SEARCH · PubMed Health

Results for “computational frameworks”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 163 records · Page 9Linked to original sources

PubMed2025

SIGEL: a context-aware genomic representation learning framework for spatial genomics analysis.

Spatial transcriptomics (ST) integrates spatial information into genomics, yet methods for generating spatially-informed gene representations are limited and computationally intensive. We present SIGEL, a cost-effective framework that derives gene manifolds from ST data by exploiting spatial genomic context. The resulting SIGEL-generated gene representations (SGRs) are context-aware, biologically meaningful, and robust across samples, making them highly effective for key downstream tasks, including imputing missing genes, detecting spatial expression patterns, identifying disease-related genes and interactions, and improving spatial clustering. Extensive experiments across diverse ST datasets validate SIGEL's effectiveness and highlight its potential in advancing spatial genomics research.

Genomics↗

PubMed1994

The hierarchical Bayesian approach to population pharmacokinetic modelling.

Compartmental models are widely used to model the profile of drug concentrations versus time from administration in an individual subject. Observed concentrations are then modelled as noisy departures from the underlying profile, the latter characterised for each individual by a small number of 'individual parameters'. When a population of individuals is studied, inter-individual variation is modelled by assuming that the individual profile parameters are drawn from a population distribution, the latter characterised by 'population parameters' describing, in effect, a mean population profile and individual variation around it. From a Bayesian statistical perspective, such models fit exactly into the so-called hierarchical modelling framework, which provides a coherent basis for individual and population inferences and prediction, as well as for decision-making (for example, the design of dosage regimens). This paper outlines the hierarchical model framework and describes how the required computations can be carried out in a straightforward manner by a Markov chain Monte Carlo technique known as Gibbs sampling, even when models involve mean-variance relationships and outliers.

Bayes Theorem↗

PubMed1995

Construction and characterization of a humanized, internalizing, B-cell (CD22)-specific, leukemia/lymphoma antibody, LL2.

The murine monoclonal antibody, LL2, is a B-cell (CD22)-specific IgG2a which has been demonstrated to be clinically significant in the radioimmunodetection of non-Hodgkin's B-cell lymphoma. The antibody carries a variable region-appended glycosylation site in the light chain and is rapidly internalized upon binding to Raji target cells. Humanization of LL2 was carried out in order to develop LL2 as a diagnostic and immunotherapeutic suitable for repeated administration. Based on the extent of sequence homology, and with the aid of computer modeling, we selected the EU framework regions (FR) 1, 2 and 3, and the NEWM FR4 as the scaffold for grafting the heavy chain complementarity determining regions (CDRs), and REI FRs for that of light chains. The light chain glycosylation site, however, was not included. Construction of the CDR-grafted variable regions was accomplished by a rapid and simplified method that involved long DNA oligonucleotide synthesis and the polymerase chain reaction (PCR). The humanized LL2 (hLL2), lacking light chain variable region glycosylation, exhibited immunoreactivities that were comparable to that of chimeric LL2 (cLL2), which was shown previously to have antigen-binding properties similar to its murine counterpart, suggesting that the VK-appended oligosaccharides found in mLL2 are not necessary for antigen binding. Moreover, the hLL2 retained its ability to be internalized into Raji cells at a rate similar to its murine and chimeric counterparts.

Amino Acid Sequence↗

PubMed1992

Modelling of intersegmental coordination in the lamprey central pattern generator for locomotion.

Rhythmic motor activity requires coordination of different muscles or muscle groups so that they are all active with the same cycle duration and appropriate phase relationships. The neural mechanisms for such phase coupling in vertebrate locomotion are not known. Swimming in the lamprey is accomplished by the generation of a travelling wave of body curvature in which the phase coupling between segments is so controlled as to give approximately one full wavelength on the body at any swimming speed. This article reviews work that has combined mathematical analysis, biological experimentation and computer simulation to provide a conceptual framework within which intersegmental coordination can be investigated. Evidence is provided to suggest that in the lamprey, ascending coupling is dominant over descending coupling and controls the intersegmental phase lag during locomotion. The significance of long-range intersegmental coupling is also discussed.

Animals↗

PubMed1996

Measuring fit at the implant prosthodontic interface.

