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Results for “generative artificial intelligence”

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At least 217 records · Page 12Linked to original sources

PubMed2005

A method for generating precise temporal patterns of retinal spiking using prosthetic stimulation.

The goal of retinal prosthetic devices is to generate meaningful visual information in patients that have lost outer retinal function. To accomplish this, these devices should generate patterns of ganglion cell activity that closely resemble the spatial and temporal components of those patterns that are normally elicited by light. Here, we developed a stimulus paradigm that generates precise temporal patterns of activity in retinal ganglion cells, including those patterns normally generated by light. Electrical stimulus pulses (> or =1-ms duration) elicited activity in neurons distal to the ganglion cells; this resulted in ganglion cell spiking that could last as long as 100 ms. However, short pulses, <0.15 ms, elicited only a single spike within 0.7 ms of the leading edge of the pulse. Trains of these short pulses elicited one spike per pulse at frequencies < or =250 Hz. Patterns of short electrical pulses (derived from normal light elicited spike patterns) were delivered to ganglion cells and generated spike patterns that replicated the normal light patterns. Finally, we found that one spike per pulse was elicited over almost a 2.5:1 range of stimulus amplitudes. Thus a common stimulus amplitude could accommodate a 2.5:1 range of activation thresholds, e.g., caused by differences arising from cell biophysical properties or from variations in electrode-to-cell distance arising when a multielectrode array is placed on the retina. This stimulus paradigm can generate the temporal resolution required for a prosthetic device.

Action Potentials↗

PubMed2003

Evolutionary Local Search of Fuzzy Rules through a novel Neuro-Fuzzy encoding method.

This paper proposes a new approach for constructing fuzzy knowledge bases using evolutionary methods. We have designed a genetic algorithm that automatically builds neuro-fuzzy architectures based on a new indirect encoding method. The neuro-fuzzy architecture represents the fuzzy knowledge base that solves a given problem; the search for this architecture takes advantage of a local search procedure that improves the chromosomes at each generation. Experiments conducted both on artificially generated and real world problems confirm the effectiveness of the proposed approach.

Algorithms↗

PubMed2005

Workflows in bioinformatics: meta-analysis and prototype implementation of a workflow generator.

BACKGROUND: Computational methods for problem solving need to interleave information access and algorithm execution in a problem-specific workflow. The structures of these workflows are defined by a scaffold of syntactic, semantic and algebraic objects capable of representing them. Despite the proliferation of GUIs (Graphic User Interfaces) in bioinformatics, only some of them provide workflow capabilities; surprisingly, no meta-analysis of workflow operators and components in bioinformatics has been reported. RESULTS: We present a set of syntactic components and algebraic operators capable of representing analytical workflows in bioinformatics. Iteration, recursion, the use of conditional statements, and management of suspend/resume tasks have traditionally been implemented on an ad hoc basis and hard-coded; by having these operators properly defined it is possible to use and parameterize them as generic re-usable components. To illustrate how these operations can be orchestrated, we present GPIPE, a prototype graphic pipeline generator for PISE that allows the definition of a pipeline, parameterization of its component methods, and storage of metadata in XML formats. This implementation goes beyond the macro capacities currently in PISE. As the entire analysis protocol is defined in XML, a complete bioinformatic experiment (linked sets of methods, parameters and results) can be reproduced or shared among users. AVAILABILITY: http://if-web1.imb.uq.edu.au/Pise/5.a/gpipe.html (interactive), ftp://ftp.pasteur.fr/pub/GenSoft/unix/misc/Pise/ (download). CONCLUSION: From our meta-analysis we have identified syntactic structures and algebraic operators common to many workflows in bioinformatics. The workflow components and algebraic operators can be assimilated into re-usable software components. GPIPE, a prototype implementation of this framework, provides a GUI builder to facilitate the generation of workflows and integration of heterogeneous analytical tools.

Algorithms↗

PubMed2006

Clustering of gene expression data: performance and similarity analysis.

