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At least 685 records · Page 38Linked to original sources
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↗
PubMed1997

Computing machinery and mentality.

I reconsider the status of computationalism (or, in a weak sense, functionalism): the claim that being a realization of some (as yet unspecified) class of abstract machine is both necessary and sufficient for having genuine, full-blooded, mentality. This doctrine is now quite widely (though by no means universally) seen as discredited. My position is that, though it is undoubtedly an unsatisfactory (perhaps even repugnant) thesis, the arguments against it are still rather weak. In particular, I critically reassess John Searle's infamous Chinese Room Argument and also some relevant aspects of Karl Popper's theory of the Open Universe. I conclude that the status of computationalism must still be regarded as undecided, and that it may still provide a satisfactory framework for research.

Animals↗
PubMed2004

High-performance computing service over the internet for intraoperative image processing.

This paper presents a framework for a cluster system that is suited for high-resolution image processing over the Internet during surgery. The system realizes high-performance computing (HPC) assisted surgery, which allows surgeons to utilize HPC resources remote from the operating room. One application available in the system is an intraoperative estimator for the range of motion (ROM) adjustment in total hip replacement (THR) surgery. In order to perform this computation-intensive estimation during surgery, we parallelize the ROM estimator on a cluster of 64 PCs, each with two CPUs. Acceleration techniques such as dynamic load balancing and data compression methods are incorporated into the system. The system also provides a remote-access service over the Internet with a secure execution environment. We applied the system to an actual THR surgery performed at Osaka University Hospital and confirmed that it realizes intraoperative ROM estimation without degrading the resolution of images and limiting the area for estimations.

Arthroplasty, Replacement, Hip↗
PubMed2004

Rational design of the pore system within the framework aluminium alkylenediphosphonate series.

We report here on the solvothermal synthesis and crystal structure of the hybrid organic-inorganic framework material Al(2)[O(3)PC(3)H(6)PO(3)](H(2)O)(2)F(2).H(2)O (orthorhombic, Pmmn, a = 12.0591(2) A, b = 19.1647(5) A, c = 4.91142(7) A, Z = 4), the second member of the Al(2)[O(3)PC(n)H(2n)PO(3)](H(2)O)(2)F(2).H(2)O series. The structure consists of corrugated chains of corner-sharing AlO(4)F(2) octahedra in which alternating AlO(4)F(2) octahedra contain two fluorine atoms in a trans or a cis configuration. The diphosphonate groups link the chains together through Al-O-P-O-Al bridges and through the propylene groups to form a three-dimensional framework structure containing a one-dimensional channel system. The linkage of the corrugated inorganic Al-O-P layers within the structure results in the formation of two types of channel that differ in size, shape and composition. The smaller channel is unoccupied; the larger channel is more elongated and contains two extra-framework water molecules per unit cell. A computational investigation into the driving force that controls the stacking arrangement of the Al-O-P inorganic layers within this series of compounds reveals that the stacking is found to be controlled by thermodynamic factors, arising chiefly from the conformation of the organic linker molecule used to connect the inorganic sheets. It is found that the registration of the inorganic layers can be engineered by selecting an appropriate, simple organic spacer or linker alkyl chain, where an even number of carbon atoms in the alkyl chain directs formation of aligned, stacked, inorganic sheets (AAAAAA), and an odd number directs formation of unaligned, stacked sheets (ABABAB) and the formation of one or two channel types in the resultant structure, respectively. This combination of alkyl-chain linkers in conjunction with corrugated inorganic layers is an effective tool to rationally design the pore system of hybrid framework materials.

Journal Article↗
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↗
PubMed1997

Ela 1.0--a framework for life-cycle impact assessment developed by the Fraunhofer-Gesellschaft. Part A: The conceptual framework.

The Fraunhofer-Gesellschaft has sponsored the development of a conceptual and flexible, computer aided tool to perform the impact assessment within LCA (life cycle assessment) for technical products and processes. The developed general framework "Ela 1.0" (environmental loads analysis) consists of four elements: the selection of appropriate impact categories, the categorization of emissions and wastes leaving the systems as well as of resource and energy consumption, the characterization and an analysis of the results of the impact assessment. The latter compares the product-based emissions with the total of emissions of a region such as Germany, the EU or OECD countries. The framework Ela 1.0 considers the environmental categories: global warming, ozone depletion, resource and energy consumption, wastes, eutrophication (including COD and BOD as measured parameters), acidification, ecotoxicity, ozone formation and human toxicity. The latter categories are handled by listing of precursors for ozone formation, and by listing of emissions scored according to their human hazard potential. The options, possibilities and limitations of the conceptual framework are presented in part A of a series of publications.

