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PubMed1999

SuperStar: a knowledge-based approach for identifying interaction sites in proteins.

An empirical method for identifying interaction sites in proteins is described and validated. The method is based entirely on experimental information about non-bonded interactions occurring in small-molecule crystal structures. These data are used in the form of scatterplots that show the experimentally observed distribution of one functional group (the "contact group" or "probe") around another. A template molecule (e.g. a protein binding site) is broken down into structure fragments and the scatterplots, showing the distribution of a chosen probe around these structure fragments, are superimposed on the corresponding parts of the template. The scatterplots are then translated into a three-dimensional map that shows the propensity of the probe at different positions around the template molecule. The method is illustrated for l -arabinose-binding protein, complexed with l -arabinose and with d -fucose, and for dihydrofolate reductase complexed with methotrexate. The method is validated on 122 X-ray structures of protein-ligand complexes. For all the binding sites of these proteins, propensity maps are generated for four different probes: a charged NH+3nitrogen, a carbonyl oxygen, a hydroxyl oxygen and a methyl carbon atom. Next, the maps are compared with the experimentally observed positions of ligand atoms of these types. For 74% of these ligand atoms (84% of the solvent-inaccessible ones) the calculated propensity of the matching probe at the experimental positions is higher than expected by chance. For 68% of the atoms (82% of the solvent-inaccessible ones) the propensity of the matching probe is higher than that of the other three probes. These results indicate that the approach generally gives good predictions for protein-ligand interactions. The potential applications of the propensity maps range from an aid in manual docking and structure-based drug design to their use in pharmacophore development.

Artificial Intelligence↗

PubMed2003

Petri net modeling of high-order genetic systems using grammatical evolution.

Understanding how DNA sequence variations impact human health through a hierarchy of biochemical and physiological systems is expected to improve the diagnosis, prevention, and treatment of common, complex human diseases. We have previously developed a hierarchical dynamic systems approach based on Petri nets for generating biochemical network models that are consistent with genetic models of disease susceptibility. This modeling approach uses an evolutionary computation approach called grammatical evolution as a search strategy for optimal Petri net models. We have previously demonstrated that this approach routinely identifies biochemical network models that are consistent with a variety of genetic models in which disease susceptibility is determined by nonlinear interactions between two DNA sequence variations. In the present study, we evaluate whether the Petri net approach is capable of identifying biochemical networks that are consistent with disease susceptibility due to higher order nonlinear interactions between three DNA sequence variations. The results indicate that our model-building approach is capable of routinely identifying good, but not perfect, Petri net models. Ideas for improving the algorithm for this high-dimensional problem are presented.

Algorithms↗

PubMed2004

Intelligent decision making in disassembly process based on fuzzy reasoning petri nets.

Practical disassembly process planning is extremely important for efficient material recycling and components reuse. The research work for the process planning in literature focuses on the generation of optimal sequences based on the predictive information of products. The used products, unfortunately, exhibit high uncertainty since products may experience very different conditions during their use stage. The indeterminate characteristics associated to used products often makes the predetermined plan unrealistic. Their disassembly process has to be decided dynamically adaptive to the products' specific status. To be able to deal with uncertainty in a dynamic decision making process, this paper presents a fuzzy reasoning Petri net (FRPN) model to represent related decision making rules in disassembly process. Using the proposed fuzzy reasoning algorithm based on the FRPN model, the multicriterion disassembly rules can be considered in the parallel way to make the decision automatically and quickly. Instead of producing the disassembly sequences before disassembling a whole product, the proposed method makes intelligent decisions based on dynamically updated status of components in the product at each disassembly step. Therefore, it is adaptive to the changes that arise during the process. Finally, an example is used to illustrate the application of the proposed methodology.

Algorithms↗

PubMed2005

Evaluation of semiautomated measurements of mesothelioma tumor thickness on CT scans.

RATIONALE AND OBJECTIVES: To evaluate the clinical acceptability of semiautomated methods for the measurement of mesothelioma tumor thickness in computed tomography (CT) scans. MATERIALS AND METHODS: A computer interface was developed to allow the acquisition of semiautomated mesothelioma tumor thickness measurements, which require the manual selection of a point along the outer margin of the tumor in a CT section. After application of an automated lung segmentation method, the computer automatically identifies a corresponding point along the inner margin of the tumor (as represented by the lung boundary), constructs a line segment between the manually selected outer tumor margin point and the computer-determined inner tumor margin point, and computes tumor thickness as the length of this line segment. Three radiologists and oncologists independently reviewed line segments representing the semiautomated measurements generated by three different algorithms at 134 measurement sites in the CT scans of 22 mesothelioma patients. The observers either accepted a measurement line segment or modified it through the interface. Differences between the initial semiautomated measurements and the measurements as modified by the observers were analyzed. RESULTS: The frequency with which observers accepted the semiautomated measurements without modification was as high as 86%. Of all measurements across all observers and methods (1,206 measurements), 89% were changed by 2 mm or less. CONCLUSION: We have developed semiautomated methods to measure mesothelioma tumor thickness. The potential of these methods has been demonstrated through an observer study. We expect these methods to become important tools for the efficient quantification of tumor extent.