Four centers in the United States and Sweden have been working for 2 years to develop systems and methods for measuring fit at the prosthodontic interface. Two systems are based on stylus contact techniques, one system uses a laser as its reader source, and one system is photogrammetric. All the systems are capable of providing data as three-dimensional x, y, and z axes coordinate values that can be transformed into linear and angular data that characterize the bearing surfaces of abutments or abutment replicas and their mating components in the prosthesis framework. The centroid, a single point computed from the collected data, was the measurement unit, derived for these bearing surfaces, that was used to compare the systems. All four methods can most likely detect misfits that are relevant in the clinical setting; however, only one system can be used intraorally. When any measurement system is assessed, the data should always be examined for repeatability to establish the reliability of the system. This investigation made comparisons among the measurement methods used at the four centers. It was apparent from this study that comparisons of data from measurement systems should be rounded to the nearest 10 microns. The SDs determined in the comparisons were larger than 5 microns and therefore misfits should be calculated in terms smaller than 10 microns. This final point is important to the clinician who relies on research reports about precision of fit when selecting treatment approaches in caring for the implant prosthodontic needs of their patients.

Calibration↗

PubMed1986

Role of electrical interactions in synchronization of epileptiform bursts.

Four general mechanisms can hypothetically contribute to or mediate localized synchronization of neuronal activity: (a) recurrent excitatory chemical synapses, (b) electrotonic coupling via gap junctions, (c) electrical field effects (ephaptic interactions), and (d) changes in the concentration of extracellular ions (e.g., K+). It has generally been believed that synchronization of epileptiform bursts derives primarily, if not exclusively, from recurrent excitatory chemical synapses. Dual intracellular recordings from the CA3 area of the hippocampus have been used to demonstrate the existence of recurrent synaptic excitation, and computer simulations have provided a theoretical framework for the idea that relatively sparse interactions through recurrent excitatory chemical synapses can generate synchronized bursting after inhibitory pathways are blocked with convulsant agents. Additional experimental studies have supported the hypothesis that a model for seizure discharge, the penicillin-induced paroxysmal depolarization shift (PDS), is associated with a large increase in excitatory synaptic conductance. However, recent studies have suggested that electrical interactions are also likely to play an important role in spike synchronization during epileptic discharges. Several research groups have used in vitro preparations to show that afterdischarges and spontaneous bursts of population spikes (which represent synchronized action potentials) can occur after chemical synaptic transmission has been blocked in solutions containing low [Ca2+]. Although this result was first observed in the CA1 area, it has recently been confirmed in other regions of the hippocampus. These experiments indicate that mechanisms other than chemical synaptic transmission are capable of synchronizing action potentials in the hippocampus. In this chapter, two forms of electrical interaction that could mediate synchronization will be considered: (a) electrotonic coupling through gap junctions and (b) electrical field effects through extracellular space. Changes in the concentration of extracellular ions are another mechanism not involving chemical synapses. However, it seems unlikely that ionic changes act on the rapid time scale of electrical interactions, and their contribution is discussed elsewhere in this volume. We review evidence for the existence of electrotonic coupling and electrical field effects in the hippocampus and neocortex, and discuss their possible involvement in the synchronization of epileptiform events.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗

PubMed1995

Disulfide stabilization of antibody Fv: computer predictions and experimental evaluation.

Using molecular modeling technology we have recently identified positions in conserved framework regions of Fvs which can be used to stabilize antibody Fvs by an interchain disulfide bond engineered in between the structurally conserved framework positions of the variable domains of heavy (VH) and light (VL) immunoglobulin chains (disulfide-stabilized Fv; dsFv). The computer model indicated the existence of other potential sites in the framework regions that might be suitable for disulfide bond formation between VH and VL. The possibility of obtaining dsFvs using these positions is evaluated here experimentally by constructing dsFv immunotoxins in which the Fv moiety is fused to a truncated form of Pseudomonas exotoxin. We analyzed the extent of dsFv formation and the activity of the resulting dsFv immunotoxins, and compared various dsFv molecules with the scFv immunotoxin. Our results demonstrate that position H44-L105 is the only one which gives high production yields of active dsFv. All other positions gave either low yields and activity or completely failed to produce active dsFv. With one exception, the formation and activities of the dsFvs corresponded to the C alpha-C alpha distance between the VH and VL positions, with an optimal distance of 5.7 A producing the best dsFv. Distances of 6.0-6.9 A resulted in a low yield of protein that was still capable of binding antigen, whereas distances > 7.0 A resulted in molecules in which dsFv formation was not obtained.

Amino Acid Sequence↗

PubMed1996

Toward a model for nursing informatics.

