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At least 73 records · Page 4Linked to original sources
PubMed2025

Privacy-preserving framework for genomic computations via multi-key homomorphic encryption.

MOTIVATION: The affordability of genome sequencing and the widespread availability of genomic data have opened up new medical possibilities. Nevertheless, they also raise significant concerns regarding privacy due to the sensitive information they encompass. These privacy implications act as barriers to medical research and data availability. Researchers have proposed privacy-preserving techniques to address this, with cryptography-based methods showing the most promise. However, existing cryptography-based designs lack (i) interoperability, (ii) scalability, (iii) a high degree of privacy (i.e. compromise one to have the other), or (iv) multiparty analyses support (as most existing schemes process genomic information of each party individually). Overcoming these limitations is essential to unlocking the full potential of genomic data while ensuring privacy and data utility. Further research and development are needed to advance privacy-preserving techniques in genomics, focusing on achieving interoperability and scalability, preserving data utility, and enabling secure multiparty computation. RESULTS: This study aims to overcome the limitations of current cryptography-based techniques by employing a multi-key homomorphic encryption scheme. By utilizing this scheme, we have developed a comprehensive protocol capable of conducting diverse genomic analyses. Our protocol facilitates interoperability among individual genome processing and enables multiparty tests, analyses of genomic databases, and operations involving multiple databases. Consequently, our approach represents an innovative advancement in secure genomic data processing, offering enhanced protection and privacy measures. AVAILABILITY AND IMPLEMENTATION: All associated code and documentation are available at https://github.com/farahpoor/smkhe.

Computer Security↗
PubMed1987

Strategic approaches to drug design. I. An integrated software framework for molecular modelling.

An integrated molecular graphics and computational chemistry framework is described which has been designed primarily to handle small molecules of up to 300 atoms. The system provides a means of integrating software from any source into a single framework. It is split into two functional subsystems. The first subsystem, called COSMIC, runs on low-cost, serial-linked colour graphics terminals and allows the user to prepare and examine structural data and to submit them for extensive computational chemistry. Links also allow access to databases, other modelling systems and user-written modules. Much of the output from COSMIC cannot be examined with low level graphics. A second subsystem, called ASTRAL, has been developed for the high-resolution Evans & Sutherland PS300 colour graphics terminal and is designed to manipulate complex display structures. The COSMIC minimisers, geometry investigators, molecular orbital displays, electrostatic isopotential generators and various interfaces and utilities are described.

Computer Graphics↗
PubMed1989

Software that works in school settings. A framework for choosing the right program.

The use of the computer as a tool in therapy is a relatively new concept. As with any new concept, there are many questions, concerns, and problems in implementation. Those who do not know how to use a computer should seek help from the individual in their school who is responsible for the care and maintenance of the computer equipment. A majority of software programs require very little knowledge about operating a computer. Those who do not have the funds to purchase needed software programs should request support from their school's PTA and community businesses, apply for a state or federal grant, or use a nearby college or university lending library. If local computer stores only carry business-oriented software, software catalogues or computer magazines such as Teaching and Computers are sources for products. The magazines not only advertise but also critique software. Finally the computer application framework (Figure 1) should help clinicians determine how an individual piece of software can be chosen and implemented, based on a student's need and a clinician's work situation. No single piece of software is perfect for every student, nor is the public school environment perfect for the utilization of software. It is hoped that this article will help clinicians extract the best from both worlds by suggesting how the microcomputer can be used for therapy in a school setting.

Adolescent↗
PubMed1992

The chronic disease data bank model: a conceptual framework for the computer-based medical record.

