On Quantum Random Walks in Biomolecular Networks

• URL: http://arxiv.org/abs/2506.06514v1
• Date: Fri, 06 Jun 2025 20:25:52 GMT
• Title: On Quantum Random Walks in Biomolecular Networks
• Authors: Viacheslav Dubovitskii, Aritra Bose, Filippo Utro, Laxmi Pardia,
• Abstract summary: Biomolecular networks offer valuable insights into the organization of biological systems.<n>These networks are key to understanding cellular functions, disease mechanisms, and identifying therapeutic targets.<n>We explore the potential of quantum random walks (QRWs) for biomolecular network analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Biomolecular networks, such as protein-protein interactions, gene-gene associations, and cell-cell interactions, offer valuable insights into the complex organization of biological systems. These networks are key to understanding cellular functions, disease mechanisms, and identifying therapeutic targets. However, their analysis is challenged by the high dimensionality, heterogeneity, and sparsity of multi-omics data. Random walk algorithms are widely used to propagate information through disease modules, helping to identify disease-associated genes and uncover relevant biological pathways. In this work, we investigate the limitations of classical random walks and explore the potential of quantum random walks (QRWs) for biomolecular network analysis. We evaluate QRWs in two network-based applications. First, in a gene-gene interaction network associated with asthma, autism, and schizophrenia, QRWs more accurately rank disease-associated genes compared to classical methods. Second, in a structured multi-partite cell-cell interaction network derived from mouse brown adipose tissue, QRWs identify key driver genes in malignant cells that are overlooked by classical random walks. Our findings suggest that quantum random walks offer a promising alternative to classical approaches, with improved sensitivity to network structure and better performance in identifying biologically relevant features. This highlights their potential in advancing network medicine and systems biology.

Related papers

• BioMaze: Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoning [49.487327661584686]
  We introduce BioMaze, a dataset with 5.1K complex pathway problems from real research.<n>Our evaluation of methods such as CoT and graph-augmented reasoning, shows that LLMs struggle with pathway reasoning.<n>To address this, we propose PathSeeker, an LLM agent that enhances reasoning through interactive subgraph-based navigation.
  arXiv  Detail & Related papers  (2025-02-23T17:38:10Z)
• Recovering Time-Varying Networks From Single-Cell Data [11.04189396013616]
  We develop a deep neural network, Marlene, to infer dynamic graphs from time series single-cell gene expression data. Marlene can identify gene interactions relevant to specific biological responses, including COVID-19 immune response, fibrosis, and aging.
  arXiv  Detail & Related papers  (2024-10-01T19:18:51Z)
• Interpreting artificial neural networks to detect genome-wide association signals for complex traits [0.0]
  We trained artificial neural networks to predict complex traits using both simulated and real genotype-phenotype datasets.<n>We detected multiple loci associated with schizophrenia.
  arXiv  Detail & Related papers  (2024-07-26T15:20:42Z)
• MMIL: A novel algorithm for disease associated cell type discovery [58.044870442206914]
  Single-cell datasets often lack individual cell labels, making it challenging to identify cells associated with disease. We introduce Mixture Modeling for Multiple Learning Instance (MMIL), an expectation method that enables the training and calibration of cell-level classifiers.
  arXiv  Detail & Related papers  (2024-06-12T15:22:56Z)
• Single-Cell Deep Clustering Method Assisted by Exogenous Gene Information: A Novel Approach to Identifying Cell Types [50.55583697209676]
  We develop an attention-enhanced graph autoencoder, which is designed to efficiently capture the topological features between cells. During the clustering process, we integrated both sets of information and reconstructed the features of both cells and genes to generate a discriminative representation. This research offers enhanced insights into the characteristics and distribution of cells, thereby laying the groundwork for early diagnosis and treatment of diseases.
  arXiv  Detail & Related papers  (2023-11-28T09:14:55Z)
• Causal machine learning for single-cell genomics [94.28105176231739]
  We discuss the application of machine learning techniques to single-cell genomics and their challenges. We first present the model that underlies most of current causal approaches to single-cell biology. We then identify open problems in the application of causal approaches to single-cell data.
  arXiv  Detail & Related papers  (2023-10-23T13:35:24Z)
• Two Novel Approaches to Detect Community: A Case Study of Omicron Lineage Variants PPI Network [0.5156484100374058]
  We aim to uncover the communities within the variant B.1.1.529 (Omicron virus) using two proposed novel algorithms and four widely recognized algorithms. We also compare the networks by the global properties, statistic summary, subgraph count, graphlet and validate by the modulaity.
  arXiv  Detail & Related papers  (2023-08-09T03:51:20Z)
• DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks [0.7349727826230864]
  The goal of this work is to explore how the fusion of systems' level information with temporal dynamics of gene expression can be used to predict novel gene functions. We propose DDeMON, an approach for scalable, systems-level inference of function annotation using time-dependent multiscale biological information.
  arXiv  Detail & Related papers  (2023-02-08T06:53:02Z)
• Granger causal inference on DAGs identifies genomic loci regulating transcription [77.58911272503771]
  GrID-Net is a framework based on graph neural networks with lagged message passing for Granger causal inference on DAG-structured systems. Our application is the analysis of single-cell multimodal data to identify genomic loci that mediate the regulation of specific genes.
  arXiv  Detail & Related papers  (2022-10-18T21:15:10Z)
• Quantum network medicine: rethinking medicine with network science and quantum algorithms [0.0]
  Quantum computing may be a key ingredient in enabling the full potential of network medicine. We propose to combine network medicine and quantum algorithms in a novel research field, quantum network medicine.
  arXiv  Detail & Related papers  (2022-06-22T09:05:24Z)
• POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
  We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations. The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
  arXiv  Detail & Related papers  (2022-01-19T09:26:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.