Biology subjects

Akhmedov, M.

Publications and source records attributed to Akhmedov, M..

7 recordsLinked to original sources

bioRxiv2026

WGCNA+: AI-powered WGCNA for Integration of Multi-Omics Data

Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Moreover, no integrated framework exists to visualize condition-specific, cross-omics relationships at module or feature level. Results: To address these limitations, we developed WGCNA+, a novel R package extending WGCNA to multi-omics. WGCNA+ offers key innovations: (i) a unified multi-omics pipeline for per-layer network inference and cross-layer module enrichment; (ii) SVD-accelerated topological overlap matrix calculation that greatly reduces computation time; (iii) a consensus framework identifying modules reproducible across independent datasets/conditions; (iv) LASAGNA, a companion R package for phenotype-conditioned, multi-partite graph visualization of cross-omics relationships; (v) AI-powered annotation and infographics offering immediate biological insight. We tested WGCNA+ across public transcriptomics, proteomics, and miRNA datasets. WGCNA+ detects biologically meaningful modules, cross-omics feature and phenotype correlations, and provides AI-powered interpretation that accelerates research. Conclusions: WGCNA+ addresses existing gaps with a principled, efficient framework for co-expression network analysis across omics. It detects cross-omics regulatory modules and their phenotype association to support basic research, biomarker discovery and pathway analysis. It uniquely offers AI-assisted interpretation and infographics, aiding hypothesis generation. Complementing WGCNA+, LASAGNA is a phenotype-aware multi-partite visualization framework to explore cross-omics relationships. Altogether, these features make WGCNA+ an innovative, powerful tool for clinical and translational research. Availability and implementation: WGCNA+ and LASAGNA are implemented in R language for statistical computing, version[≥]3.5. WGCNA+ and LASAGNA are fully and freely available with no restrictions (https://github.com/bigomics/WGCNAplus; https://github.com/bigomics/lasagna)

bioinformatics↗

bioRxiv2025

PLAID: ultrafast single-sample gene set enrichment scoring

MotivationIn recent years, computational methods have emerged that calculate enrichment of gene signatures within individual samples, rather than across pooled samples. These signatures offer critical insights into the coordinated activity of functionally related genes, proteins or metabolites, enabling identification of unique molecular profiles based on gene set and pathway activity in individual cells and patients. This strategy is pivotal for patient stratification and advancement of personalized medicine. However, the rise of large-scale datasets, including single-cell profiles and population biobanks, has exposed significant computational inefficiencies in existing methods. Current methods often demand excessive runtime and memory resources, becoming impractical for large datasets. Overcoming these limitations is a focus of current efforts by bioinformatics teams in academia and the pharmaceutical industry, as essential to support basic and clinical biomedical research. ResultsTo address this critical need, we developed PLAID (Pathway Level Average Intensity Detection), an ultrafast and memory optimized single sample gene set enrichment algorithm that utilizes sparse matrix computation. PLAID delivers highly accurate gene set scoring and surpasses the performance of current methods in single-cell and bulk transcriptomics, and proteomics data. A distinctive feature of PLAID is its integration of the most widely used gene set scoring algorithms, enabling researchers to apply multiple methods for cross-validation with outstanding runtime efficiency and minimal memory requirement. Availability and ImplementationPLAID is implemented in the R language for statistical computing. PLAID source code and installation instructions are available with no restrictions at https://github.com/bigomics/plaid

bioinformatics↗

bioRxiv2024

NPmatch: Latent Batch Effects Correction of Omics data by Nearest-Pair Matching

MotivationBatch effects (BEs) are a predominant source of noise in omics data and often mask real biological signals. BEs remain common in existing datasets. Current methods for BE correction mostly rely on specific assumptions or complex models, and may not detect and adjust BEs adequately, impacting downstream analysis and discovery power. To address these challenges we developed NPmatch, a nearest-neighbor matching-based method that adjusts BEs satisfactorily and outperforms current methods in a wide range of datasets. ResultsWe assessed distinct metrics and graphical readouts, and compared our method to commonly used BE correction methods. NPmatch demonstrates overall superior performance in correcting for BEs while preserving biological differences than existing methods. Altogether, our method proves to be a valuable BE correction approach to maximize discovery in biomedical research, with applicability in clinical research where latent BEs are often dominant. Data availability and implementationNPmatch is freely available on Github (https://github.com/bigomics/NPmatch) and on Omics Playground (https://bigomics.ch/omics-playground). The datasets underlying this article are the following: GSE120099, GSE82177, GSE162760, GSE171343, GSE153380, GSE163214, GSE182440, GSE163857, GSE117970, GSE173078, GSE10846. All these datasets are publicly available and can be freely accessed on the Gene Expression Omnibus (GEO) repository.

bioinformatics↗

bioRxiv2021

IL-1α secreted by subcapsular sinus macrophages promotes melanoma metastasis in the sentinel lymph node by upregulating STAT3 signaling in the tumor.

