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PubMed2026

AI-integrated digital breeding for crop improvement.

Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.

artificial intelligence↗

PubMed1994

A case-based assistant for clinical psychiatry expertise.

Case-based reasoning is an artificial intelligence methodology for the processing of empirical knowledge. Recent case-based reasoning systems also use theoretic knowledge about the domain to constrain the case-based reasoning. The organization of the memory is the key issue in case-based reasoning. The case-based assistant presented here has two structures in memory: cases and concepts. These memory structures permit it to be as skilled in problem-solving tasks, such as diagnosis and treatment planning, as in interpretive tasks, such as clinical research. A prototype applied to clinical work about eating disorders in psychiatry, reasoning from the alimentary questionnaires of these patients, is presented as an example of the system abilities.

Anorexia↗

PubMed2026

Decoding glioblastoma evolution and heterogeneity through mechanistic modeling: implications for clinical translation.

Glioblastoma (GBM) is one of the most aggressive and lethal primary brain tumors in adults, characterized by dynamic clonal evolution and extensive genomic, cellular, spatial, and microenvironmental heterogeneity. Multi-omics studies have revealed that GBM follows complex evolutionary trajectories involving genetic, epigenetic, transcriptional, and immune-microenvironmental remodeling as tumors grow, adapt to the brain microenvironment, and acquire therapeutic resistance. Increasing evidence suggests that GBM may originate from aberrant neural stem or progenitor cells, including those residing in the subventricular zone, and that glioblastoma stem cells (GSCs) contribute to tumor propagation, heterogeneity, and recurrence. A key conceptual challenge is to reconcile hierarchical cancer stem cell models, in which GSCs are viewed as relatively stable tumor-propagating subpopulations, with dynamic state plasticity models, in which stem-like properties can be reversibly acquired or lost during transitions among proneural-like, mesenchymal-like, invasive, and therapy-tolerant states. Recent advances in single-cell profiling, spatial transcriptomics, lineage tracing, organoid culture, 3D bioprinting, genetically engineered models, and artificial intelligence (AI)-assisted computational modeling have substantially improved the ability to study these processes. However, no currently available model fully recapitulates human GBM heterogeneity, recurrence, treatment history, and tumor-microenvironment interactions. Therefore, model selection should be guided by clearly defined mechanistic questions rather than by reliance on any single platform. This review summarizes current advances in in vitro, ex vivo, in vivo, and computational models for studying GBM evolution and heterogeneity, and discusses how integrated model pipelines may improve preclinical drug testing, treatment-response prediction, and precision neuro-oncology.

Humans↗

PubMed2000

Rule extraction by successive regularization.

Knowledge acquisition is, needless to say, important, because it is a key to the solution to one of the bottlenecks in artificial intelligence. Recently, knowledge acquisition using neural networks, called rule extraction, is attracting wide attention because of its computational simplicity and ability to generalize. Proposed in this paper is a novel approach to rule extraction named successive regularization. It generates a small number of dominant rules at an earlier stage and less dominant rules or exceptions at later stages. It has various advantages such as robustness of computation, better understanding, and similarity to child development. It is applied to the classification of mushrooms, the recognition of promoters in DNA sequences and the classification of irises. Empirical results indicate superior performance of rule extraction in terms of the number and the size of rules for explaining data.

Agaricales↗

PubMed2000

DATA: a decision aid for management of the patient with stable angina pectoris.

OBJECTIVE: The patient with stable disabling angina must choose between bypass surgery, coronary angioplasty, or medical therapy. Estimation of comparative outcomes of these alternative therapies is difficult. DESIGN: Utilizing Artificial Intelligence, an expert-system computerized decision aid, DATA (Decision Aid for Therapy of Angina), was developed to run on a personal computer and to calculate the probability of each possible clinical event based on individual patient characteristics. For each therapeutic option, relative clinical outcomes and anticipated charges are computed. Ten patients were evaluated retrospectively by DATA and by a group of cardiologists. RESULTS: DATA agreed with the primary therapy given to all patients. The physician group underestimated the value of alternative therapies and underestimated charges for all therapies. CONCLUSIONS: This decision aid allows comparison of alternative therapies in terms of relative patient outcome and anticipated costs can be better estimated.

