SEARCH · PubMed Health

Results for “generative artificial intelligence”

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

PubMed2001

Real time distributed processing of multiple associated pulse pattern sequences.

A Real Time Distributed Associative Memory Artificial Neural Network (RTANN) is described. This network associates groups of pulse pattern sequences. The subsequent reoccurrence of some sequences will cause the remainder to be regenerated. Training is carried out in real time simply by feeding pattern sequences directly into the network. The connections between units incorporate a wide range of transmission delays. During training the network enhances connection weights on units where coincidences occur between input and delayed pulses. Pattern regeneration utilises the reoccurrence of coincidences between delayed pulses. The simulation of an RTANN is presented. Continuous dual pattern sequences from notional sensors monitoring the shape and colour of an object were associated directly with a third dual pattern sequence having the form 'These objects look colour'. After training the network was able to correctly generate sentences describing combinations of object and colour not encountered during training.

Artificial Intelligence↗

PubMed2005

Genetic-based EM algorithm for learning Gaussian mixture models.

We propose a genetic-based expectation-maximization (GA-EM) algorithm for learning Gaussian mixture models from multivariate data. This algorithm is capable of selecting the number of components of the model using the minimum description length (MDL) criterion. Our approach benefits from the properties of Genetic algorithms (GA) and the EM algorithm by combination of both into a single procedure. The population-based stochastic search of the GA explores the search space more thoroughly than the EM method. Therefore, our algorithm enables escaping from local optimal solutions since the algorithm becomes less sensitive to its initialization. The GA-EM algorithm is elitist which maintains the monotonic convergence property of the EM algorithm. The experiments on simulated and real data show that the GA-EM outperforms the EM method since: 1) We have obtained a better MDL score while using exactly the same termination condition for both algorithms. 2) Our approach identifies the number of components which were used to generate the underlying data more often than the EM algorithm.

Algorithms↗

PubMedDate not supplied

New robotics: design principles for intelligent systems.

New robotics is an approach to robotics that, in contrast to traditional robotics, employs ideas and principles from biology. While in the traditional approach there are generally accepted methods (e. g., from control theory), designing agents in the new robotics approach is still largely considered an art. In recent years, we have been developing a set of heuristics, or design principles, that on the one hand capture theoretical insights about intelligent (adaptive) behavior, and on the other provide guidance in actually designing and building systems. In this article we provide an overview of all the principles but focus on the principles of ecological balance, which concerns the relation between environment, morphology, materials, and control, and sensory-motor coordination, which concerns self-generated sensory stimulation as the agent interacts with the environment and which is a key to the development of high-level intelligence. As we argue, artificial evolution together with morphogenesis is not only "nice to have" but is in fact a necessary tool for designing embodied agents.

Artificial Intelligence↗

PubMed2002

Simultaneous classification and feature clustering using discriminant vector quantization with applications to microarray data analysis.

In many applications of supervised learning, automatic feature clustering is often desirable for a better understanding of the interaction among the various features as well as the interplay between the features and the class labels. In addition, for high dimensional data sets, feature clustering has the potential for improvement in classification accuracy and reduction in computational complexity. In this paper, a method is developed for simultaneous classification and feature clustering by extending discriminant vector quantization (DVQ), a prototype classification method derived from the principle of minimum description length using source coding techniques. The method incorporates feature clustering with classification performed by fusing features in the same clusters. To illustrate its effectiveness, the method has been applied to microarray gene expression data for human lymphoma classification. It is demonstrated that incorporating feature clustering improves classification accuracy, and the clusters generated match well with biological meaningful gene expression signature groups.

Algorithms↗

PubMed1999

Intelligent systems in medical diagnosis.

From an intelligent system for a computer supported medical diagnosis it is expected to achieve high accuracy and ability to draw conclusions from a small data sets. Medical practice could takes many years to generate a large database. A new mathematical method that is able to learn on a small data set is presented.

Algorithms↗

PubMed2026

AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.

