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At least 127 records · Page 7Linked to original sources

PubMed2026

Secure bioinformatics: privacy-preserving federated analytics using homomorphic encryption.

MOTIVATION: Large-scale bioinformatics analyses increasingly require collaboration across multiple cohorts and institutions, yet existing workflows often rely on data co-localization, which is slow, difficult to scale, and raises privacy concerns. We present a privacy-preserving federated analytics framework that enables secure statistical analysis across distributed datasets without transferring raw data, by performing all computations on encrypted data via cryptographic methods. RESULTS: We evaluate the framework by validating polygenic risk scores and conducting meta-analyses on two real-world cohorts. The proposed solution achieves over 99.9% accuracy relative to plaintext analyses, while maintaining scalable runtime performance with increasing data size and number of participating sites. These results demonstrate the feasibility of secure federated analytics for practical bioinformatics applications involving sensitive data.

Computational Biology↗

PubMed1992

Continuum electrostatics of the C-peptide: anatomy of the problem.

A computational study of the role of all ionizable groups of the C-peptide in its helix-coil transition is performed within the framework of continuum electrostatics. The method employed in our computations involves a numeric solution of the Poisson equation with the Boundary Element Method. Our calculations correctly predict the experimentally observed trends in the helix-coil equilibrium of the C-peptide, and suggest that the mechanisms involved are more complex than usually presumed in the literature. Our results suggest that electrostatic interactions in the unfolded conformation are often more important than in the helix, total electrostatic contribution to the helix-coil transition due to the side chains of the C-peptide destabilizes the helix, changes in the helix stability produced by the changes in the ionization state of the side chains are dominated by side chain effects, the effect of the helix dipole on the energetics of the helix-coil transition of the C-peptide is either minor or similar to other contributions in magnitude; while the formation of a salt bridge is electrostatically favorable, formation of the hydrogen bond between a charged and a polar side chains is not. Factors limiting the accuracy of the computations are discussed.

C-Peptide↗

PubMed2026

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor antigens. Early clinical studies have demonstrated the feasibility, safety, and immunogenicity of these vaccines across multiple solid tumors, with encouraging outcomes particularly when combined with immune checkpoint blockade. However, broader clinical translation remains limited by sequential bottlenecks across the vaccine development pipeline, including false-positive neoantigen selection,  imperfect modeling of antigen processing and HLA presentation, limited prediction of T-cell receptor recognition, and challenges in formulation, delivery, and manufacturing. Artificial intelligence and advanced computational workflows are increasingly integrated into this pipeline to improve candidate prioritization and support more reproducible decision-making. In this review, we summarize clinical progress and key translational barriers in personalized neoantigen vaccination, and discuss how AI-enabled approaches may contribute across four major stages: multi-omics integration for neoantigen discovery, processing-aware HLA presentation prediction, structure-aware and TCR-informed immunogenicity modeling, and data-driven formulation optimization, particularly for lipid nanoparticle-based delivery systems. These approaches are able to help narrow biological and chemical search spaces, improve prioritization, and provide mechanistic insights into antigen presentation and immune recognition rather than replacing experimental validation. This articlefurther addresses future implementation challenges, including dataset diversity, model interpretability, prospective benchmarking, manufacturing traceability, and evolving regulatory frameworks for individualized mRNA cancer immunotherapies. Integrating computational innovation with rigorous immunological validation, scalable manufacturing, and regulatory oversight will be essential for advancing personalized neoantigen vaccines toward broader clinical implementation.

Cancer Vaccines↗

PubMed2026

Transfer Learning across Material Properties Using Center-Environment Features: From Energetics to Mechanical Properties in Multicomponent Mo Alloys.