The principles underlying the chronic disease data bank model are straight-forward: (1) the purpose of medical care is to improve patient outcomes; (2) patient outcomes in contemporary developed societies are overwhelmingly linked to chronic illnesses and degenerative processes and will become increasingly so; (3) such outcomes have multiple determinants including the psychological and social, as well as the biologic; (4) outcome antecedents (risk factors) may precede clinical illness by years or decades; and (5) these complex characteristics require for their study (a) computer aid and (b) longitudinal data. The chronic disease data bank provides an important resource, available to many investigators, for examination of the complex set of clinical and policy questions arising with long-term illness and addressing the questions of lifetime health. The chronic disease data bank consecutively enrolls eligible subjects, follows them for life, and amasses time-oriented, multidisciplinary data including clinical findings, medical history, demographics, treatments, resource utilization and disease outcomes, and assessing both the quality of life and its duration. Analyses are longitudinal and time-series in type, examining changes in trends and tempo of the disease, and are focused upon long-term outcomes. Outcomes are regularly and carefully assessed, and include the outcome dimensions of death, disability, discomfort, iatrogenic toxicity, and dollar cost. Specific studies address the description of the disease from biologic, demographic, economic, and social viewpoints, identify the factors associated with good and bad outcomes, and assess the effects of treatment, both good and ill. The clinical and policy goals are focused upon delaying transitions from more benign disease states to more serious ones, thus improving both longevity and the quality of life. The time is appropriate to consider generalization of the chronic disease data bank model to the usual clinical care situation, with large rewards in improvement of the quality of care. The traditional medical record lacks systematic documentation of end results and of important covariates related to health risks. As such, it cannot readily be used to assess the ultimate quality of care and to establish a feedback loop to change behaviors and thereby improve outcomes. It is weak where the chronic disease data bank is strong. The technology is transferable.

Chronic Disease↗
PubMed2026

Molecular origins of pH gradients in charge-regulated biomolecular condensates.

Biomolecular condensates exhibit spontaneous electrochemical microenvironments characterized by asymmetric ion distributions and pH gradients that emerge from protein-sequence-dependent charge regulation. Despite their biological importance, mechanistic understanding of these microenvironments has been constrained by the absence of computationally tractable frameworks capable of treating proton exchange, counterion partitioning, and buffer equilibria on consistent thermodynamic footing. Here, we introduce the buffered Charge-Regulation Monte Carlo (b-CR-MC) framework, which couples grand-canonical exchange of ions and buffer species with explicit charge regulation of titratable residues. By extending the CR-MC ion-merging strategy to multicomponent reservoirs and employing the restricted primitive model, b-CR-MC achieves computational efficiency while maintaining thermodynamic rigor, achievingquantitative agreement with the more expensive generalized grand-reaction Monte Carlo approach. Applied to full-length FUS (net positive) and PGL-3 (net negative) under physiological conditions, the framework reveals sequence-dependent pH gradients: the dense phase of FUS exhibits an alkaline shift, while that of PGL-3 exhibits an acidic shift, in both cases driving the condensate interior toward the protein's isoelectric point. Slab-geometry simulations further resolve the Donnan potential and continuous ion profiles across the condensate interface, confirming the direction of these electrochemical shifts. Additionally, we identify spatially resolved buffer depletion within dense phases, establishing that dynamic charge regulation is a primary determinant rather than a secondary correction to condensate electrochemistry. By establishing a sequence-resolved, thermodynamically consistent computational platform, b-CR-MC enables quantitative prediction of how mutations and post-translational modifications reprogram condensate microenvironments across biological and pathophysiological contexts.

Hydrogen-Ion Concentration↗
PubMed2026

Multicellular ecosystems: Linking cellular diversity to tissue function and disease.

Tissue function emerges from coordinated interactions among diverse cell populations, whereas disruption of these interactions can lead to dysfunction. Recent advances in single-cell and spatial genomics have not only cataloged cellular diversity but also revealed how tissues are organized as dynamic multicellular ecosystems. Moving beyond descriptive cell atlases toward functional, system-level representations represents a major frontier in tissue biology. In this review, we outline conceptual and methodological frameworks for dissecting multicellular coordination, highlight recurrent multicellular ecosystems across physiological and pathological contexts, and explore translational opportunities such as patient stratification, therapeutic reprogramming, and regenerative strategies. Viewing tissues through an ecosystem lens provides a unifying framework that links cellular diversity to emergent tissue function and informs strategies for disease intervention.