BACKGROUND: DNA Microarray technology is an innovative methodology in experimental molecular biology, which has produced huge amounts of valuable data in the profile of gene expression. Many clustering algorithms have been proposed to analyze gene expression data, but little guidance is available to help choose among them. The evaluation of feasible and applicable clustering algorithms is becoming an important issue in today's bioinformatics research. RESULTS: In this paper we first experimentally study three major clustering algorithms: Hierarchical Clustering (HC), Self-Organizing Map (SOM), and Self Organizing Tree Algorithm (SOTA) using Yeast Saccharomyces cerevisiae gene expression data, and compare their performance. We then introduce Cluster Diff, a new data mining tool, to conduct the similarity analysis of clusters generated by different algorithms. The performance study shows that SOTA is more efficient than SOM while HC is the least efficient. The results of similarity analysis show that when given a target cluster, the Cluster Diff can efficiently determine the closest match from a set of clusters. Therefore, it is an effective approach for evaluating different clustering algorithms. CONCLUSION: HC methods allow a visual, convenient representation of genes. However, they are neither robust nor efficient. The SOM is more robust against noise. A disadvantage of SOM is that the number of clusters has to be fixed beforehand. The SOTA combines the advantages of both hierarchical and SOM clustering. It allows a visual representation of the clusters and their structure and is not sensitive to noises. The SOTA is also more flexible than the other two clustering methods. By using our data mining tool, Cluster Diff, it is possible to analyze the similarity of clusters generated by different algorithms and thereby enable comparisons of different clustering methods.

Algorithms↗

PubMedDate not supplied

Data representation for subsequent image interpretation.

Radiological scans acquired using either the X-ray CT or the NMR imaging techniques provide a wealth of information about tissue behaviour under that imaging modality and contrast agent. To reason about the image in an interpretation stage the scans have to be converted from a pixel by pixel representation to a symbolic form. The technique used by us to generate such a description is region-based segmentation. Each region refers to a pixel or group of pixels having a common attribute. This work has provided a quantitative measure for the partial evaluation of the segmentation which can be applied independent of attribute or combination of attributes. From our initial studies of the behaviour of CT scans a precept for segmentation was developed. The segmentation employs a one-to-one map as an adaptive mechanism. The segmentation criterion at each point in the image therefore depends on the value at the corresponding point in the map. Any process can be used to generate this map, and so easily utilizes new operators as they are developed.

Artificial Intelligence↗

PubMed1995

Informatics for care protocols and guidelines: towards a European knowledge model.

The DILEMMA Generic Protocol Model (DGPM) is a trans-national ontology of clinical protocols designed using a blend of logic engineering and business modelling techniques, and developed as part of the AIM programme's DILEMMA project. It allows the declarative representation of clinical activities and the knowledge associated with them. It is being used to represent protocols of all sorts-including standards and guidelines-for use in acute, primary, home and shared care. Central to the model are the states that protocol-derived actions can assume, and the statements that must be true before transitions between those states can be proposed. Proposed transitions may then be scrutinised by clinicians, patients and carers, and approved or rejected. This approach enables the model to handle anticipated exception situations, as well as more normal protocol selection and application. Links to multi-media material are being investigated, to enable users to examine the evidence upon which protocols are based, and to provide decision support where deterministic reasoning is not readily available. The model is being discussed with other AIM projects, with a view to developing a European consensus, and generating a version of the model for submission to CEN TC251 as a European pre-normative standard.

Artificial Intelligence↗

PubMed2001

Building an explanation function for a hypertension decision-support system.

ATHENA DSS is a decision-support system that provides recommendations for managing hypertension in primary care. ATHENA DSS is built on a component-based architecture called EON. User acceptance of a system like this one depends partly on how well the system explains its reasoning and justifies its conclusions. We addressed this issue by adapting WOZ, a declarative explanation framework, to build an explanation function for ATHENA DSS. ATHENA DSS is built based on a component-based architecture called EON. The explanation function obtains its information by tapping into EON's components, as well as into other relevant sources such as the guideline document and medical literature. It uses an argument model to identify the pieces of information that constitute an explanation, and employs a set of visual clients to display that explanation. By incorporating varied information sources, by mirroring naturally occurring medical arguments and by utilizing graphic visualizations, ATHENA DSS's explanation function generates rich, evidence-based explanations.