Acid Rain↗
PubMed2003

MIL-50, an open-framework GaPO with a periodic pattern of small water ponds and dry rubidium atoms: a combined XRD, NMR, and computational study.

A new fluorinated gallium phosphate, MIL-50, has been synthesized under mild hydrothermal conditions using 1,6-diaminohexane. The chemical formula of MIL-50 is Rb(2)Ga(9)(PO(4))(8)(HPO(4))(OH)F(6).2N(2)C(6)H(18).7H(2)O. The structure is a network of hexameric units of Ga(3)(PO(4))(3)F(2) and Ga(3)(PO(4))(2)(HPO(4))F(3) via corner sharing. It creates a three-dimensional open-framework delimiting 6- and 18-ring channels running along the c axis. The diprotonated 1,6-diaminohexane and water molecules are trapped within the 18-ring pores, whereas the rubidium cations reside in the 6-ring ones. A double quantum (31)P NMR experiment and partial charge calculations indicate that water molecules are present under the form of periodic small clusters, lowering the multiplicity of one phosphorus site, P3. Though water hops within the clusters, the motion leaves the water pattern periodic. Rubidium is so tightly embedded into the framework that water moving in the large 18-ring channels does not reach it, leaving it therefore dry. The crystal framework may be ascribed to the orthorhombic space group Cmc2(1) (n degrees 36), a = 32.1510(2), b = 17.2290(3), c = 10.2120(1) A. The periodic water pattern has a different symmetry than that of the framework. A method has been devised to superpose the two sublattices that coexist in the same unit cell in order to have full occupancy of each site and to perform Madelung summations. This original method is of general interest for most zeolitic materials exhibiting a different symmetry for the framework and the template sublattices.

Journal Article↗
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↗
PubMed1993

Some computational models at the cellular level.

A number of viewpoints on how a cell can be modelled are discussed in this paper in light of the ability it has to process information. The paper begins with a very brief summary of four general types of computation: sequential, parallel, distributed, and emergent. These form the general framework from which a number of comparisons are made. Several metaphors are introduced to enable reflections to be made about cellular computational properties. The most important metaphor, namely the cell as a machine, is discussed, and then a number of other ideas are introduced that complement much current thinking in this area. The idea of networks or circuits in the cell is then developed, as this provides a means of describing the mechanisms within a machine. Following on from this, three further metaphors are applied in order to overcome certain limitations in current machine thinking, cell-as-society, cell-as-text, and cell-as-field.

Animals↗
PubMed2001

Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role.

The theory of coupled phase oscillators provides a framework to understand the emergent properties of networks of neuronal oscillators. When the architecture of the network is dominated by short-range connections, the pattern of electrical output is predicted to correspond to traveling plane and rotating waves, in addition to synchronized output. We argue that this theory provides the foundation for understanding the traveling electrical waves that are observed across olfactory, visual, and visuomotor areas of cortex in a variety of species. The waves are typically present during periods outside of stimulation, while synchronous activity typically dominates in the presence of a strong stimulus. We suggest that the continuum of phase shifts during epochs with traveling waves provides a means to scan the incoming sensory stream for novel features. Experiments to test our theoretical approach are presented.

Animals↗
PubMed2004

On the sample complexity of learning for networks of spiking neurons with nonlinear synaptic interactions.

We study networks of spiking neurons that use the timing of pulses to encode information. Nonlinear interactions model the spatial groupings of synapses on the neural dendrites and describe the computations performed at local branches. Within a theoretical framework of learning we analyze the question of how many training examples these networks must receive to be able to generalize well. Bounds for this sample complexity of learning can be obtained in terms of a combinatorial parameter known as the pseudodimension. This dimension characterizes the computational richness of a neural network and is given in terms of the number of network parameters. Two types of feedforward architectures are considered: constant-depth networks and networks of unconstrained depth. We derive asymptotically tight bounds for each of these network types. Constant depth networks are shown to have an almost linear pseudodimension, whereas the pseudodimension of general networks is quadratic. Networks of spiking neurons that use temporal coding are becoming increasingly more important in practical tasks such as computer vision, speech recognition, and motor control. The question of how well these networks generalize from a given set of training examples is a central issue for their successful application as adaptive systems. The results show that, although coding and computation in these networks is quite different and in many cases more powerful, their generalization capabilities are at least as good as those of traditional neural network models.