PURPOSE: To propose a new model for the development of nursing informatics based on historical precedent. SIGNIFICANCE: Nursing informatics is expanding rapidly. The proposed model aids in understanding the areas of research, relating them to each other, and it shows areas where work is missing or should be extended. ORGANIZING FRAMEWORK: Nursing informatics as the interaction of cognitive science, computer science, and information science resting on a base of nursing science. IMPLICATIONS: As this model is tested, it can act as an organizing framework to understand and relate studies of nursing informatics and give organization for future research, education, and development.

Computer Communication Networks↗

PubMed1995

Protein docking combining symbolic descriptions of molecular surfaces and grid-based scoring functions.

With the growing number of known 3D protein structures, computing systems, that can predict where two protein molecules interact with each other is becoming of increasing interest. A system is presented, integrating preprocessing like the computation of molecular surfaces, segmentation, and searching for complementarity in the general framework of a pattern analyzing semantic network (ERNEST). The score of coarse symbolic computations is used by the problem independent control strategy of ERNEST to guide a more detailed analysis considering steric clash and judgements based on grid-based surface representations. Successful examples of the docking system are discussed that compare well with other approaches.

Binding Sites↗

PubMed1993

A constraint logic programming framework for constructing DNA restriction maps.

Restriction mapping is an important computational problem in molecular biology, particularly in genetic engineering and DNA sequencing. It is different in that it is not only a purely computational problem but involves an interaction between experimental data collection procedures and the mapping algorithms. Consequently, the problem is loosely defined and in practice requires a flexible and versatile algorithm. We describe a framework for solving many restriction mapping problems in the constraint logic programming language CLP (R) which takes advantage of the declarative and powerful features of constraint logic programming. A CLP (R) algorithm is developed for solving a simple restriction mapping problem. The algorithm is the extended to handle more complex variations of restriction mapping such as fragments with errors, circular maps, multiple enzymes and partial digests. The mapping variants are integrated within the same framework and differ in the constraints required to define the kind of map consistency. Various search heuristics and control strategies to improve the search process are also incorporated as constraints.

Algorithms↗

PubMedDate not supplied

Graph automorphism perception algorithms in computer-enhanced structure elucidation.

The concept of graph symmetry is explained in terms of the vertex automorphism group, which is a subgroup of the complete vertex permutation group. The automorphism group can be deduced from the automorphism partition of graph vertices. An algorithm is described which constructs the automorphism group of a graph from the automorphism vertex partitioning. The algorithm is useful especially for graphs which contain more than one vertex-partition set. Several well-known topological symmetry perception algorithms that yield automorphism partitions are compared. The comparison is favorable to the Shelley-Munk algorithm, developed in the framework of the SESAMI system for computer-enhanced structure elucidation.

Algorithms↗

PubMed1994

A humanized antibody specific for the platelet integrin gpIIb/IIIa.

C4G1, a murine mAb reactive with the platelet gpIIb/IIIa integrin, was humanized for potential treatment of thrombosis-related disorders. The variable regions of light- and heavy-chain cDNAs from the C4G1 hybridoma were first cloned and sequenced. Humanized C4G1 Ab of the IgG1 isotype was constructed by combining the complementarity-determining regions of C4G1 with human framework and constant regions. The human framework was chosen to maximize homology with the C4G1 variable region sequence, and a computer model of C4G1 was used to aid design of the final framework sequence. Genetic constructs were also developed to produce Fab and F(ab')2 fragments of the humanized C4G1 Ab. The humanized IgG1 Ab as well as the Fab and F(ab')2 fragments showed equivalent binding affinities to their murine counterparts, indicating no loss in binding affinity during the humanization process. The humanized Ab and its fragments were also shown to inhibit platelet aggregation and to inhibit binding of fibrinogen to gpIIb/IIIa in vitro.

Amino Acid Sequence↗

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↗

PubMed1993

Application of three-dimensional molecular hydrophobicity potential to the analysis of spatial organization of membrane domains in proteins. III. Modeling of intramembrane moiety of Na+, K(+)-ATPase.

The most probable interlocation of transmembrane alpha-helices of Na+, K(+)-ATPase has been calculated by a computer-aided molecular simulation approach in the framework of models with eight and 10 helical peptides for the alpha-subunit. The method is based on the concept of three-dimensional molecular hydrophobicity potential (MHP) and provides valuable description of spatial hydrophobic properties of membrane-spanning segments as well as helix-helix packing interactions inside the membrane. Resulting model of the arrangement of intramembrane domain agrees with recent results on hydrophobic photolabeling of an intramembrane part of the beta-subunit and the sixth transmembrane segment of the alpha-subunit. It is also consistent with current ideas on hydrophobic organization of integral membrane proteins. Possible topology of a cation-binding site is discussed.

Cell Membrane↗

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↗