Transfer learning (TL) provides a viable approach to mitigate data scarcity in materials informatics. While conventional TL focuses on predicting identical properties across different systems, this work demonstrates a cross-property extension of TL from energy to mechanical properties via end-to-end model weight pre-training and fine-tuning: knowledge learned from predicting substitution energies is transferred to predict distinctly different mechanical properties, substantially improving computational efficiency given the typically higher cost of acquiring target-domain data. To accelerate computational alloy design, machine learning models using center-environment (CE) features were first developed to predict substitution energies of alloying elements in molybdenum (Mo)-based alloys. The Random Forest models achieved the optimal performance and transferability-R2 = 0.97, 〈MAE〉 = 0.11 eV, and 〈RMSE〉 = 0.16 eV-against the density functional theory (DFT) benchmark. The model dependency of feature selection and importance analysis was discussed. The transferability of the energy models was validated on unknown systems with new elements. Subsequently, the energy models were fine-tuned using limited mechanical property data to construct energy-to-property (E2P) TL models capable of predicting elastic properties, including bulk modulus, Young's modulus, shear modulus, and elastic constants, achieving an improved accuracy over the non-transferred ML by ∼10-30%, with its transferability verified by additional DFT calculations. This cross-property E2P transfer learning framework opens a new avenue for accelerating computational materials discovery and may be extended to other multiproperty predictions governed by similar physical principles.

center-environment feature↗

PubMed2026

Biological Parts in Yeast Synthetic Biology: From Regulatory Elements to Predictive Design Platforms.

Yeasts, particularly Saccharomyces cerevisiae, are important eukaryotic chassis for synthetic biology because of their tractable genetics, versatile toolkits, and broad utility in metabolic engineering and functional genomics. Progress in this field has been driven by biological parts that enable programmable control of gene expression and cellular behavior. Early efforts focused mainly on promoters, terminators, and other regulatory elements for tuning individual genes. However, as engineering expanded to multigene pathways, genetic circuits, and dynamic regulatory systems, the limits of part-centric design became clear. Part performance is often shaped by genomic context, chromatin state, host physiology, and interactions with other components, which restricts modularity and predictability. In response, yeast synthetic biology is shifting toward integrated design frameworks combining multilayer regulation, standardized assembly, automated experimentation, and computational modeling. This review provides an integrated perspective on the evolution of biological parts across DNA-, RNA-, and protein-level regulation, connecting these advances with assembly frameworks, biofoundries, and machine learning to trace the trajectory from part-centric engineering toward predictive, system-level design in yeast synthetic biology.

Biofoundry↗

PubMed2025

Characterizing the regulatory logic of transcriptional control at the DNA sequence level by ensembles of thermodynamic models.

MOTIVATION: Understanding how the genome encodes the regulatory logic of transcription is a main challenge of the post-genomic era, and can be overcome with the aid of customized computational tools. RESULTS: We report an automated framework for analyzing an ensemble of fits to data of a thermodynamics-based sequence-level model for transcriptional regulation. The fits are clustered accordingly with their intrinsic regulatory logic. A multiscale analysis enables visualization of quantitative features resulting from the deconvolution of the regulatory profile provided by multiple transcription factors interacting with the locus of a gene. Quantitative experimental data on reporters driven by the whole locus of the even-skipped gene in the blastoderm of Drosophila embryos was used for validating our approach. A few clusters of highly active DNA binding sites within the enhancers collectively modulate even-skipped gene transcription. Analysis of variable enhancers' length shows the importance of bound protein-protein interactions for transcriptional regulation. The interplay between activation and quenching enables function conservation of enhancers despite length variations. AVAILABILITY AND IMPLEMENTATION: The transcription factor level data used for performing the reported study is accessible in the input files in Zenodo and GitHub as well the full code. Additional data from formerly FlyEx database will be available under request.

Thermodynamics↗

PubMed1980

Towards a rationale for neural stability: a model of neural computation and network architecture.

Networks of Boolean processing cells with low connectivity are known to be inherently stable, in the sense that they exhibit only limited reverberatory activity among their possible state transitions. This paper discusses the value of such a network as a functional model of a neural system and, in the light of the observed decrease in stability with increasing cell connectivity, seeks to identify the features of network architecture and cell computation which act to protect network stability, thereby providing a framework for an understanding of neural stability.