Humans↗
PubMed1991

[In-vitro research on the precision of the marginal adaptation of computer-milled titanium crowns (I). Scanning electron microscopy marginal fissure analysis].

The Digitising Computer System (DCS) can be mechanically programmed to produce computer-milled titanium frameworks for veneered ceramic single crowns using three-dimensional computer models. Titanium frameworks were constructed in this way during the present in-vitro investigation involving twelve extracted teeth with shoulder or chamfered preparation. An electron scanning microscope was used to evaluate the marginal accuracy of fit of the cemented crowns. The total length of 42.7 cm comprised by the crowns enabled 397 measuring points to be used. The arithmetical mean marginal leakage for the shoulder preparation amounted to 68.3 +/- 61.1 micrometers, and 95.7 +/- 83.2 micrometers for the chamfered one. From these results, it can be affirmed that the marginal accuracy of fit in constructing crowns by the Digitising Computer System is comparable to that of the conventional method.

Computer Graphics↗
PubMed2026

IBAS: Interaction-bridged association studies discovering novel genes underlying complex traits.

Genetic contributions to complex traits are often mediated through coordinated gene-gene interaction networks, yet most existing association frameworks focus on marginal single-gene effects and overlook higher-order dependency structures. Direct modeling of interactions remains challenging due to combinatorial complexity and statistical instability. We introduce Interaction-Bridged Association Study (IBAS), a general framework that incorporates pathway-level interaction patterns into genotype-phenotype association analysis without explicitly enumerating interactions. IBAS leverages transcriptomic reference data to construct low-dimensional representations of pathway activity, which guide SNP-weighting and gene-level association testing within a kernel-based framework. In perturbation-based simulations, IBAS demonstrates improved stability and reproducibility compared to conventional TWAS and gene-based methods, while maintaining well-calibrated Type I error under phenotype permutation. Application to the WTCCC datasets identifies both known and novel genes across multiple complex diseases, including candidates with modest marginal effects missed by standard approaches. These findings are supported by replication in an independent cohort, and analyses across multiple reference tissues revealing both shared and tissue-specific signals. Overall, IBAS provides a statistically robust and computationally tractable framework for incorporating interaction effects into association mapping, extending beyond the single-gene paradigm and enabling more comprehensive characterization of complex trait. IBAS is available on GitHub at: https://github.com/QingrunZhangLab/IBAS.

Polymorphism, Single Nucleotide↗
PubMed2026

NeuroOmics-Net: An interpretable multimodal deep learning framework for Alzheimer's disease diagnosis and progression prediction using neuroimaging, EEG, and genomic data.

