Biology subjects

Zare, S.

Publications and source records attributed to Zare, S..

3 recordsLinked to original sources

bioRxiv2025

Graph-Based Modeling of Alzheimer's Protein Interactions via Spiking Neural, Hyperdimensional Encoding, and Scalable Ray-Based Learning

This study introduces a novel computational framework for predicting protein-protein interactions (PPIs) in Alzheimers disease by integrating biologically inspired and graph-based learning paradigms. The pipeline combines genetic algorithm-driven feature selection, hyperdimensional encoding for robust semantic representation, and spiking neural networks to capture temporal dynamics. These representations are fused with graph neural network embeddings and processed via scalable nearest-neighbor inference. Unlike conventional models, this multi-view architecture enables biologically grounded prediction under data sparsity, offering interpretable insights into disease-relevant molecular interactions.

bioinformatics↗

bioRxiv2023

Video Diffusion models for the apoptosis forcasting

Reliable and early prediction of cell death (apoptosis) is critically important in various areas of biology, particularly in characterizing the effectiveness of cell-based infusion products utilized for cancer immunotherapy. While deep Convolutional Neural Networks (CNNs) are often used in state-of-the-art approaches for apoptosis classification, they typically focus solely on individual cells and ignore cell-cell interaction. To address this limitation, we propose a novel generative approach based on a video diffusion model, which predicts future cellular behaviors for early detection of apoptosis events, even before molecular markers like Annexin-V or visual indications like membrane blebbing become apparent. Our approach accounts for the interactions of multiple target cells and their spatial and temporal relationships at each time frame. We condition our generative model on two starting frames and utilize an auto-regressive framework to predict the subsequent five frames. Our model achieves a 0.88 F1 score on the cell death event classification and 2.11 mean absolute death-time error, significantly outperforming state-of-the-art methods.

cell biology↗

bioRxiv2019

Normal human lymph node T follicular helper cells and germinal center B cells accessed via fine needle aspirations

Germinal centers (GC) are critically important for the maturation of the antibody response and the generation of memory B cells, which are the basis for long-term protection from pathogens. Germinal centers only occur in lymphoid tissue, such as lymph nodes, and are not present in blood. Therefore, cells of the germinal center, including GC B cells and GC T follicular helper (TFH) cells, are not well-studied in humans under normal healthy conditions, due to the limited availability of healthy lymph node samples. We used a minimally invasive, routine clinical procedure, lymph node fine needle aspirations (LN FNAs), to obtain lymph node cells from healthy human subjects to establish benchmarks of GC cells under noninflammatory conditions. This study of 50 lymph nodes demonstrates that human LN FNAs are a safe and feasible technique for immunological research, and defines benchmarks for human GC biology. The findings indicate that assessment of the GC response via LN FNAs will have application to the study of human vaccination, allergy, and autoimmune disease.

immunology↗