During melanoma metastasization, tumor cells originated in the skin migrate via lymphatic vessels to the sentinel lymph node (sLN) in a process that facilitates their spread across the body. Here, we characterized the innate inflammatory responses to melanoma metastasis in the sLN. For this purpose, we confirmed the migration of fluorescent metastatic melanoma cells to the sLN and we characterized the inflammatory response in the metastatic microenvironment. We found that macrophages located in the subcapsular sinus (SSM), produce pro-tumoral IL-1 after recognition of tumor antigens. Moreover, we confirmed that the administration of an anti-IL-1 depleting antibody reduced metastasis. Conversely, the administration of recombinant IL-1 accelerated the lymphatic spreading of the tumor. Additionally, the elimination of the macrophages significantly reduced the progression of the metastatic spread. To understand the mechanism of action of IL-1 in the context of the lymph node microenvironment, we applied single-cell RNA sequencing to dissected metastases obtained from animals treated with an anti-IL-1 blocking antibody. Amongst the different pathways affected, we identified STAT3 as one of the main targets of IL-1 signaling in metastatic cells. Moreover, we found that the anti-IL-1 anti-tumoral effect was not mediated by lymphocytes, as IL-1R1 KO mice did not show any improvement in metastasis growth. Finally, we found a synergistic anti-metastatic effect of the combination of IL-1 blocking and the STAT3 inhibitor (STAT3i) stattic. In summary, we described a new mechanism by which SSM support melanoma metastasis, highlighting a new target for immunotherapy.

immunology↗

bioRxiv2020

Reanalysis of MERS, SARS and COVID-19 Infection Datasets using VirOmics Playground Reveals Common Patterns in Gene and Protein Expression

Three lethal lower respiratory tract coronavirus epidemics have occurred over the past 20 years. This coincided with major developments in genome-wide gene and protein expression analysis, resulting in a wealth of datasets in the public domain. Seven such in vitro studies were selected for comparative bioinformatic analysis through the VirOmics Playground, a visualisation and exploration platform we developed. Despite the heterogeneous nature of the data sets, several commonalities could be observed across studies and species. Differences, on the other hand, reflected not only variations between species, but also other experimental variables, such as cell lines used for the experiments, infection protocols and viral strains. The datasets analysed here are available online through our platform (https://public.bigomics.ch/app/omicsplayground_viromics).

genomics↗

bioRxiv2018

A prize-collecting Steiner tree application for signature selection to stratify diffuse large B-cell lymphoma subtypes

BackgroundWith the explosion of high-throughput data available in biology, the bottleneck is shifted to effective data interpretation. By taking advantage of the available data, it is possible to identify the biomarkers and signatures to distinguish subtypes of a specific cancer in the context of clinical trials. This requires sophisticated methods to retrieve the information out of the data, and various algorithms have been recently devised.\n\nResultsHere, we applied the prize-collecting Steiner tree (PCST) approach to obtain a gene expression signature for the classification of diffuse large B-cell lymphoma (DLBCL). The PCST is a network-based approach to capture new insights about genomic data by incorporating an interaction network landscape. Moreover, we adopted the ElasticNet incorporating PCA as a classification method. We used seven public gene expression profiling datasets (three for training, and four for testing) available in the literature, and obtained 10 genes as signature. We tested these genes by employing ElasticNet, and compared the performance with the DAC algorithm as current golden standard. The performance of the PCST signature with ElasticNet outperformed the DAC in distinguishing the subtypes. In addition, the gene expression signature was able to accurately stratify DLBCL patients on survival data.\n\nConclusionsWe developed a network-based optimization technique that performs unbiased signature selection by integrating genomic data with biological networks. Our classifier trained with the obtained signature outperformed the state-of-the-art method in subtype distinction and survival data stratification in DLBCL. The proposed method is a general approach that can be applied on other classification problems.

bioinformatics↗

bioRxiv2017

OmicsNet: Integration of Multi-Omics Data using Path Analysis in Multilayer Networks

Integrative analysis of heterogeneous omics data is essential to obtain a comprehensive overview of otherwise fragmented information and to better understand dysregulated biological pathways leading to a specific condition. One of the major challenges in systems biology is to develop computational methods for proper integration of multi-omics datasets. We propose OmicsNet that uses a multilayer network for the integration and analysis of multi-omics data of heterogeneous types. Each layer of the multilayer network represents a certain data type: input layers correspond to genotype features and nodes in the output layer correspond to phenotypes, while intermediate layers may represent genesets or biological concepts to facilitate functional interpretation of the data. OmicsNet then calculates the highest coefficient paths in multilayer network from each genomic feature to the phenotype by computing an integrated score along the paths. These paths may indicate the most plausible signalling cascade caused by perturbed genotype features leading to a particular phenotype response. With example applications, we illustrate the potential power of OmicsNet in the functional analysis, biomarker discovery and drug response prediction in personalized medicine using multi-omics data.

bioinformatics↗