Adult↗

PubMed2026

Maternal genetic variants associated with aneuploid conception: a narrative review.

BACKGROUND: Human aneuploid conception, a leading cause of infertility, pregnancy loss, and congenital disorders (e.g. Down's syndrome), arises from errors in chromosome segregation during oocyte meiosis or embryonic mitosis. While advanced maternal age is a well-established risk factor, significant inter-individual variation exists among younger women, suggesting a substantial role for maternal genetic determinants. OBJECTIVE AND RATIONALE: This review summarizes the identified maternal genetic variants associated with aneuploid conceptions and highlights directions for future research. SEARCH METHODS: We systematically searched PubMed, Embase, and the Cochrane Library (up to 12 January 2026), using key terms related to maternal genetics, genetic variants, aneuploidy, and pregnancy. Inclusion criteria were human studies, genetic confirmation of aneuploidy (in oocytes/embryos/products of conception/fetal cells), maternal variants (rare single-nucleotide variations, single-nucleotide polymorphisms, and small indels [≤50 bp]), and English-language publications. Exclusion criteria were non-human studies, structural/non-aneuploid numerical abnormalities, paternal factors, and conference abstracts. Extracted data items included study identifiers, population characteristics, variant details, detection methods, clinical phenotypes, type and origin of aneuploidy, pathogenicity or effect assessment, and gene inclusion in currently commercially available infertility next-generation sequencing (NGS) panels. Rare variants were classified per American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines, whereas common variants were evaluated based on effect estimates and functional validation. Study quality was appraised using a modified Newcastle-Ottawa Scale. Supplementary searches explored associations between the identified genes and a broader range of reproductive phenotypes. OUTCOMES: From 28 studies covering the broad clinical spectrum of aneuploid pregnancies (including embryo arrest, implantation failure, pregnancy loss, hydatidiform mole, and fetal aneuploidy), we identified maternal variants associated with aneuploid conceptions. These were functionally categorized into meiotic recombination, spindle dynamics, checkpoint enforcement, and the maternal-to-zygotic transition. Among them, variants in several genes are supported by higher-quality evidence, including likely pathogenic rare variants in KIF18A, ELL3, and CEP120, as well as common variants in PLK4 and CCDC66. Although some identified genes (HFM1, MCM9, MEI1, BUB1B, NLRP2, NLRP7, and TLE6) are included in commercial infertility NGS panels, their direct association with aneuploidy requires further validation. WIDER IMPLICATIONS: This review proposes that 'aneuploidy predisposition' constitutes a critical, mechanism-driven dimension for the genetic diagnosis of infertility, complementing phenotype-based frameworks. This approach would best serve women with unexplained infertility and a normal karyotype who have either a history of recurrent aneuploidy or heterogeneous reproductive phenotypes across different cycles. Adopting this perspective refines clinical genetic testing paradigms and underscores the need to prioritize artificial intelligence-enhanced clinico-genomic association studies and develop polygenic risk models integrated with clinical factors. PROSPERO REGISTRATION NUMBER: CRD42025636217.

Humans↗

PubMed2005

How to choose a mechanical ventilator.

PURPOSE OF REVIEW: To provide some practical and clinical considerations that may guide users through the decision process when choosing mechanical ventilators RECENT FINDINGS: Although the complexity of mechanical ventilators is steadily increasing, the importance of many devices developed over the course of the technical evolution is still a matter of discussion. Recent data demonstrate that the technical performance of equivalent ventilators (ie, machines of the same generation and category) is pretty similar, suggesting that the different manufacturers keep in step with new developments. Thus, other factors than technical limitations will probably influence the choice of ventilators. Among them the ability of the staff to understand the rationale of the different devices and controls as well as deal with the complexity of the ventilator may be particularly important. SUMMARY: Choosing mechanical ventilators should begin by defining the algorithms of how to ventilate a patient. Once this is done, a ventilator should allow the transformation of specific strategies into practice and the adaptation of the mechanical support to the needs of the individual patient. This procedure is crucially important, because ventilator therapy should always be determined by the physician and based on solid physiologic rationales rather than by the technical features of the machine.