Cell Communication↗

PubMed2025

CaXML: Chemistry-informed machine learning explains mutual changes between protein conformations and calcium ions in calcium-binding proteins using structural and topological features.

Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes. However, it is unclear whether the limited experimental data available can be effectively used to train models to accurately predict the charges of calcium-binding protein variants. Here, we developed a chemistry-informed, machine-learning algorithm that implements a game theoretic approach to explain the output of a machine-learning model without the prerequisite of an excessively large database for high-performance prediction of atomic charges. We used the ab initio electronic structure data representing calcium ions and the structures of the disordered segments of calcium-binding peptides with surrounding water molecules to train several explainable models. Network theory was used to extract the topological features of atomic interactions in the structurally complex data dictated by the coordination chemistry of a calcium ion, a potent indicator of its charge state in protein. Our design created a computational tool of CaXML, which provided a framework of explainable machine learning model to annotate ionic charges of calcium ions in calcium-binding proteins in response to the chemical changes in an environment. Our framework will provide new insights into protein design for engineering functionality based on the limited size of scientific data in a genome space.

Machine Learning↗

PubMed1990

An algorithm for approximating conditional probabilities.

When diagnostic programs are constructed within a probabilistic framework, it is often the case that computation of joint probabilities of exhaustive combinations of events is easy, but computation of the kind of conditional probabilities the user wishes to know, is hard. This paper describes a simple algorithm for computing the required values, and then suggests several heuristic optimizations that may enable suitable approximations to be obtained in a feasible time when the task is otherwise intractable. An account is given of a specific application of the method in the construction of a medical diagnostic program, which is described in more detail elsewhere.

Algorithms↗

PubMed2026

Leveraging Interradiomic Feature Relationships for Enhanced Prediction of Distant Metastasis and Characterization of Heterogeneity in Head and Neck Cancer.

PURPOSE: Distant metastasis remains a major cause of treatment failure in head and neck (HN) cancer, highlighting the need for more accurate early risk stratification. This study developed and validated a deep radiomics framework to characterize tumor heterogeneity from pretreatment computed tomography (CT) images and improve prediction of distant metastasis-free survival (DMFS). METHODS AND MATERIALS: This multicenter study included 3421 patients with HN cancer from 4 cohorts across 12 institutions. Radiomics features were extracted from primary tumors and transformed into OmicsMaps, a structured representation that spatially organizes interfeature relationships to facilitate learning of complex prognostic patterns. A convolutional neural network was trained to derive prognostic signatures, which were integrated with key clinical variables to construct an OmicsMap-clinical fusion model for patient risk stratification. Model performance was assessed using the concordance index (C-index) and time-dependent area under the receiver operating characteristic curve (AUC) in the CT Images from Large Head and Neck Cohort (RADCURE), HEAD-NECK-RADIOMICS-HN1 (HN1), and Head-Neck-Positron Emission Tomography-Computed Tomography (HN-PET-CT) cohorts. Radiogenomic analyses using RNA-seq data were conducted in the Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) cohort to investigate biological characteristics associated with the imaging-defined risk groups. RESULTS: The OmicsMap achieved C-index values of 0.742, 0.768, and 0.671 in the RADCURE, HN1, and HN-PET-CT cohorts, outperforming the conventional radiomics approach by 5.40%-6.37%. Incorporating clinical variables further improved generalizability, yielding a C-index of 0.864 (HN1) and 0.730 (HN-PET-CT), with time-dependent AUC of 0.727-0.895. The fusion model consistently stratified patients into distinct high- and low-risk groups for both DMFS and overall survival across cohorts (P <.01). Radiogenomic analyses revealed enrichment of immune-related pathways in the low-risk group, whereas the high-risk group exhibited a more aggressive phenotype enriched for proliferation, hypoxia, and epithelial-mesenchymal transition pathways, along with a fibrosis-prone tumor microenvironment characterized by extracellular matrix remodeling. CONCLUSIONS: Modeling interradiomic feature relationships using the OmicsMap representation substantially improves CT-based prediction of DMFS and characterization of tumor heterogeneity in HN cancer, supporting precision risk stratification in clinical oncology.