Action Potentials↗
PubMedDate not supplied

An algorithmic synthesis of the deterministic and stochastic paradigms via computer intensive methods.

In this paper, an approach to synthesizing the deterministic and stochastic paradigms, via computer intensive methods, is presented within the framework of a stochastic model of a HIV/AIDS epidemic in a population of homosexuals. Because of dependence among members of a population, the problem of determining threshold conditions was approached by systematically embedding a system of differential equations in a stochastic process and determining if the Jacobian matrix of this system is stable or not stable, when evaluated at a disease free equilibrium. It has been shown in numerous Monte Carlo simulation experiments that if this matrix is not stable, then an epidemic will develop in a population with positive probability, following the introduction of infectives into a population of susceptibles. This technique was used to search for points in the parameter space such that an epidemic would develop in a population of susceptibles, following the entrance of one or more infectious recruits during any time interval with small probability. Such recurrent rare events are of interest in the studying the emergence of new diseases, involving the transmission of a virus from a species that has evolved resistance to it to another species that lacks resistance.

Acquired Immunodeficiency Syndrome↗
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↗
PubMed2005

Lyapunov exponents from unstable periodic orbits.

We propose a method that allows us to analytically compute the largest Lyapunov exponent of a Hamiltonian chaotic system from the knowledge of a few unstable periodic orbits (UPOs). In the framework of a recently developed theory for Hamiltonian chaos, by computing the time averages of the metric tensor curvature and of its fluctuations along analytically known UPOs, we have re-derived the analytic value of the largest Lyapunov exponent for the Fermi-Pasta-Ulam-beta (FPU-beta) model. The agreement between our results and the Lyapunov exponents obtained by means of standard numerical simulations confirms the point of view which attributes to UPOs the special role of efficient probes of general dynamical properties, among them chaotic instability.

Journal Article↗
PubMedDate not supplied

Pilot study of a point-of-use decision support tool for cancer clinical trials eligibility.

Many adults with cancer are not enrolled in clinical trials because caregivers do not have the time to match the patient's clinical findings with varying eligibility criteria associated with multiple trials for which the patient might be eligible. The authors developed a point-of-use portable decision support tool (DS-TRIEL) to automate this matching process. The support tool consists of a hand-held computer with a programmable relational database. A two-level hierarchic decision framework was used for the identification of eligible subjects for two open breast cancer clinical trials. The hand-held computer also provides protocol consent forms and schemas to further help the busy oncologist. This decision support tool and the decision framework on which it is based could be used for multiple trials and different cancer sites.

Adult↗
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↗
PubMed2005

Modelling novice clinical reasoning for a computerized decision support system.

AIM: The aim of this paper is to introduce the theoretical framework that directs the project. BACKGROUND: The Novice Computer Decision Support (N-CODES) Project is developing a point-of-care system to assist novice acute care nurses while making clinical judgements. Unlike prior approaches, N-CODES is guided by a theoretical understanding of nurses' decision-making processes, including the manner by which novices develop this skill. FRAMEWORK: Assumptions within information processing theory guided the clinical decision-making framework. The framework is composed of a clinical decision-making model and a second embedded model depicting the clinical reasoning development of novice nurses. MODELS: The model is developed within a pluralistic perspective synthesizing theoretical and empirical knowledge on clinical decision-making and the development of novice reasoning skills. A visual representation of experienced nurse decision-making is presented. A central element is the nurse's use of pre-encounter data and working knowledge. A second model integrates empirical data on the developing clinical reasoning of the novice. This knowledge is loosely scattered through 25 years of literature. The intersection of these models provides a novel perspective on the way novices begin to identify working knowledge patterns and develop a sense of saliency. CONCLUSIONS: Previous attempts to build comprehensive clinical decision support systems have disregarded important theoretical considerations hindering the success of these projects. Grounding a Decision Support System in a theoretical model of novice nurse decision-making will strengthen the utility and acceptance of the Decision Support System. Additionally, a conceptualization of novice nurse development is an asset to nurse educators, managers and scientists interested in improving clinical decision-making.

Clinical Competence↗