Artificial Intelligence↗

PubMed2026

PubMind: literature-based genetic variant extraction and functional annotation using large language models.

Biomedical literature contains extensive functional knowledge on genetic variants, but much remains inaccessible in unstructured text. Existing resources such as ClinVar and HGMD remain limited by coverage, submission bias, update frequency, and sparse annotation. We develop PubMind, an artificial intelligence (AI) framework that uses large language models (LLMs) to triage and extract variant-function-disease associations and supporting evidence from biomedical text. PubMind captures single-nucleotide, copy-number, structural, and gene-fusion variants, and normalizes records to genomic and transcriptomic coordinates. Benchmarking shows >90% accuracy for variant recognition and 99% precision for disease extraction. Applied to >41 million PubMed abstracts and >5 million full-text articles, PubMind generates PubMind-DB, a database of ~1.3 million unique variants with contextual annotations, accessible via web interface and API. Only ~10% of PubMind variants overlap with ClinVar, and >80% of them show concordant pathogenicity labels. PubMind transforms unstructured biomedical text into structured genomic knowledge, advancing variant interpretation for precision medicine.

Large Language Models↗

PubMed2005

New approaches to catheter navigation for interventional radiology simulation.

For over 20 years, interventional methods have improved the outcomes of patients with cardiovascular disease. However, these procedures require an intricate combination of visual and tactile feedback and extensive training periods. In this paper, we describe a series of novel approaches that have lead to the development of a high-fidelity simulation system for interventional neuroradiology. In particular we focus on a new approach for real-time deformation of devices such as catheters and guidewires during navigation inside complex vascular networks. This approach combines a real-time incremental Finite Element Model, an optimization strategy based on substructure decomposition, and a new method for handling collision response in situations where the number of contacts points is very large. We also briefly describe other aspects of the simulation system, from patient-specific segmentation to the simulation of contrast agent propagation and fast volume rendering techniques for generating synthetic X-ray images in real-time.

Algorithms↗

PubMed2005

TRAP: automated classification, quantification and annotation of tandemly repeated sequences.

TRAP, the Tandem Repeats Analysis Program, is a Perl program that provides a unified set of analyses for the selection, classification, quantification and automated annotation of tandemly repeated sequences. TRAP uses the results of the Tandem Repeats Finder program to perform a global analysis of the satellite content of DNA sequences, permitting researchers to easily assess the tandem repeat content for both individual sequences and whole genomes. The results can be generated in convenient formats such as HTML and comma-separated values. TRAP can also be used to automatically generate annotation data in the format of feature table and GFF files.

Algorithms↗

PubMed2003

Modelling dependencies between relations to ensure consistency of a cerebral cortex anatomy knowledge base.

A symbolic model of anatomy that could be used in various contexts is a key feature. However, explicitly representing anatomy requires managing many specialisation, part-whole and topological relationships. Furthermore, we notice dependencies between some of them. These dependencies have to be taken into account in order to insure both intrinsic and incremental consistency of the model. Our approach is composed of three steps. First, we define the relationships between anatomical concepts by relationships between the space region they take up or the portion of matter they are made of. Second, we use these definitions and properties of spatial and matter relationships to infer dependencies between anatomical relationships. Eventually, we apply these dependencies to the set of independent anatomical relationships to automatically generate all the dependent relationships. This method was used to maintain an anatomical model of the frontal, temporal, parietal and occipital regions of the cerebral cortex. For 113 concepts, 221 of the 370 relationships could be automatically generated. The more the number of concepts increase, the more pertinent the method appears.

Artificial Intelligence↗

PubMed2006

A sequential injection electronic tongue employing the transient response from potentiometric sensors for anion multidetermination.