Journal Article↗

PubMed2025

WinPCA: a package for windowed principal component analysis.

SUMMARY: With chromosomal reference genomes and population-scale whole genome-sequencing becoming increasingly accessible, contemporary studies often include characterizations of the genomic landscape as it varies along chromosomes, commonly termed genome scans. While traditional summary statistics like FST and dXY between pre-assigned populations remain integral to characterizing the genomic divergence profile, PCA differs by providing single-sample resolution, thereby supporting the identification of polymorphic inversions, introgression and other types of divergent sequence that may not be fully aligned with global population structure. Here, we introduce WinPCA, a user-friendly package to compute, polarize and visualize genetic principal components in windows along the genome. To accommodate low-coverage whole genome-sequencing datasets, WinPCA can optionally make use of PCAngsd methods to compute principal components in a genotype likelihood framework. WinPCA accepts variant data in either VCF or BEAGLE format and can generate rich plots for interactive data exploration and downstream presentation. AVAILABILITY AND IMPLEMENTATION: WinPCA is implemented in Python and freely available at https://github.com/MoritzBlumer/winpca and https://doi.org/10.5281/zenodo.15614979.

Software↗

PubMed1972

The role of operational research in clinical chemistry.

This paper begins with a critical examination of the published account (Carruthers, 1970) of a pioneering study of simulation in a clinical chemistry laboratory. It next considers the basic requirements for the formal mathematical description of such a laboratory: these include the setting of criteria, the resource and input variates, and the definition of success by means of criterion variates. Investigation of criterion variates can be by direct measurement, or by a theoretical approach which may include simulation. The aims of simulation are discussed and an attempt to simulate the operation of a large clinical chemistry laboratory is described. The difficulty of constructing an overall measure of efficiency is considered in relation to improving performance within an existing framework, to evaluating new equipment such as computers on-line to laboratory apparatus, and to inter-laboratory comparisons of performance. It is concluded that complex operational research techniques, including simulation, have little to offer at least for the present, and may even lead to misleading conclusions.

Chemistry, Clinical↗

PubMed1985

Risk assessment extrapolations and physiological modeling.

The process of assessing the risk associated with human exposure to environmental chemicals inevitably relies on a number of assumptions, estimates and rationalizations. One of the more challenging aspects of risk assessment involves the need to extrapolate beyond the range of conditions used in experimental animal studies to predict anticipated human risks. The most obvious extrapolation required is that from the tested animal species to humans; but others are also generally required, including extrapolating from high dose to low dose, from one route of exposure to another and from one exposure timeframe to another. Several avenues are available for attempting these extrapolations, ranging from the assumption of strict correspondence of dose to the use of statistical correlations. One promising alternative for conducting more scientifically sound extrapolations is that of using physiologically based pharmacokinetic models that contain sufficient biological detail to allow pharmacokinetic behavior to be predicted for widely different exposure scenarios. In recent years, successful physiological models have been developed for a variety of volatile and nonvolatile chemicals, and their ability to perform the extrapolations needed in risk assessment has been demonstrated. Techniques for determining the necessary biochemical parameters are readily available, and the computational requirements are now within the scope of even a personal computer. In addition to providing a sound framework for extrapolation, the predictive power of a physiologically based pharmacokinetic model makes it a useful tool for more reliable dose selection before beginning large-scale studies, as well as for the retrospective analysis of experimental results.

Animals↗

PubMed1975

Physical basis of charge pairing in mitochondria.

The postulate of charge pairing in the mitochondrial inner membrane is justified by applying a formula due to Fuoss to calculate the probability density for the distance between a positive and a negative charge. For dielectric constants 10 or less pairing is absolute, for 20 there is some tendency towards pairing, and at 78 it is nonexistent. Pairing, partner exchange or charge substitution, inhibition, and antiport uncoupling can be rationalized within this framework.

Computers↗

PubMed1989

Decision analysis: a framework for critical care decision assistance.

