Quantum machine learning framework for longitudinal biomedical studies

• URL: http://arxiv.org/abs/2504.18392v1
• Date: Thu, 24 Apr 2025 16:50:04 GMT
• Title: Quantum machine learning framework for longitudinal biomedical studies
• Authors: Maria Demidik, Filippo Utro, Alexey Galda, Karl Jansen, Daniel Blankenberg, Laxmi Parida,
• Abstract summary: We explore the potential of quantum machine learning (QML) for longitudinal biomarker discovery.<n>We propose a novel modification to the instantaneous quantum time (IQP) feature map, designed to encode temporal dependencies.<n>We demonstrate that our longitudinal IQP map improves the ability of quantum kernels to capture intra-subject temporal patterns.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Longitudinal biomedical studies play a vital role in tracking disease progression, treatment response, and the emergence of resistance mechanisms, particularly in complex disorders such as cancer and neurodegenerative diseases. However, the high dimensionality of biological data, combined with the limited size of longitudinal cohorts, presents significant challenges for traditional machine learning approaches. In this work, we explore the potential of quantum machine learning (QML) for longitudinal biomarker discovery. We propose a novel modification to the instantaneous quantum polynomial time (IQP) feature map, designed to encode temporal dependencies across multiple time points in biomedical datasets. Through numerical simulations on both synthetic and real-world datasets - including studies on follicular lymphoma and Alzheimer's disease - we demonstrate that our longitudinal IQP feature map improves the ability of quantum kernels to capture intra-subject temporal patterns, offering a promising direction for QML in clinical research.

Related papers

• Causal Representation Meets Stochastic Modeling under Generic Geometry [49.24293444627916]
  We develop causal representation learning for continuous-time latent point processes.<n>We develop MUTATE, an identifiable variational autoencoder framework with a time-adaptive transition module.<n>Across simulated and empirical studies, we find that MUTATE can effectively answer scientific questions.
  arXiv  Detail & Related papers  (2026-02-04T20:40:53Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Frequency-Domain Analysis of Time-Dependent Multiomic Data in Progressive Neurodegenerative Diseases: A Proposed Quantum-Classical Hybrid Approach with Quaternionic Extensions [0.0]
  We propose a theoretical mathematical framework that transforms time-series data into frequency or s-domain.<n>We employ quantum-classical hybrid computing with variational quantum eigensolvers (VQE) for enhanced pattern detection.<n>This framework aims to lay the groundwork for redefining precision medicine for neurodegenerative diseases through future validations.
  arXiv  Detail & Related papers  (2025-08-11T13:03:58Z)
• A Quantum Platform for Multiomics Data [0.0]
  Quantum computing offers a new paradigm for addressing classically intractable problems.<n>We introduce a hybrid quantum-classical machine learning platform designed to bridge this gap.<n>We propose to demonstrate the platform's utility through quantum-enhanced classification of phenotypic states from molecular variables and prediction of temporal evolution in biological systems.
  arXiv  Detail & Related papers  (2025-06-17T00:33:06Z)
• 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)
• Advancing Precision Oncology Through Modeling of Longitudinal and Multimodal Data [1.6163129903911508]
  Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. Today's data-driven research in oncology has primarily focused on cross-sectional analysis using data from a single modality. Advances in multiscale data collection and computational methods now enable the discovery of longitudinal multimodal biomarkers for precision oncology.
  arXiv  Detail & Related papers  (2025-02-11T01:44:51Z)
• Robust Quantum Reservoir Computing for Molecular Property Prediction [0.5399129278613575]
  We propose the quantum reservoir computing (QRC) approach to predict the biological activity of potential drug molecules.<n>We observe more robust QRC performance as the size of the dataset decreases.<n>In addition, we leverage the uniform manifold approximation and projection technique to analyze structural changes as classical features are transformed through quantum dynamics.
  arXiv  Detail & Related papers  (2024-12-09T18:49:18Z)
• How quantum computing can enhance biomarker discovery [0.14043931310479377]
  Quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery.<n>The opportunities and challenges associated with the algorithms and applications are discussed.<n>An outlook is provided concerning open research challenges.
  arXiv  Detail & Related papers  (2024-11-15T16:50:05Z)
• Multi-Omic and Quantum Machine Learning Integration for Lung Subtypes Classification [0.0]
  The fusion of quantum computing and machine learning holds promise for unraveling complex patterns within multi-omics datasets. We developed a method for finding the best differentiating features between LUAD and LUSC datasets, which has the potential for biomarker discovery.
  arXiv  Detail & Related papers  (2024-10-02T23:16:31Z)
• ProBio: A Protocol-guided Multimodal Dataset for Molecular Biology Lab [67.24684071577211]
  The challenge of replicating research results has posed a significant impediment to the field of molecular biology. We first curate a comprehensive multimodal dataset, named ProBio, as an initial step towards this objective. Next, we devise two challenging benchmarks, transparent solution tracking and multimodal action recognition, to emphasize the unique characteristics and difficulties associated with activity understanding in BioLab settings.
  arXiv  Detail & Related papers  (2023-11-01T14:44:01Z)
• Long Short-term Memory with Two-Compartment Spiking Neuron [64.02161577259426]
  We propose a novel biologically inspired Long Short-Term Memory Leaky Integrate-and-Fire spiking neuron model, dubbed LSTM-LIF. Our experimental results, on a diverse range of temporal classification tasks, demonstrate superior temporal classification capability, rapid training convergence, strong network generalizability, and high energy efficiency of the proposed LSTM-LIF model. This work, therefore, opens up a myriad of opportunities for resolving challenging temporal processing tasks on emerging neuromorphic computing machines.
  arXiv  Detail & Related papers  (2023-07-14T08:51:03Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• Translational Quantum Machine Intelligence for Modeling Tumor Dynamics in Oncology [18.069876260017605]
  Quantum Machine Intelligence offers unparalleled insights into tumor dynamics via a quantum mechanics perspective. We introduce a novel hybrid quantum-classical neural architecture named $eta-$Net that enables quantifying quantum dynamics of tumor burden concerning treatment effects.
  arXiv  Detail & Related papers  (2022-02-21T08:46:58Z)
• Neuro-symbolic Neurodegenerative Disease Modeling as Probabilistic Programmed Deep Kernels [93.58854458951431]
  We present a probabilistic programmed deep kernel learning approach to personalized, predictive modeling of neurodegenerative diseases. Our analysis considers a spectrum of neural and symbolic machine learning approaches. We run evaluations on the problem of Alzheimer's disease prediction, yielding results that surpass deep learning.
  arXiv  Detail & Related papers  (2020-09-16T15:16:03Z)
• Machine Learning in Nano-Scale Biomedical Engineering [77.75587007080894]
  We review the existing research regarding the use of machine learning in nano-scale biomedical engineering. The main challenges that can be formulated as ML problems are classified into the three main categories. For each of the presented methodologies, special emphasis is given to its principles, applications, and limitations.
  arXiv  Detail & Related papers  (2020-08-05T15:45:54Z)

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.