Accurate diagnosis and progression prediction of Alzheimer's disease (AD) remain challenging due to the heterogeneous nature of the disease, which involves structural brain degeneration, electrophysiological dysfunction, and molecular dysregulation. Most existing deep learning approaches rely on a single modality or limited multimodal combinations, thereby failing to capture the complex cross-domain interactions underlying AD progression. Furthermore, the scarcity of large-scale datasets containing synchronized neuroimaging, electrophysiological, and genomic measurements restricts the development of comprehensive multimodal diagnostic systems. To address these challenges, this study proposes NeuroOmics-Net, a multimodal deep learning framework for Alzheimer's disease analysis that integrates structural magnetic resonance imaging (sMRI), electroencephalography (EEG), and gene expression data. The proposed framework combines a Hierarchical Multi-View Encoder (HME) for modality-specific feature extraction, a Cross-Omics Attention Fusion (CAF) module for adaptive integration of complementary biomarkers, and a Disease Progression Graph Learning (DPGL) module for modeling progression-related relationships across biological domains. To facilitate cross-modal integration from independent cohorts, Regularized Canonical Correlation Analysis (RCCA) is employed to align heterogeneous feature representations within a shared latent space. Experiments were conducted using publicly available datasets from ADNI, PhysioNet, and GEO repositories comprising 1120 diagnosis-aligned samples. The proposed framework achieved 94.3% classification accuracy and an AUC of 0.975 for distinguishing normal controls (NC), mild cognitive impairment (MCI), and Alzheimer's disease subjects, while attaining 93.7% accuracy for predicting conversion from stable mild cognitive impairment (sMCI) to progressive mild cognitive impairment (pMCI). However, a fairness sensitivity analysis using stratified demographic reweighting revealed accuracy ranging from 90.8% (low-education, high-comorbidity proxy subgroup) to 96.1% (low-risk, high-reserve proxy subgroup), a demographic parity gap of 5.3 percentage points, indicating that overall accuracy reflects a performance ceiling in a relatively homogeneous research cohort rather than a realistic estimate for demographically diverse clinical populations. Comparative evaluations demonstrated consistent improvements over state-of-the-art unimodal and multimodal deep learning models. Interpretability analysis further identified clinically relevant biomarkers, including hippocampal and entorhinal atrophy, theta-alpha EEG alterations, and APOE-associated molecular pathways. Because sMRI, EEG, and gene expression data were sourced from separate, unpaired cohorts with no subjects possessing all three synchronized measurements, all reported cross-modal associations reflect population-level statistical correspondence across diagnosis-matched groups rather than within-subject physiological coupling; no claim of intra-individual causal cross-modal interaction is made. These findings demonstrate that NeuroOmics-Net provides an effective computer-aided framework for multimodal biomedical data processing and Alzheimer's disease analysis. By integrating neuroimaging, electrophysiological, and genomic information, the proposed approach enables accurate diagnosis, progression prediction, and biologically interpretable decision support for clinical and translational applications.

Humans↗
PubMed2025

Biophysical metabolic modeling of complex bacterial colony morphology.

Microbial colony growth is shaped by the physics of biomass propagation and nutrient diffusion and by the metabolic reactions that organisms activate as a function of the surrounding environment. While microbial colonies have been explored using minimal models of growth and motility, full integration of biomass propagation and metabolism is still lacking. Here, building upon our framework for computation of microbial ecosystems in time and space (COMETS), we combine dynamic flux balance modeling of metabolism with collective biomass propagation and demographic fluctuations to provide nuanced simulations of E. coli colonies. Simulations produced realistic colony morphology, consistent with our experiments. They characterize the transition between smooth and furcated colonies and the decay of genetic diversity. Furthermore, we demonstrate that under certain conditions, biomass can accumulate along "metabolic rings" that are reminiscent of coffee-stain rings but have a completely different origin. Our approach is a key step toward predictive microbial ecosystems modeling. A record of this paper's transparent peer review process is included in the supplemental information.

Models, Biological↗
PubMed2026

Beyond antibiotics: artificial intelligence-enabled anti-infective ecosystems for next-generation precision therapeutics against antimicrobial resistance.

The rapid global expansion of antimicrobial resistance (AMR) threatens to undermine decades of progress in infectious disease management and highlights the limitations of conventional antibiotic-centered therapeutic strategies. Although emerging technologies-including antimicrobial peptides, bacteriophage therapy, CRISPR-based antimicrobials, microbiome therapeutics, anti-virulence approaches, nanotechnology-enabled drug delivery, and artificial intelligence (AI)-have individually demonstrated considerable promise, they are predominantly being developed as independent interventions rather than as coordinated components of an integrated therapeutic strategy. This Perspective proposes the Intelligent Anti-Infective Ecosystem (IAIE) as a conceptual systems-level framework that computationally integrates multimodal diagnostics, pathogen genomics, microbiome profiling, AI-assisted decision support, programmable precision therapeutics, ecological monitoring, and longitudinal clinical feedback within a continuously learning dynamically optimized workflow. Unlike existing paradigms that primarily optimize individual technologies or therapeutic decisions, IAIE emphasizes closed-loop coordination among complementary antimicrobial approaches to support precision-guided infection management while preserving microbiome integrity and mitigating resistance selection pressure. We further outline the core components, operational principles, translational challenges, and technology readiness of the major therapeutic platforms that could contribute to such an ecosystem, while distinguishing clinically established interventions from emerging experimental strategies. Importantly, IAIE should be interpreted as a prospective conceptual architecture rather than an existing clinical platform. Its proposed clinical value remains to be established through sequential computational, preclinical, and prospective clinical investigations using standardized microbiological, ecological, and patient-centered outcome measures. By framing antimicrobial innovation within an responsive systems perspective, IAIE provides a roadmap for future multidisciplinary research aimed at integrating artificial intelligence and systems microbiology to enable sustainable management of antimicrobial resistance.