Colorectal cancer is a leading cause of cancer death, yet its molecular heterogeneity remains poorly translated into individualized treatment. We present a reproducible artificial intelligence (AI) framework that integrates multi-omics benchmarking, sample-level drug prioritization, E3 ubiquitin ligase selection, and shape-anchored Proteolysis Targeting Chimera (PROTAC) design for KRASG12D in colon adenocarcinoma (COAD). A controlled synthetic benchmark comprising 425 tumor and 41 simulated normal profiles, parameterized to match The Cancer Genome Atlas (TCGA) distributions, was used for pipeline verification. Among sixteen methods, the Balanced Latent Integration with Stability Selection (BLISS) model achieved the highest silhouette width (0.86) and competitive agreement (Adjusted Rand Index, ARI, 0.90). The pipeline was validated on real data: a TCGA COAD cohort (186 tumors) with independent Consensus Molecular Subtype (CMS) labels and a CPTAC cohort (104 tumors). Integration modestly recovered CMS (ARI 0.28), and stage, not molecular cluster, drove survival (log-rank p = 0.005 versus 0.81). Sample-level prioritization differed from cluster-level ranking in 82.6% of profiles, below chance (p < 0.0001), without indicating efficacy. Candidate NOVEL00489 showed a good MM-GBSA estimate, matching the reference ASP3082. Compounds are computational candidates requiring experimental validation. This establishes a transparent benchmark for in silico degrader generation in precision oncology.

Humans↗

PubMed2025

Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine: Bringing next-generation precision oncology to patients.

The human genome project ushered in a genomic medicine era that was largely unimaginable three decades ago. Discoveries of druggable cancer drivers enabled biomarker-driven gene- and immune-targeted therapy and transformed cancer treatment. Minimizing treatment not expected to benefit, and toxicity-including financial and time-are important goals of modern oncology. The Worldwide Innovative Network (WIN) Consortium in Personalized Cancer Medicine founded by Drs. John Mendelsohn and Thomas Tursz provided a vision for innovation, collaboration and global impact in precision oncology. Through pursuit of transcriptomic signatures, artificial intelligence (AI) algorithms, global precision cancer medicine clinical trials and input from an international Molecular Tumor Board (MTB), WIN has led the way in demonstrating patient benefit from precision-therapeutics through N-of-1 molecularly-driven studies. WIN Next-Generation Precision Oncology (WINGPO) trials are being developed in the neoadjuvant, adjuvant or metastatic settings, incorporate real-world data, digital pathology, and advanced algorithms to guide MTB prioritization of therapy combinations for a diverse global population. WIN has pursued combinations that target multiple drivers/hallmarks of cancer in individual patients. WIN continues to be impactful through collaboration with industry, government, sponsors, funders, academic and community centers, patient advocates, and other stakeholders to tackle challenges including drug access, costs, regulatory barriers, and patient support. WIN's collaborative next generation of precision oncology trials will guide treatment selection for patients with advanced cancers through MTB and AI algorithms based on serial liquid and tissue biopsies and exploratory omics including transcriptomics, proteomics, metabolomics and functional precision medicine. Our vision is to accelerate the future of precision oncology care.

Humans↗

PubMed1999

Galen: a third generation terminology tool to support a multipurpose national coding system for surgical procedures.

GALEN has developed a new generation of terminology tools based on a language independent concept reference model using a compositional formalism allowing computer processing and multiple reuses. During the 4th framework program project Galen-In-Use we applied the modelling and the tools to the development of a new multipurpose coding system for surgical procedures (CCAM) in France. On one hand we contributed to a language independent knowledge repository for multicultural Europe. On the other hand we support the traditional process for creating a new coding system in medicine which is very much labour consuming by artificial intelligence tools using a medically oriented recursive ontology and natural language processing. We used an integrated software named CLAW to process French professional medical language rubrics produced by the national colleges of surgeons into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation on one hand we generate controlled French natural language to support the finalization of the linguistic labels in relation with the meanings of the conceptual system structure. On the other hand the classification manager of third generation proves to be very powerful to retrieve the initial professional rubrics with different categories of concepts within a semantic network.