Angina Pectoris↗

PubMed1996

Computer-guided randomized concentration-controlled trials of tacrolimus in autoimmunity: multiple sclerosis and primary biliary cirrhosis.

A randomized concentration-controlled clinical trial (RCCCT) is a trial design in which patients are randomized to predefined blood drug concentrations (low, medium, high). If the concentration ranges are sufficiently separated, this study design can reveal important blood concentration-response relations. Tacrolimus is a potent yet "infant" immunosuppressant for the treatment and prevention of graft rejection and has been shown to exhibit significant clinical activity in some immune-mediated disorders. A tacrolimus artificial intelligence modeling system (AIMS) was used to guide patient dosing to achieve target concentrations specified by the study protocols. In the Multiple Sclerosis study group, we were able to define a concentration range (0.3-0.7 ng/ml) that appeared to show efficacy and minimal tacrolimus toxicity. Patients randomized to the high zone (0.6-1.2 ng/ml) in the Primary Biliary Cirrhosis study group showed significant reduction (approximately 50%) in surrogate efficacy markers [aspartate aminotransferase (SGOT), alanine aminotransferase (SGPT)] compared with patients in the low zone (0.1-0.6 ng/ml). Therefore the RCCCT allowed the detection and delineation of clinically significant concentration-response relations in an ethical and efficient manner.

Artificial Intelligence↗

PubMed1993

Two-dimensional electrophoresis image interpretation.

A novel method to interpret two-dimensional electrophoresis gels is presented. Genetic background and electrophoretic processes are summarized. Present methods to analyze gel images and to exploit series of gels are described, then their drawbacks are outlined. Artificial Intelligence techniques are introduced to build an image interpretation system which can compensate for certain failures of present methods and augment their efficiency. By reproducing methods of biological experts, this system automatically identifies proteins--whether isolated or inside constellations--on an electrophoretic gel. This system is based on a modular architecture, featuring image processing procedures, which allows extraction of parameters on the image and a top-down and bottom-up reasoning process. The reasoning process first matches extracted parameters to possible geometric models of proteins; it then returns to the image to determine possible missing elements on the gel. A prototype of this system was implemented and tested on plasma gels to identify apolipoproteins.

Apolipoproteins↗

PubMed1995

On the heuristic nature of medical decision-support systems.

In the realm of medical decision-support systems, the term "heuristic systems" is often considered to be synonymous with "medical artificial intelligence systems" or with "systems employing informal model(s) of problem solving". Such a view may be inaccurate and possibly impede the conceptual development of future systems. This article examines the nature of heuristics and the levels at which heuristic solutions are introduced during system design and implementation. The authors discuss why heuristics are ubiquitous in all medical decision-support systems operating at non-trivial domains, and propose a unifying definition of heuristics that encompasses formal and ad hoc systems. System developers should be aware of the heuristic nature of all problem solving done in complex real world domains, and characterize their own use of heuristics in describing system development and implementation.

Artificial Intelligence↗

PubMed2004

Integrating the New York citywide immunization registry and the childhood blood lead registry.

In February of 2004, the New York City Department of Health and Mental Hygiene completed the integration of its childhood immunization and blood lead test registry databases, each containing over 2 million children. A modular approach was used to build a separate integrated system, called Master Child Index, to include all children in both the immunization and lead test registries. The principal challenge of this integration was to properly align records so that a child represented in one database is matched with the same child in the other database. To accomplish this task as well as to identify internal duplicate records within each database, an artificial intelligence record linkage system was created. The preliminary results show high rates of accurate merging of records both within and between the two databases. The 4,610,585 records contained in both databases before Master Child Index implementation consolidated into 2,977,290 records in the integrated system. The matching system eliminated 523,720 duplicate records within the two databases and matched and merged 1,109,575 records between the two databases. The Department of Health and Mental Hygiene plans to further develop the Master Child Index and use it as the department-wide, record-matching system.

Artificial Intelligence↗

PubMed2004

Multirobot systems: a classification focused on coordination.