Artificial Intelligence↗

PubMed2006

Hierarchical stochastic image grammars for classification and segmentation.

We develop a new class of hierarchical stochastic image models called spatial random trees (SRTs) which admit polynomial-complexity exact inference algorithms. Our framework of multitree dictionaries is the starting point for this construction. SRTs are stochastic hidden tree models whose leaves are associated with image data. The states at the tree nodes are random variables, and, in addition, the structure of the tree is random and is generated by a probabilistic grammar. We describe an efficient recursive algorithm for obtaining the maximum a posteriori estimate of both the tree structure and the tree states given an image. We also develop an efficient procedure for performing one iteration of the expectation-maximization algorithm and use it to estimate the model parameters from a set of training images. We address other inference problems arising in applications such as maximization of posterior marginals and hypothesis testing. Our models and algorithms are illustrated through several image classification and segmentation experiments, ranging from the segmentation of synthetic images to the classification of natural photographs and the segmentation of scanned documents. In each case, we show that our method substantially improves accuracy over a variety of existing methods.

Algorithms↗

PubMed1998

Knowledge through documentation: from patients' data records to the basis of knowledge.

The written documentation generated in the dental practice can only be used for clinical tasks under certain conditions. The systematic use of clinical information is not possible until therapy and record data are organized as empirical bases of knowledge by electronic application. Then all documented data are available to support clinical decisions. The first task of dental treatment planning is the assessment of the individualized prognosis. Empirical bases of knowledge can process the surveyed information for this purpose and present it in a suitable manner. Moreover, an empirical basis of knowledge is suitable for an analysis of the effectiveness of dental therapy measures and for the elaboration of empirical foundations for discussion between colleagues. The most advanced form of data application is the expert system, which is able to carry out case comparisons and draw model-based conclusions on the basis of empirical knowledge.

Artificial Intelligence↗

PubMed2005

A computational model for color naming and describing color composition of images.

The extraction of high-level color descriptors is an increasingly important problem, as these descriptions often provide links to image content. When combined with image segmentation, color naming can be used to select objects by color, describe the appearance of the image, and generate semantic annotations. This paper presents a computational model for color categorization and naming and extraction of color composition. In this paper, we start from the National Bureau of Standards' recommendation for color names, and through subjective experiments, we develop our color vocabulary and syntax. To assign a color name from the vocabulary to an arbitrary input color, we then design a perceptually based color-naming metric. The proposed algorithm follows relevant neurophysiological findings and studies on human color categorization. Finally, we extend the algorithm and develop a scheme for extracting the color composition of a complex image. According to our results, the proposed method identifies known color regions in different color spaces accurately, the color names assigned to randomly selected colors agree with human judgments, and the description of the color composition of complex scenes is consistent with human observations.

Algorithms↗

PubMed1989

Stereausis: binaural processing without neural delays.

A neural network model is proposed for the binaural processing of interaural-time and level cues. The two-dimensional network measures interaural differences by detecting the spatial disparities between the instantaneous outputs of the two ears. The network requires no neural delay lines to generate such attributes of binaural hearing as the lateralization of all frequencies, and the detection and enhancement of noisy signals. It achieves this by comparing systematically, at various horizontal shifts, the spatiotemporal responses of the tonotopically ordered array of auditory-nerve fibers. An alternative view of the network operation is that it computes approximately the cross correlation between the responses of the two cochleas by combining an ipsilateral input at a given characteristic frequency (CF) with contralateral inputs from locally off-CF locations. Thus the network utilizes the delays already present in the traveling waves of the basilar membrane to extract the correlation function. Simulations of the network operation with various signals are presented as are comparisons to computational schemes suggested for stereopsis in vision. Physiological arguments in support of this scheme are also discussed.

Artificial Intelligence↗

PubMed1994

A model for structured data entry based on explicit descriptional knowledge.

Clinical narratives in patient records are usually recorded in free text, limiting the use of this information for research, quality assessment, and decision support. This study focuses on the capture of clinical narratives in a structured format by supporting physicans with structured data entry (SDE). We analyzed and made explicit which requirements SDE should meet to be acceptable for the physician on the one hand, and generate unambiguous patient data on the other. Starting from these requirements, we found that in order to support SDE, the knowledge on which it is based needs to be made explicit: we refer to this knowledge as descriptional knowledge. We articulate the nature of this knowledge, and propose a model in which it can be formally represented. The model allows the construction of specific knowledge bases, each representing the knowledge needed to support SDE within a circumscribed domain. Data entry is made possible through a general entry program, of which the behavior is determined by a combination of user input and the content of the applicable domain knowledge base. We clarify how descriptional knowledge is represented, modeled, and used for data entry to achieve SDE, which meets the proposed requirements.

