Biology subjects

Kiselev, V. Y.

Publications and source records attributed to Kiselev, V. Y..

6 recordsLinked to original sources

bioRxiv2026

Artemis: Harnessing Knowledge Graphs for Next-Generation Drug Target Prioritization

Knowledge graphs (KGs) have become an important asset in biomedical research and drug discovery by enabling the structured integration of heterogeneous biological knowledge. When combined with machine learning (ML), KGs support the identification of novel drug-target relationships, but existing approaches are often KG-centric, relying primarily on graph structure and embeddings while overlooking disease-specific biological and clinical context. Moreover, many high-impact applications depend on proprietary KG infrastructures, limiting accessibility for the broader research community. Here, we introduce Artemis, a practical and generalisable machine-learning framework for indication-aware target prioritisation that integrates public biomedical KGs with clinical evidence from the ChEMBL database. Artemis derives graph-based representations of clinically validated drug targets from multiple publicly available KGs and augments them with disease-relevant clinical features from ChEMBL. This hybrid feature space is used to train supervised ML models across seven disease indications, with performance assessed via cross-validation and guided parameter optimisation. The framework is further evaluated on emerging breast cancer targets reported at the San Antonio Breast Cancer Symposium 2024, demonstrating its ability to prioritise novel candidates. Overall, this work demonstrates that publicly available KGs can be used for actionable, translational target discovery when coupled with clinical data. Artemis provides an accessible, scalable, and cost-efficient alternative to proprietary KG platforms. Thereby offering a practical solution for researchers seeking to prioritise therapeutic targets in real-world drug discovery settings. Key PointsO_LIKG applications can support the identification of novel drug-target relationships but rely primarily on graph structure while overlooking disease-specific biological and clinical context. C_LIO_LIArtemis performs indication-aware target prioritisation that integrates public biomedical KGs with clinical evidence from the ChEMBL database. C_LIO_LIArtemis is evaluated on emerging breast cancer targets reported at the San Antonio Breast Cancer Symposium 2024, demonstrating its ability to prioritise novel candidates. C_LIO_LIArtemis provides an accessible, scalable, and cost-efficient alternative to proprietary KG platforms offering a practical solution for researchers seeking to prioritise therapeutic targets in real-world drug discovery settings. C_LI

bioinformatics↗

bioRxiv2021

Single-Cell Atlas of Common Variable Immunodeficiency reveals germinal center-associated epigenetic dysregulation in B cell responses

Common variable immunodeficiency (CVID), the most prevalent symptomatic primary immunodeficiency, is characterized by impaired terminal B-cell differentiation and defective antibody responses. Incomplete genetic penetrance and a wide range of phenotypic expressivity in CVID suggest the participation of additional pathogenic mechanisms. Monozygotic (MZ) twins discordant for CVID are uniquely valuable for studying the contribution of epigenetics to the disease. We used single-cell epigenomics and transcriptomics to create a cell census of naive-to-memory B cell differentiation in a pair of CVID-discordant MZ twins. Our analysis identifies DNA methylation, chromatin accessibility and transcriptional defects in memory B cells that mirror defective cell-cell communication defects following activation. These findings were validated in a cohort of CVID patients and healthy donors. Our findings provide a comprehensive multi-omics map of alterations in naive-to-memory B-cell transition in CVID and reveal links between the epigenome and immune cell cross-talk. Our resource, publicly available at the Human Cell Atlas, paves the way for future diagnosis and treatments of CVID patients.

immunology↗

bioRxiv2020

Flexible comparison of batch correction methods for single-cell RNA-seq using BatchBench

As the cost of single-cell RNA-seq experiments has decreased, an increasing number of datasets are now available. Combining newly generated and publicly accessible datasets is challenging due to non-biological signals, commonly known as batch effects. Although there are several computational methods available that can remove batch effects, evaluating which method performs best is not straightforward. Here we present BatchBench (https://github.com/cellgeni/batchbench), a modular and flexible pipeline for comparing batch correction methods for single-cell RNA-seq data. We apply BatchBench to eight methods, highlighting their methodological differences and assess their performance and computational requirements through a compendium of well-studied datasets. This systematic comparison guides users in the choice of batch correction tool, and the pipeline makes it easy to evaluate other datasets.

bioinformatics↗

bioRxiv2020

Comparison of visualisation tools for single-cell RNAseq data

In the last decade, single cell RNAseq (scRNAseq) datasets have grown from a single cell to millions of cells. Due to its high dimensionality, the scRNAseq data contains a lot of valuable information, however, it is not always feasible to visualise and share it in a scientific report or an article publication format. Recently, a lot of interactive analysis and visualisation tools have been developed to address this issue and facilitate knowledge transfer in the scientific community. In this study, we review and compare several of the currently available analysis and visualisation tools and benchmark those that allow to visualize the scRNAseq data on the web and share it with others. To address the problem of format compatibility for most visualisation tools, we have also developed a user-friendly R package, sceasy, which allows users to convert their own scRNAseq datasets into a specific data format for visualisation.

bioinformatics↗

bioRxiv2019

Fast searches of large collections of single cell data using scfind

Single cell technologies have made it possible to profile millions of cells, but for these resources to be useful they must be easy to query and access. To facilitate interactive and intuitive access to single cell data we have developed scfind, a search engine for cell atlases. Using transcriptome data from mouse cell atlases we show how scfind can be used to evaluate marker genes, to perform in silico gating, and to identify both cell-type specific and housekeeping genes. Moreover, we have developed a subquery optimization routine to ensure that long and complex queries return meaningful results. To make scfind more user friendly and accessible, we use indices of PubMed abstracts and techniques from natural language processing to allow for arbitrary queries. Finally, we show how scfind can be used for multi-omics analyses by combining single-cell ATAC-seq data with transcriptome data.

bioinformatics↗

bioRxiv2017

scmap - A tool for unsupervised projection of single cell RNA-seq data

Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues1-9 since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome8,10. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. Here, we present scmap (http://bioconductor.org/packages/scmap), a method for projecting cells from a scRNA-seq experiment onto the cell-types or individual cells identified in other experiments (the application can be run for free, without restrictions, from http://www.hemberg-lab.cloud/scmap).

bioinformatics↗