Multirobot systems (MRS) are, nowadays, an important research area within robotics and artificial intelligence and a growing number of systems have recently been presented in the literature. Since application domains and tasks that are faced by MRS are of increasing complexity, the ability of the robots to cooperate can be regarded as a fundamental feature. In this paper, we present a survey of the recent work in the area by specifically examining the forms of cooperation and coordination realized in the MRS. In particular, we propose a new taxonomy for classification of the approaches to coordination in MRS and we describe some systems, which we consider representative in our taxonomy. We finally discuss the outcomes of our analysis and try to highlight future trends of the research on MRS.

Algorithms↗

PubMed2025

Diagnosing the undiagnosed: AI-enhanced multimodal modeling for placental mesenchymal dysplasia in high-risk pregnancies.

Placental mesenchymal dysplasia (PMD) is a rare vascular placental disorder that mimics molar pregnancy but often coexists with a viable fetus, making its misdiagnosis potentially devastating. In high-risk pregnancies, artificial intelligence (AI)-enhanced multimodal modeling - incorporating imaging, genomics, proteomics, and clinical features - offers a transformative diagnostic strategy. Leveraging Bayesian hyperparameter optimization for model refinement, this approach improves diagnostic accuracy while reducing uncertainty and clinician hesitation. Recent clinical studies support its efficacy and interpretability through SHAP and LIME models, while real-time surgical enhancements using Bayesian methods highlight its broader clinical utility. Despite current challenges such as data heterogeneity and integration barriers, multimodal AI provides unprecedented resolution in placental analysis, enabling precise differentiation between PMD and similar fetopathies. Ultimately, this advancement supports timely, non-invasive diagnosis, personalized management, and emotionally informed decision-making aligned with ethical AI implementation standards.

Bayesian optimization↗

PubMed1996

Role of pharmacokinetic-pharmacodynamic principles in rational and cost-effective drug development.

An important goal of drug development is to define dose and concentration-response relationships for new drugs and biologics. Such critical information from controlled clinical trials can provide primary evidence of efficacy and safety and an informative database for devising dosing instructions for clinical use. This article describes applications of pharmacologic principles [pharmacokinetic-pharmacodynamic (PK-PD)] and modeling methods for drugs in which the evaluation process is guided by and/or identifies significant PK and/or PD variability in drug response. In the case of the recently registered immunosuppressive agent, tacrolimus, preclinical PK-PD in model systems can be used to rationally design safe and effective immunomodulatory dosing regimens for phase 1 clinical studies. Furthermore, a study design based on concentration control guided by a novel artificial intelligence modeling system (AIMS) can be efficiently applied to conduct randomized clinical trials in auto-immunity and to implement cost-effective therapeutic drug monitoring of tacrolimus and cyclosporine in clinical transplantation. In the case of a cardioselective beta-adrenergic blocking agent, betaxolol, marketed for essential hypertension, population PD modeling can be shown to be a more efficient method for estimating dose response compared with standard statistical tests. Using a sigmoid Emax PD model, only a fraction (40 of 300) of the randomized patients was needed to demonstrate dose response. Therefore, two methods, i.e., PD modeling of dose response and AIMS-guided dosing, can achieve significant cost benefits for drug developers, patient care, and the health care system.

Artificial Intelligence↗

PubMed1999

The application of a computerized measurement of the content analysis of natural language to the assessment of the effects of psychoactive drugs.

This article aims to answer the question whether developing technology is now capable of measuring objectively and accurately the effects of psychoactive pharmacological agents by means of computerized assessments of psychological states and traits through the analysis of content and form of people's speech. At the present time, psychopharmacological researchers involved in clinical trials rely on DSM-IV criteria and standardized self-report measures and observer rating scales to assess psychoactive drug effects. Attention is drawn to the potentially unrecognized measurement errors and relatively low interrater reliability by these methods--for example, all raters are not free of observer bias and every subject administered a drug is not equally and accurately well-informed about the self. The computerized content analysis methods tend to avoid these biases and measurement errors. A review is provided describing the Gottschalk-Gleser method of measuring psychobiological dimensions from the form and content of short (usually five-minute) speech samples of verbal behavior, generally elicited by standardized and purposely ambiguous instructions to talk about any interesting or dramatic personal life experiences. Norms have been obtained by this method of speech elicitation, adjusted for age, sex and educational level. Sections are provided covering cross-cultural and language validation research on the Gottschalk-Gleser content analysis method, the influence of medical or psychiatric illness as well as psychoactive drugs on verbal content analysis-derived scores, and the research carried out for more than 20 years computerizing this content analysis procedure through the development of artificial intelligence software enabling these measurements to be done from typescripts of speech samples on computer diskettes. A brief review deals with the general applications of this method of measurement to basic and clinical psychiatry, psychosomatic medicine, neuropsychology, the diagnostic process in a psychiatry outpatient clinic, children's mental health problems, dream research, and assessment of mental processes during PET scanning of the brain. This is followed by a review of the applications of this method of content analysis of speech to neuropsychopharmacological testing of antianxiety, antidepressive, antipsychotic, and other psychoactive drugs, as well as to pharmacokinetic variables and clinical response. This method is now in the process of being used in clinical trials in psychopharmacology and is recommended for more extensive use in this research area.

