Quantum computing in bioinformatics: a systematic review mapping

Briefings in Bioinformatics, Journal Year: 2024, Volume and Issue: 25(5)

Published: July 25, 2024

Abstract The field of quantum computing (QC) is expanding, with efforts being made to apply it areas previously covered by classical algorithms and methods. Bioinformatics one such domain that developing in terms QC. This article offers a broad mapping review methods QC bioinformatics, marking the first its kind. It presents an overview aids researchers identifying further research directions early stages this knowledge. work presented here shows current state-of-the-art solutions, focuses on general future directions, highlights limitations gathered data includes comprehensive list identified along descriptions, classifications, elaborations their advantages disadvantages. Results are not just descriptive table but also aggregated visual format.

Language: Английский

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Quantum natural language processing and its applications in bioinformatics: a comprehensive review of methodologies, concepts, and future directions

Frontiers in Computer Science, Journal Year: 2025, Volume and Issue: 7

Published: Feb. 18, 2025

Quantum Natural Language Processing (QNLP) is a relatively new subfield of research that extends the application principles natural language processing and quantum computing has enabled complex biological information to unprecedented levels. The present comprehensive review analyses potential QNLP in influencing many branches bioinformatics such as genomic sequence analysis, protein structure prediction, drug discovery design. To establish correct background techniques, this article going explore basics including qubits, entanglement, algorithms. next section devoted extraction material valuable knowledge related development, prediction assessment drug-target interactions. In addition, paper also explains structural by embedding, simulation, optimization for exploring sequence-structure relationship. However, study acknowledges future discussion challenges weaknesses hardware, data representation, encoding, construction enhancement This looks into real-life problems solved from industry applications, benchmarking criteria, comparison with other traditional NLP methods. Therefore, enunciates perspectives, well developmental implementation blueprint bioinformatics. plan follows: its function achieve objectives precision medicine, design, multi-omics, green chemistry.

Language: Английский

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Nuclear-Spin-Dependent Chirogenesis: Hidden Symmetry Breaking of Poly(di-n-butylsilane) in n-Alkanes

Symmetry, Journal Year: 2025, Volume and Issue: 17(3), P. 433 - 433

Published: March 13, 2025

Since the 1960s, theorists have claimed that electroweak force, which unifies parity-conserving electromagnetic and parity-violating weak nuclear forces, induces tiny energy differences (10−10–10−21 eV) between mirror-image molecules. This study reports dual mirror-symmetry-breaking second-order phase transition characteristics of mirror-symmetric 73-helical poly(di-n-butylsilane) in n-alkanes under static (non-stirring) conditions. In particular, n-dodecane-h26 significantly enhances circular dichroism (CD) circularly polarized luminescence (CPL) spectra. A new (−)-CD band emerges at 299 nm below TC1 ~ 105 °C, with a helix–helix TC2 28 exhibits gabs = +1.3 × 10−2 −10 °C. Synchronously, CPL 340 exhibiting glum −0.7 60 °C inverts to +2.0 0 Interestingly, clockwise counterclockwise stirring mixture induced non-mirror-image CD n-Dodecane-d26 weakens values by an order magnitude, oppositely signed lower ~45 are observed. The notable H/D isotope effect suggests CH3 termini polysilane n-dodecane-h26, comprise three identical spin-1/2 system triple-well potential, effectively work as unidirectional hindered rotors due handedness nuclear-spin-dependent universal forces. is supported (−)-sign vibrational bands symmetric asymmetric bending modes group n-dodecane-h26.

Language: Английский

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Function and Mechanism of Mitochondrial-Associated Membranes in Acute Respiratory Distress Syndrsome: A Comprehensive Study Combining Bioinformatics and Experimental Approaches

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: March 17, 2025

Background: Acute respiratory distress syndrome (ARDS) is a life-threatening lung condition characterized by severe inflammation, immune dysregulation, and oxidative stress, leading to high mortality (30–40%). Mitochondria-associated membranes (MAMs) regulate cellular metabolism signaling, but their role in ARDS remains unclear. This study explores the involvement of MAM-related genes pathogenesis through bioinformatics experimental validation. Methods: Publicly available RNA-sequencing data from control samples were analyzed identify differentially expressed (DEGs). Functional enrichment, gene set variation analysis (GSVA), weighted co-expression network (WGCNA) performed explore pathway alterations hub interactions. Immune cell infiltration was conducted using CIBERSORT. Candidate validated Poly I:C-induced mouse model MLE-12 murine epithelial cells. The assessed for histopathology, wet-to-dry weight ratio, bronchoalveolar lavage fluid (BALF) inflammatory cytokine levels (IL-1β TNF-α), injury scores. cells treated with I:C, viability, lactate dehydrogenase (LDH) release, apoptosis evaluated. Protein-protein interaction (PPI) drug prediction used potential therapeutic targets. Results: A total 3152 DEGs including 1549 upregulated 1603 downregulated identified samples. Pathway revealed autophagy suppression activation, 14 types significantly elevated patients. Experimental validation confirmed that mice exhibited increased reaction, while I:C-treated showed cytotoxicity LDH release. HBB ZMAT2 as key genes, negatively correlating severity positively associated disease progression. Drug 29 pharmacological agents interacting HBB, suggesting potential. Conclusions: identifies contributing pathogenesis, diagnostic applications. integration vivo vitro provides novel insights into molecular mechanisms. Further clinical studies are needed translational relevance.