Intelligent and automatic systems based on arrays of non-specific-response chemical sensors were recently developed in our laboratory. For multidetermination applications, the normal choice is an array of potentiometric sensors to generate the signal, and an artificial neural network (ANN) correctly trained to obtain the calibration model. As a great amount of information is required for the proper modelling, we proposed its automated generation by using the sequential injection analysis (SIA) technique. First signals used were steady-state: the equilibrium signal after a step-change in concentration. We have now adapted our procedures to record the transient response corresponding to a sample step. The novelty in this approach is therefore the use of the dynamic components of the signal in order to better discriminate or differentiate a sample. In the developed electronic tongue systems, detection is carried out by using a sensor array formed by five potentiometric sensors based on PVC membranes. For the developed application we employed two different chloride-selective sensors, two nitrate-selective sensors and one generic response sensor. As the amount of raw data (fivefold recordings corresponding to the five sensors) is excessive for an ANN, some feature extraction step prior to the modelling was needed. In order to attain substantial data reduction and noise filtering, the data obtained were fitted with orthonormal Legendre polynomials. In this case, a third-degree Legendre polynomial was shown to be sufficient to fit the data. The coefficients of these polynomials were the input information fed into the ANN used to model the concentrations of the determined species (Cl-, NO3- and HCO3-). Best results were obtained by using a backpropagation neural network trained with the Bayesian regularisation algorithm; the net had a single hidden layer containing three neurons with the tansig transfer function. The results obtained from the time-dependent response were compared with those obtained from steady-state conditions, showing the former superior performance. Finally, the method was applied for determining anions in synthetic samples and real water samples, where a satisfactory comparison was also achieved.

Algorithms↗

PubMed2006

Activity classification using realistic data from wearable sensors.

Automatic classification of everyday activities can be used for promotion of health-enhancing physical activities and a healthier lifestyle. In this paper, methods used for classification of everyday activities like walking, running, and cycling are described. The aim of the study was to find out how to recognize activities, which sensors are useful and what kind of signal processing and classification is required. A large and realistic data library of sensor data was collected. Sixteen test persons took part in the data collection, resulting in approximately 31 h of annotated, 35-channel data recorded in an everyday environment. The test persons carried a set of wearable sensors while performing several activities during the 2-h measurement session. Classification results of three classifiers are shown: custom decision tree, automatically generated decision tree, and artificial neural network. The classification accuracies using leave-one-subject-out cross validation range from 58 to 97% for custom decision tree classifier, from 56 to 97% for automatically generated decision tree, and from 22 to 96% for artificial neural network. Total classification accuracy is 82 % for custom decision tree classifier, 86% for automatically generated decision tree, and 82% for artificial neural network.

Activities of Daily Living↗

PubMed2026

The Computational Revolution in Natural Product Research: A Data-Driven Roadmap for Next-Generation Drug Development.

Natural products (NPs) have historically provided the foundational scaffolds for drug development, yet traditional bioprospecting faces critical limitations: high rediscovery rates, laborious isolation workflows, and substantial attrition during clinical translation. The emergence of big data technologies is fundamentally transforming this landscape, enabling a shift from serendipity-based discovery toward systematic, data-driven approaches. This review examines how the integration of artificial intelligence (AI), machine learning (ML), and multi-omics datasets is accelerating natural product research across three key domains: (1) genome mining for biosynthetic gene cluster identification using platforms such as antiSMASH, (2) cheminformatics-driven prediction of structure-activity relationships and ADMET properties, and (3) metabolomics-guided dereplication to prioritize novel bioactive scaffolds. We evaluate the convergence of genomics, metabolomics, and computational chemistry in enabling in silico lead optimization and the discovery of cryptic metabolites from previously inaccessible microbial taxa. While challenges in data standardization and scalability persist, the synergy between big data and NP research is accelerating clinical translation. Despite persistent challenges in data standardization, scalability, and equitable benefit-sharing, the convergence of big data and NP research is poised to redefine drug development. These advances position computational NP research as a cornerstone of next-generation drug development.

big data analytics↗

PubMed2026

Artificial intelligence for anticancer drug discovery from natural products of macroalgae and sponges: A systematic review.