Abstracting and Indexing↗

PubMed2003

Formalizing an electronic institution for the distribution of human tissues.

The use of multi-agent systems (MAS) in health-care domains is increasing. Such agent-mediated medical systems can manage complex tasks and have the potential to adapt gracefully to unexpected events. However, in these kinds of systems the issues of privacy, security and trust are particularly sensitive in relation to matters such as agents' access to patient records, what is acceptable behaviour for an agent in a particular role and the development of trust both between (heterogeneous) agents and between users and agents. To address these issues we propose a formal normative framework, deriving from and developing the notion of an electronic institution. Such institutions provide a framework to define and police norms that guide, control and regulate the behaviour of the heterogeneous agents that participate in the institution. These norms define the acceptable actions that each agent may perform depending on the role or roles it is playing, and clearly specifies the data it may access and/or modify in playing those roles. In this paper, we present the formalization of Carrel, a virtual organization for the procurement of organs and tissues for transplantation purposes, as an electronic institution using the ISLANDER institution specification language as formalizing languages. We demonstrate aspects of the formalization of such an institution, example fragments in the language used for the textual specification, and how such formalization can be used as a blueprint in the implementation of the final agent architecture, through techniques such as skeleton generation.

Artificial Intelligence↗

PubMed2004

A new approach to prediction of short-range conformational propensities in proteins.

MOTIVATION: Knowledge-based potentials are valuable tools for protein structure modeling and evaluation of the quality of the structure prediction obtained by a variety of methods. Potentials of such type could be significantly enhanced by a proper exploitation of the evolutionary information encoded in related protein sequences. The new potentials could be valuable components of threading algorithms, ab-initio protein structure prediction, comparative modeling and structure modeling based on fragmentary experimental data. RESULTS: A new potential for scoring local protein geometry is designed and evaluated. The approach is based on the similarity of short protein fragments measured by an alignment of their sequence profiles. Sequence specificity of the resulting energy function has been compared with the specificity of simpler potentials using gapless threading and the ability to predict specific geometry of protein fragments. Significant improvement in threading sensitivity and in the ability to generate sequence-specific protein-like conformations has been achieved.

Algorithms↗

PubMed2000

Nonlinear dimensionality reduction by locally linear embedding.

Many areas of science depend on exploratory data analysis and visualization. The need to analyze large amounts of multivariate data raises the fundamental problem of dimensionality reduction: how to discover compact representations of high-dimensional data. Here, we introduce locally linear embedding (LLE), an unsupervised learning algorithm that computes low-dimensional, neighborhood-preserving embeddings of high-dimensional inputs. Unlike clustering methods for local dimensionality reduction, LLE maps its inputs into a single global coordinate system of lower dimensionality, and its optimizations do not involve local minima. By exploiting the local symmetries of linear reconstructions, LLE is able to learn the global structure of nonlinear manifolds, such as those generated by images of faces or documents of text.

Algorithms↗

PubMed2006

Fuzzy logic and maximum a posteriori-based image restoration for confocal microscopy.

We propose a maximum a posteriori image restoration approach to 3D confocal microscopy. The image field is suitably modeled as a Markov random field, resulting in a Gibbs distributed image. A fuzzy-logic-based potential is employed in the Gibbs prior. Unlike other potentials, the fuzzy potential distinguishes intensity variation due to genuine edges and noise. The proposed approach has generated artifact-free restored confocal microscopy images.

Algorithms↗

PubMed1992

Possibilities of software phantoms for quality control of KBS in nuclear medicine.

The assessment of the results of a "knowledge-based system" (KBS) for quality control is a basic requirement for clinical application: Large numbers of test studies are necessary in order to cover as widely as possible the spectrum of cases to be analyzed by the KBS. The use of original patient data as test data is one possibility, but real data are provided unevenly. This is due to the set of characteristics which are relevant to the analysis. Data are available in a limited quantity only. This implies a remaining set of unvalidated cases which are not represented in the data pool. The software phantom is an approach towards systematically guided validation. It permits the generation of test data adjusted to the demands of the validation.