Artificial Intelligence↗

PubMed2005

Rate-distortion optimal video summary generation.

The need for video summarization originates primarily from a viewing time constraint. A shorter version of the original video sequence is desirable in a number of applications. Clearly, a shorter version is also necessary in applications where storage, communication bandwidth, and/or power are limited. The summarization process inevitably introduces distortion. The amount of summarization distortion is related to its "conciseness," or the number of frames available in the summary. If there are m frames in the original sequence and n frames in the summary, we define the summarization rate as m/n, to characterize this "conciseness". We also develop a new summarization distortion metric and formulate the summarization problem as a rate-distortion optimization problem. Optimal algorithms based on dynamic programming are presented and compared experimentally with heuristic algorithms. Practical constraints, like the maximum number of frames that can be skipped, are also considered in the formulation and solution of the problem.

Algorithms↗

PubMed2005

Next generation autonomous wheelchair control.

Often times the physically challenged, limited to a wheelchair, also have difficulty with vision. In order to help, something must "see" for them. Therefore there must be some way for a wheelchair to know its environment, sense where it is, and where it must go. It also must be able to avoid any obstacles which are not normally part of the environment. An autonomous wheelchair will serve an important role by allowing users more freedom and independence. This design challenge is broken into four major steps: wheelchair control, environment recognition, route planning, and obstacle avoidance. The first step is to reverse engineer a wheelchair and rebuild the controls, which will be the main topic of discussion for this paper. Two big challenges with this step are high power motor control and joystick control. An H-bridge motor interface, controlled by a microprocessor, was designed for the motors. The joystick control is handled with the same microprocessor.

Artificial Intelligence↗

PubMed1999

A strategy for developing practice guidelines for the ICU using automated knowledge acquisition techniques.

OBJECTIVES: To implement practice guideline entry tools in a reminder system in order to provide decision support to health care workers in clinical care and emergency care environments. To design a knowledge acquisition environment that enables physicians to formulate, update, and verify guidelines without the assistance of a knowledge engineer. METHODS: We developed a knowledge acquisition environment for the Intensive Care Unit (ICU) consisting of 1) a graphical knowledge acquisition tool, 2) tools that perform logical and semantic tests on proposed guidelines, 3) a Patient Data Management System (PDMS) containing clinical patient data, and 4) an expert system that reminds ICU health care workers of inconsistencies between a treatment plan and implemented guidelines. Physicians enter the guidelines using the knowledge acquisition tool, after which consistency and correctness tests are performed on the guidelines. The guidelines are then transferred to the knowledge base of the reminder system and validated by applying the new guidelines to a large stored data set of previous patients. If the new guidelines are approved, they are exported to the reminder system that is used in daily practice. RESULTS: ICU physicians used the knowledge acquisition tool to enter 58 guidelines into the reminder system's knowledge base. These guidelines were tested on a data set consisting of 803 previously admitted patients. As a result, 27 guidelines fired at least once, generating 406 reminders in total. Of the 406 generated reminders, 356 (88%) were issued correctly and 50 (12%) were false alarms. The reminders that were issued correctly involved 3 situations: 1) the database contained inconsistent or incomplete information, 2) the actions or decisions of the health care workers were not the most appropriate ones, and 3) there was a potential risk involved. All false alarms were caused by the fact that the corresponding guidelines were not specific enough to handle certain exceptions. As a result of this analysis, the guidelines could be improved in such a way as to eliminate all false alarms. CONCLUSIONS: These first results demonstrate that this bottom-up knowledge acquisition strategy, implemented by the automated knowledge acquisition tools, enables medical specialists to improve the quality of computer support in an ICU without assistance of a knowledge engineer.

Artificial Intelligence↗

PubMed2006

Inferring global levels of alternative splicing isoforms using a generative model of microarray data.