Language: Английский

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Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis

Frontiers in Computer Science, Journal Year: 2025, Volume and Issue: 7

Published: May 7, 2025

This paper presents a systematic review of quantum computing approaches to healthcare-related computational problems, with an emphasis on quantum-theoretical foundations and algorithmic complexity. We adopt optimized machine learning methodology—combining Particle Swarm Optimization (PSO) Latent Dirichlet Allocation (LDA)—to analyze the literature identify key research themes at intersection healthcare. A total 63 peer-reviewed studies were analyzed, 41 categorized under first domain 22 second. approach revealed two primary directions: (1) for artificial intelligence in healthcare, (2) healthcare data security. highlight theoretical advances underlying these domains, from novel algorithms biomedical cryptographic protocols securing medical information. gradient boosting classifier further validates our taxonomy by reliably distinguishing between categories research, demonstrating robustness identified themes, accuracy 84.2%, precision 88.9%, recall F1-score 84.5%, area curve 0.875. Interpretability analysis using Local Interpretable Model-Agnostic Explanations (LIME) exposes features each category (e.g., references applications versus blockchain-based security frameworks), offering transparency into literature-driven categorization, latter showing most significant contributions topic assignment (ranging −0.133 +0.128). Our findings underscore that offer potential enhance security, optimize complex diagnostic computations, provide speedups health informatics. also outstanding challenges—such as need scalable error-tolerant hardware integration—that must be addressed translate advancements real-world clinical impact. study emphasizes importance hybrid quantum-classical models cross-disciplinary bridge gap cutting-edge theory its practical

Language: Английский

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InteracTor: A new integrative feature extraction toolkit for improved characterization of protein structural properties

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 11, 2024

Abstract Understanding the structural and functional diversity of protein families is crucial for elucidating their biological roles. Traditional analyses often focus on primary secondary structures, which include amino acid sequences local folding patterns like alpha helices beta sheets. However, structures alone may not fully represent complex interactions within proteins. To address this limitation, we developed a new algorithm (InteracTor) to analyze proteins by extracting features from three-dimensional (3D) structures. The toolkit extracts interatomic interaction such as hydrogen bonds, van der Waals interactions, hydrophobic contacts, are understanding dynamics, structure, function. Incorporating 3D data provides more comprehensive structure function, potentially enhancing downstream predictive modeling capabilities. By using extracted in Mutual Information scoring (MI), Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation Projection (UMAP), hierarchical clustering analysis use cases, identified clear separations among families, highlighting distinct aspects. Our revealed that were informative than features, providing insights into potential properties. These findings underscore significance considering tertiary analysis, offering robust framework future studies aiming at capabilities models function prediction drug discovery.

Language: Английский

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Quantum Computing for Bioinformatics

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown

Published: Jan. 1, 2024

Language: Английский

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Quantum Bioinformatics: A Novel Approach to Understanding Diabetes Mellitus

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 9, 2024

Introduction: Diabetes mellitus (DM) is a complex metabolic disorder posing significant global health concern. While classical biochemical models have provided valuable insights, the underlying molecular mechanisms of this disease remain incompletely understood. Recent advancements in quantum mechanics and bioinformatics opened new avenues for exploring nature biological processes, including those involved DM. Objective: To investigate potential role pathophysiology DM by employing multidisciplinary approach that integrates mechanical calculations with analysis. Methods: A comprehensive dataset proteins implicated was curated from Protein Data Bank. Quantum calculations, Density Functional Theory Time-Dependent Theory, were performed to elucidate electronic structure, vibrational properties, effects key amino acid residues active sites these proteins. Bioinformatics tools used analyze protein-protein interaction networks, identify allosteric sites, predict impact mutations on protein structure function. Results: Our findings provide strong evidence effects, particularly coherence tunneling, may play crucial regulating enzymatic activity, protein-ligand interactions, energy transfer processes glucose metabolism insulin signaling. Key include identification tunneling pathways enzymes, vibronic coupling modulating Conclusion: This study offers novel perspective diabetes integrating bioinformatics. Our suggest contribute pathogenesis DM, opening development innovative diagnostic therapeutic strategies.

Language: Английский

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A Study on the Discovery of the Relationship between Patent and Papers from the Perspective of Patent Subjects --- A Case Study on the Topic of Quantum Computing

Published: Dec. 20, 2024

Language: Английский

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