Artificial Intelligence↗

PubMed1991

Automatic generation of plans for biomedical image interpretation.

This paper presents a new object-centered, goal-driven planning approach to biomedical image interpretation. We describe here a prototype system which takes advantage of spatial and detectability constraints from an expert-derived model of expected anatomical structures to automatically generate plans for the interpretation of multimodality images.

Artificial Intelligence↗

PubMed2000

The genetic algorithm applied to haplotype data at the LDL receptor locus.

Conventional statistical methods based upon single restriction fragment length polymorphisms often prove inadequate in studies of genetic variation. Cladistic analysis has been suggested as an alternative, but requires basic assumptions that usually cannot be met. We wanted to test whether it could be a workable approach to apply the genetic algorithm, an artificial intelligence method, to haplotype data. The genetic algorithm creates in-computer artificial 'individuals', all having 'genes' coding for solutions to a problem. The individuals are allowed to compete and 'mate', individuals with genes coding for better solutions mating more often. Genes coding for good solutions survive through generations of the genetic algorithm. At the end of the run, the best solutions can be extracted. We applied the genetic algorithm to data consisting of cholesterol values and haplotypes made up of seven restriction sites at the LDL receptor locus. The persons included were 114 FH (familial hypercholesterolemia) patients and 61 normals. The genetic algorithm found the restriction sites 1 (Sph1 in intron 6), 2 (StuI in exon 8), and 7 (ApaLI site in the 3' flanking region) were associated with high cholesterol levels. As a validity check we used runs of the genetic algorithm applied to 'artificial patients', i.e. artificially generated haplotypes linked to artificially generated cholesterol values. This demonstrated the genetic algorithm consistently found the appropriate haplotype. We conclude that the genetic algorithm may be a useful tool for studying genetic variation.

Algorithms↗

PubMed2002

Intelligent agent software for medicine.

An important trend for the future of health technology will be the increasing use of intelligent agent software for medical applications. As the complexity of situations faced by both patients and health care providers grows, conventional interfaces that rely on users to manually transfer data and manually perform each problem-solving step, won't be able to keep up. This article describes how software agents that incorporate learning, personalization, proactivity, context-sensitivity and collaboration will lead to a new generation of medical applications that will streamline user interfaces and enable more sophisticated communication and problem-solving.

Artificial Intelligence↗

PubMed2005

Identification of signatures in biomedical spectra using domain knowledge.

OBJECTIVE: Demonstrate that incorporating domain knowledge into feature selection methods helps identify interpretable features with predictive capability comparable to a state-of-the-art classifier. METHODS: Two feature selection methods, one using a genetic algorithm (GA) the other a L(1)-norm support vector machine (SVM), were investigated on three real-world biomedical magnetic resonance (MR) spectral datasets of increasing difficulty. Consensus sets of the feature sets obtained by the two methods were also assessed. RESULTS AND CONCLUSIONS: Features identified independently by the two methods and by their consensus, determine class-discriminatory groups or individual features, whose predictive power compares favorably with that of a state-of-the-art classifier. Furthermore, the identified feature signatures form stable groupings at definite spectral positions, hence are readily interpretable. This is a useful and important practical result for generating hypothesis for the domain expert.

Algorithms↗

PubMed2002

HepatoConsult: a knowledge-based second opinion and documentation system.

HepatoConsult is a publicly available knowledge-based second opinion and documentation system aiding in the diagnosis of liver diseases. The positive results of a prospective diagnostic evaluation study encouraged its use in clinical routine, although the available hardware infrastructure was not optimal. The comments of the physicians who used the system confirmed the results of the study and showed that the time for data entering is acceptable and the implicit standardization of terminology and documentation is welcome. Suggestions for improvement included the interface to enter data more easily, the scope to be usable for more patients and the additional capability to generate medical reports from the data.

Artificial Intelligence↗