Biology subjects

Abdelkareem, A. O.

Publications and source records attributed to Abdelkareem, A. O..

3 recordsLinked to original sources

bioRxiv2025

XenoSignal: Investigating Intra- and Inter-Species Ligand-Receptor Interactions Using AlphaFold3

Xenograft models, in which human tumors are implanted into mouse hosts, offer a unique platform to study tumor-microenvironment (TME) interactions. Ligand-receptor (LR) interactions are central to intercellular communication in this context, but most inference tools rely on single-species databases and assume conserved function across orthologues. To test whether this assumption holds given sequence and structural divergence, we developed XenoSignal, a computational framework that integrates AlphaFold3 structural predictions to assess the plausibility of intraspecies and interspecies LR interactions. The pipeline predicts whether human-mouse orthologues retain or lose their functional interaction, enabling construction of a high-confidence cross-species LR resource. XenoSignal facilitates the interpretation of tumor-TME crosstalk in xenografts and prioritizes LR pairs for experimental validation. This resource enhances our ability to dissect interspecies signaling, uncover mechanisms of immune evasion, and identify candidate therapeutic targets in translational cancer models.

bioinformatics↗

bioRxiv2025

spOT-NMF: Optimal Transport-Based Matrix Factorization for Accurate Deconvolution of Spatial Transcriptomics

Spatial transcriptomics technologies advance our understanding of complex biology by directly profiling cellular organization within tissues. However, accurate deconvolution of cell types and functional states remains challenging as most current computational methods either rely on high-quality matched single-cell reference profiles (often lacking for many tissues or disease states), struggle across spatial resolutions and under variable sequencing depths, and face scalability bottlenecks in large datasets. To address these challenges, we developed an optimal transport-based non-negative matrix factorization method (spOT-NMF) that leverages the Wasserstein distance to disentangle mixed gene expression signals in a reference-free manner. Benchmarking against well-established unsupervised deconvolution approaches demonstrates top performance of spOT-NMF in simulated and real spatial transcriptomics data spanning sub-cellular to multi-cellular resolutions, across multiple platforms, in two-species admixture scenarios such as xenografts, and in human cancer. We provide spOT-NMF as a freely available package for spatial data analysis, supporting GPU acceleration for large-scale analyses.

bioinformatics↗

bioRxiv2020

Viral Sequence Identification in Metagenomes using Natural Language Processing Techniques

Viral reads identification is one of the important steps in metagenomic data analysis. It shows up the diversity of the microbial communities and the functional characteristics of microorganisms. There are various tools that can identify viral reads in mixed metagenomic data using similarity and statistical tools. However, the lack of available genome diversity is a serious limitation to the existing techniques. In this work, we applied natural language processing approaches for document classification in analyzing metagenomic sequences. Text featurization is presented by treating DNA similar to natural language. These techniques reveal the importance of using the text feature extraction pipeline in sequence identification by transforming DNA base pairs into a set of characters with a term frequency and inverse document frequency techniques. Various machine learning classification algorithms are applied to viral identification tasks such as logistic regression and multi-layer perceptron. Moreover, we compared classical machine learning algorithms with VirFinder and VirNet, our deep attention model for viral reads identification on generated fragments of viruses and bacteria for benchmarking viral reads identification tools. Then, as a verification of our tool, It was applied to a simulated microbiome and virome data for tool verification and real metagenomic data of Roche 454 and Illumina for a case study.

bioinformatics↗