MOTIVATION: Alternative splicing (AS) is a frequent step in metozoan gene expression whereby the exons of genes are spliced in different combinations to generate multiple isoforms of mature mRNA. AS functions to enrich an organism's proteomic complexity and regulates gene expression. Despite its importance, the mechanisms underlying AS and its regulation are not well understood, especially in the context of global gene expression patterns. We present here an algorithm referred to as the Generative model for the Alternative Splicing Array Platform (GenASAP) that can predict the levels of AS for thousands of exon skipping events using data generated from custom microarrays. GenASAP uses Bayesian learning in an unsupervised probability model to accurately predict AS levels from the microarray data. GenASAP is capable of learning the hybridization profiles of microarray data, while modeling noise processes and missing or aberrant data. GenASAP has been successfully applied to the global discovery and analysis of AS in mammalian cells and tissues. RESULTS: GenASAP was applied to data obtained from a custom microarray designed for the monitoring of 3126 AS events in mouse cells and tissues. The microarray design included probes specific for exon body and junction sequences formed by the splicing of exons. Our results show that GenASAP provides accurate predictions for over one-third of the total events, as verified by independent RT-PCR assays. SUPPLEMENTARY INFORMATION: http://www.psi.toronto.edu/GenASAP.

Algorithms↗

PubMed2006

An iterative refinement algorithm for consistency based multiple structural alignment methods.

MOTIVATION: Multiple STructural Alignment (MSTA) provides valuable information for solving problems such as fold recognition. The consistency-based approach tries to find conflict-free subsets of alignments from a pre-computed all-to-all Pairwise Alignment Library (PAL). If large proportions of conflicts exist in the library, consistency can be hard to get. On the other hand, multiple structural superposition has been used in many MSTA methods to refine alignments. However, multiple structural superposition is dependent on alignments, and a superposition generated based on erroneous alignments is not guaranteed to be the optimal superposition. Correcting errors after making errors is not as good as avoiding errors from the beginning. Hence it is important to refine the pairwise library to reduce the number of conflicts before any consistency-based assembly. RESULTS: We present an algorithm, Iterative Refinement of Induced Structural alignment (IRIS), to refine the PAL. A new measurement for the consistency of a library is also proposed. Experiments show that our algorithm can greatly improve T-COFFEE performance for less consistent pairwise alignment libraries. The final multiple alignment outperforms most state-of-the-art MSTA algorithms at assembling 15 transglycosidases. Results on three other benchmarks showed that the algorithm consistently improves multiple alignment performance. AVAILABILITY: The C++ code of the algorithm is available upon request.

Algorithms↗

PubMed1997

Knowledge generating in expert systems as support of immunological investigations.

One of the most important parameters in immunologic investigations linked with the analysis of TIL phenotypic characteristics is the ratio between particular populations and subpopulations of TIL. Values of exact parameter are not obtained with its direct measurement but with indirect measurement of values in its numerator and its denominator that represent mean values of absolute TIL figures. Exactness of estimate of specific parameter is obtained by determining the intervals of values where this parameter should lie. Determination of confidence intervals for relationship between particular populations and subpopulations of TIL was performed using Fieller's theorem which full filled requests from the nature and process of obtaining exact parameter. By analyzing the obtained confidence intervals informations can be obtained regarding TIL phenotypic characteristics of various histologic types of tumors and various types of intervals within, as well as changes in TIL content that are occurring in malignant tumors.

Artificial Intelligence↗

PubMed2026

Precision medicine in combating antimicrobial resistance: A comprehensive review.

Antimicrobial resistance (AMR) represents one of the most pressing threats to global public health, undermining the effectiveness of modern antimicrobial therapy and challenging decades of medical progress. This comprehensive review examines the transition from broad-spectrum empirical therapy toward precision medicine as an integrated framework for improving antimicrobial use and combating AMR. Precision medicine seeks to tailor treatment decisions by combining pathogen-specific genomic and resistance data with relevant host characteristics to optimize therapy while limiting unnecessary antimicrobial exposure and the selective pressures that drive resistance. The review synthesizes advances reported from 2020, highlighting established and emerging approaches including rapid molecular diagnostics, next-generation sequencing, CRISPR-based detection, machine learning (ML)-assisted decision support, precision dosing, and targeted therapeutics such as bacteriophage therapy, antimicrobial peptides, and bacterial proteolysis-targeting chimeras. Rather than functioning as isolated technologies, these approaches achieve their greatest clinical value when integrated within antimicrobial stewardship programs and a One Health framework that recognizes the interconnected human, animal, and environmental drivers of resistance. Despite considerable progress, important challenges remain, including equitable access to advanced technologies, interpretation of increasingly complex datasets, workforce and infrastructure limitations, and evolving regulatory pathways for novel diagnostics and therapeutics. This review concludes that while precision medicine is not a standalone solution, its successful implementation will depend on coordinated integration of diagnostics, host factors, computational tools, pharmacological optimization, and stewardship strategies to improve patient outcomes while preserving the long-term effectiveness of existing antimicrobials.

Antimicrobial resistance↗