and approved drugs. However, in contrast to the mouse model of arthritis, great interindividual cellular and gene expression differences were found in patients with the same diagnosis. Such differences could explain why some patients did or did not respond to treatment. This explanation was supported by the application of scDrugPrio to scRNA-seq data from eleven individual Crohns disease patients. The analysis showed great variations in drug predictions between patients, for example, assigning a high rank to anti-TNF treatment in a responder and a low rank in a nonresponder to that treatment. ConclusionWe propose a computational framework, scDrugPrio, for drug prioritisation based on scRNA-seq of IMID disease. Application to individual patients indicates scDrugPrios potential for personalised network-based drug screening on cellulome-, genome-, and drugome-wide scales. For this purpose, we made scDrugPrio into an easy-to-use R package (https://github.com/SDTC-CPMed/scDrugPrio).

immunology↗

bioRxiv2019

Assessing the multiple dimensions of poverty. Data mining approaches to the 2004-14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua

We aimed to describe multiple dimensions of poverty according to the capability approach theory by applying data mining approaches to the Cuatro Santos Health and Demographic Surveillance databases, Nicaragua. Four municipalities in northern Nicaragua constitute the Cuatro Santos area, with 25,893 inhabitants in 5,966 households (2014). A local process analyzing poverty-related problems and prioritizing suggested actions, was initiated 1997 based on a community action plan 2002-2015. Priority interventions were school breakfasts, environmental protection, water and sanitation, preventive healthcare, home gardening, micro credits, technical training, stipends for university education, and the use of Internet. In 2004, a survey of basic health and demographic information was performed in the whole population followed by surveillance updates in 2007, 2009, and 2014 linking households and individuals by unique identifiers. Information included the house (floor, walls) and services (water, sanitation, electricity) as well as demographic data (birth, deaths, migration). Data on participation in interventions, on food security, household assets, and womens self-rated health were collected in 2014. A K-means algorithm were used to cluster the household data (54 variables). The poverty ranking of household clusters using the unsatisfied basic needs index (UBN) variables changed when including variables describing basic capabilities. The households in the fairly rich cluster, having assets as motorbikes and computers were described as modern. Those in the fairly poor cluster, having different degrees of food insecurity were labeled vulnerable. Poor and poorest clusters of households were traditional e.g. in using horses for transport. Results displayed a society transforming from traditional to a modern, where the forerunners were not the richest but educated, had more working members of household, fewer children and were food secure. Those lagging were the poor, traditional and food insecure. The approach and results may be useful for an improved understanding of poverty and to direct local interventions.

epidemiology↗