Humans↗
PubMed2025

An educator framework for organizing Wikipedia editathons for computational biology.

MOTIVATION: Wikipedia is a vital open educational resource in computational biology; however, a significant knowledge gap exists between English and non-English Wikipedias. Reducing this knowledge gap via intensive editing events, or "editathons," would be beneficial in reducing language barriers that disadvantage learners whose native language is not English. Results: We present a framework to guide educators in organizing editathons for learners to improve and create relevant Wikipedia articles. As a case study, we present the results of an editathon held at the 2024 ISCB Latin America conference, in which ten new articles were created for the Spanish-language edition of Wikipedia. We also present a web tool, "compbio-on-wiki," which identifies relevant English Wikipedia articles missing in other languages. We demonstrate the value of editathons to expand the accessibility and visibility of computational biology content in multiple languages. AVAILABILITY AND IMPLEMENTATION: Source code for the compbio-on-wiki Toolforge site is available at: https://github.com/lubianat/compbio-on-wiki.

Computational Biology↗
PubMedDate not supplied

Computers and nursing. Possibilities for transforming nursing.

The use of computers is becoming commonplace in the clinical setting. However, the impact of computer use and its implications for nursing have yet to be understood (Birckhead, 1978). The purpose of this article is to explore how computer technology may transform nursing. The discourse is guided by Burch's (1985) thesis that "the use of technology is non-neutral. It transforms experience, whether for better or worse, and ultimately shapes human thinking and being". If one values nursing as a humanizing activity, then most of the potential transformations can be viewed as negative. When viewed from an instrumental framework, however, the computer may have a positive rather than a negative impact because computer use promotes expediency, efficiency, and precision.

Humans↗
PubMed2026

Integrated computational and experimental benchmarking of Bacillus phage endolysins reveals the relationship between peptidoglycan-fragment recognition descriptors and antibacterial performance.

Protein-based antibacterials such as bacteriophage endolysins offer a targeted therapeutic strategy against Gram-positive pathogens. However, prioritizing the most effective candidates from the large sequence diversity available remains a significant challenge. Here we present a standardized computational-experimental benchmarking framework that evaluates seven phage-derived endolysin variants (E1, E2, E3, E7, E10, E12, and E15) identified from Bacillus genomes. We combined molecular docking and residue-level interaction mapping against muramyl dipeptide (MDP), a minimal conserved peptidoglycan motif, with 1000-ns molecular dynamics simulations, MM/PBSA binding free-energy estimation, and matched functional inhibition assays against Staphylococcus aureus and Micrococcus luteus. Computational analyses revealed generally favorable MDP recognition across variants, albeit with notable differences in contact patterns and complex stability profiles. Experimental screening identified E2 as the most potent antibacterial agent against both species, while E7 and E1 performed strongly in selected computational metrics. Integrated analysis showed only modest correlations between computational descriptors of fragment recognition/stability and observed antibacterial performance. This study establishes a practical comparative benchmarking platform for endolysin candidate prioritization, nominates E2 and E7 as promising candidates for further development, and highlights E1 as a potential structural scaffold for rational engineering, while explicitly demonstrating both the utility and the current limitations of using minimal peptidoglycan fragments as proxies for full cell-wall recognition in lysin benchmarking.

Endopeptidases↗