Marine natural products (MNPs) from macroalgae and marine sponges have inspired clinically important anticancer agents, including the cytarabine pharmacophore and the eribulin scaffold, while cyanobacterial dolastatin chemistry supplies the auristatin payloads of several marine-inspired antibody-drug conjugates (ADCs) such as brentuximab vedotin. Artificial intelligence (AI) methods, encompassing both classical machine learning (ML) with hand-engineered features and modern deep learning (DL) with many-layered neural networks, are increasingly supporting key decisions in natural-product anticancer drug discovery, including bioactivity prediction, target identification, absorption, distribution, metabolism, excretion and toxicity (ADMET) filtering, generative analogue design, and the selection of preclinical candidates. DL architectures relevant to this field include graph neural networks, transformer-based molecular generators, diffusion models for protein-ligand docking, and convolutional networks for mass spectrometry, while classical ML contributes interpretable fingerprint-based bioactivity models and molecular networking for dereplication. This review follows a systematic literature review methodology to organize the landscape of AI methods now applied to MNP anticancer discovery, distinguishing ML and DL approaches where relevant, situating them within the chemical context of macroalgal and sponge-derived oncology leads, and critically examining published case studies, including validation level (computational, in vitro, in vivo, clinical). The principal bottleneck for medical translation has shifted partly from algorithmic capability toward data infrastructure and experimental validation. Sparse, heterogeneous, and taxonomically biased bioactivity records limit what current models can learn and reduce the reliability of AI-prioritized candidates entering the preclinical pipeline. A roadmap is proposed that prioritizes open MNP-specific benchmarks, symbiont-aware modeling, and active learning loops with synthesizability and ADMET constraints. These AI workflows may accelerate the prioritization of marine-derived anticancer leads and support earlier, more evidence-based translational decisions in oncology drug development.

Biological Products↗

PubMed1990

Overcoming deficiencies of the rule-based medical expert system.

One of the current deficiencies of the rule-based expert system is its static nature. As these systems are applied to medicine, this shortcoming becomes accentuated by: the rapid speed at which new knowledge is generated, the regional differences associated with the expression of many diseases, and the rate at which patient demographics and disease incidence change over time. This research presents a solution to the static nature of the rule-based expert system by proposing a hybrid system. This system consists of an expert system and a statistical analysis system linked to a patient database. The additional feature of a rule base manager which initiates automatic database analysis to refresh the statistical correlation of each rule ensures a dynamic, current, statistically accurate rule base. The philosophical differences between data and knowledge are also addressed as they apply to this type of hybrid system. The system is then used to generate four rule bases from different knowledge sources. These rule bases are then compared.

Artificial Intelligence↗

PubMed2004

Improving the performance of dictionary-based approaches in protein name recognition.

Dictionary-based protein name recognition is often a first step in extracting information from biomedical documents because it can provide ID information on recognized terms. However, dictionary-based approaches present two fundamental difficulties: (1) false recognition mainly caused by short names; (2) low recall due to spelling variations. In this paper, we tackle the former problem using machine learning to filter out false positives and present two alternative methods for alleviating the latter problem of spelling variations. The first is achieved by using approximate string searching, and the second by expanding the dictionary with a probabilistic variant generator, which we propose in this paper. Experimental results using the GENIA corpus revealed that filtering using a naive Bayes classifier greatly improved precision with only a slight loss of recall, resulting in 10.8% improvement in F-measure, and dictionary expansion with the variant generator gave further 1.6% improvement and achieved an F-measure of 66.6%.

Abstracting and Indexing↗

PubMed2005

Reliability assessment of an automated forced swim test device using two mouse strains.

The Porsolt forced swim test (FST) is one of the most widely used behavioral tests in the evaluation of the antidepressant effects of drugs. It is based on the fact that these drugs reduce the depression-related behaviors of learned helplessness. The model has been modified for use in mice. In contrast to rats, mice are exposed to forced swimming only once and their immobility behavior is measured and considered a "depression-like" phenotype. Like many other behavioral tests, FST can be affected by observer-related artifacts. In recent years, automated testing systems have been developed to decrease artifacts that may greatly influence the interpretation of results. In this work, we used two strains of mice, i.e., C3H/HeJ and C57BL/6J, which differ in their FST immobility times. We employed a new commercially available automated FST device and a blinded observer-based FST, and we examined their ability to measure behavioral differences between these two mouse strains. Our results suggest that the tested automated FST system generates reliable data comparable to results obtained by trained observers.

Algorithms↗