The ultimate goal of medical computer systems is to help clinicians make good decisions. Such systems must be based on sound principles. Decision analysis is a 25-year-old discipline that provides the needed rigorous foundation for decision assistance. Decision analysis comprises the philosophy, procedures, and tools that can correct the flaws in existing critical care decision-making practice. Intelligent decision systems--computer-based systems that automate decision analysis--make it practical to apply decision analysis to critical care. Orchestra is a pilot intelligent decision system (now under development) that coordinates the efforts of the critical care specialist, the bedside physician, and the bedside nurse in building decision models that can provide recommendations and insight for ventilator management decisions. Decision analysis delivered by intelligent decision systems has great potential for improving critical care decision-making.

Algorithms↗

PubMed2026

AI-genomics synergy for drug repurposing in breast cancer: an interpretability-driven framework.

Breast cancer's genomic heterogeneity complicates drug discovery, making repurposing an attractive but challenging strategy. Advances in artificial intelligence now enable integration of multi-omics data to reveal drug-gene-disease relationships and generate subtype-specific repurposing hypotheses. In this Review, we examine AI-driven computational approaches from signature-based to multi-modal frameworks and propose an integrated interpretability-driven framework linking mechanistic validation with clinical translation toward more transparent and actionable precision oncology.

Journal Article↗

PubMed2026

Comparative and Subtractive Genomics Analysis of Multidrug-Resistant Klebsiella pneumoniae Strains for Novel Target Identification and Drug Repurposing Strategies.

The rapid rise of multidrug-resistant (MDR) Klebsiella pneumoniae has created a major global health challenge due to the limited availability of conserved therapeutic targets effective across diverse resistant strains. In this study, an integrative computational target-discovery and drug-repurposing framework was applied to six clinically relevant K. pneumoniae strains. Comparative genomic analysis identified 3012 conserved genes, which were subsequently filtered to nine essential, non-host homologous proteins. Among these, three conserved cytoplasmic proteins (accD, cpxR, and mraZ) were prioritized for functional analysis, with acetyl-CoA carboxylase subunit beta (accD) emerging as the most promising therapeutic target based on sequence conservation, predicted essentiality, subcellular localization, and pathway association. Structural assessment supported the reliability of the predicted accD model, whereas consensus binding-site analysis identified key residues suitable for ligand interaction. Virtual screening of FDA-approved drugs followed by molecular docking identified several compounds with favorable binding profiles toward accD. Subsequent molecular dynamics simulations, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), hydrogen-bond occupancy, principal component analysis (PCA), and PCA-based free energy landscape (FEL) analyses, consistently identified tenapanor, micafungin, deferoxamine, and cobicistat as the most stable protein-ligand complexes, with tenapanor exhibiting the most favorable overall structural and thermodynamic stability profile. These findings identify accD as a promising therapeutic target in MDR K. pneumoniae and suggest several FDA-approved compounds as potential candidates for drug repurposing. Although experimental validation is needed to confirm their biological activity and therapeutic potential, this study demonstrates the potential of integrating comparative genomics with molecular dynamics analyses to support antimicrobial target identification and drug repurposing against MDR bacterial pathogens.

Klebsiella pneumoniae↗

PubMed1992

Cluster analysis and related techniques in medical research.

In this paper we review methods of cluster analysis in the context of classifying patients on the basis of clinical and/or laboratory type observations. Both hierarchical and non-hierarchical methods of clustering are considered, although the emphasis is on the latter type, with particular attention devoted to the mixture likelihood-based approach. For the purposes of dividing a given data set into g clusters, this approach fits a mixture model of g components, using the method of maximum likelihood. It thus provides a sound statistical basis for clustering. The important but difficult question of how many clusters are there in the data can be addressed within the framework of standard statistical theory, although theoretical and computational difficulties still remain. Two case studies, involving the cluster analysis of some haemophilia and diabetes data respectively, are reported to demonstrate the mixture likelihood-based approach to clustering.

Algorithms↗