Artificial Intelligence↗

PubMed2001

Hybrid expert system for decision supporting in the medical area: complexity and cognitive computing.

This paper proposes a hybrid expert system (HES) to minimise some complexity problems pervasive to the artificial intelligence such as: the knowledge elicitation process, known as the bottleneck of expert systems; the model choice for knowledge representation to code human reasoning; the number of neurons in the hidden layer and the topology used in the connectionist approach; the difficulty to obtain the explanation on how the network arrived to a conclusion. Two algorithms applied to developing of HES are also suggested. One of them is used to train the fuzzy neural network and the other to obtain explanations on how the fuzzy neural network attained a conclusion. To overcome these difficulties the cognitive computing was integrated to the developed system. A case study is presented (e.g. epileptic crisis) with the problem definition and simulations. Results are also discussed.

Algorithms↗

PubMed2006

Usefulness of traditionally defined herbal properties for distinguishing prescriptions of traditional Chinese medicine from non-prescription recipes.

Traditional Chinese medicine (TCM) has been widely practiced and is considered as an attractive to conventional medicine. Multi-herb recipes have been routinely used in TCM. These have been formulated by using TCM-defined herbal properties (TCM-HPs), the scientific basis of which is unclear. The usefulness of TCM-HPs was evaluated by analyzing the distribution pattern of TCM-HPs of the constituent herbs in 1161 classical TCM prescriptions, which shows patterns of multi-herb correlation. Two artificial intelligence (AI) methods were used to examine whether TCM-HPs are capable of distinguishing TCM prescriptions from non-TCM recipes. Two AI systems were trained and tested by using 1161 TCM prescriptions, 11,202 non-TCM recipes, and two separate evaluation methods. These systems correctly classified 83.1-97.3% of the TCM prescriptions, 90.8-92.3% of the non-TCM recipes. These results suggest that TCM-HPs are capable of separating TCM prescriptions from non-TCM recipes, which are useful for formulating TCM prescriptions and consistent with the expected correlation between TCM-HPs and the physicochemical properties of herbal ingredients responsible for producing the collective pharmacological and other effects of specific TCM prescriptions.

Algorithms↗

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↗

PubMed1993

Inductive learning and biological sequence analysis. The PLAGE program.

Inductive learning, also called 'learning from examples', is a subfield of artificial intelligence. Inductive learning methods are able to deal with 'structural descriptions'. These portray objects as composite structures consisting of various components. The use of structural descriptions to represent biological objects is appealing. For instance, they have been used by Rawlings et al [1] for symbolically and comprehensively representing the folding of proteins. This paper shows how inductive learning techniques may be used for extracting information from biological objects. We briefly describe some general techniques for describing objects in a structural way and for learning from these descriptions. We present details of a program that we developed, PLAGE, and show the application of this program for a study on signal peptides, which was done in collaboration with A Danchin [2,3]. Finally, we survey some other approaches and applications of inductive learning to molecular biology.

Algorithms↗

PubMed1998

IDEM: a Web application of case-based reasoning in histopathology.

Different software engineering and artificial intelligence methods can be used to design Internet retrieval of prototypical medical images. We used the case-based reasoning (CBR) approach to provide an 'intelligent' access to a collection of illustrated medical cases through the Internet. This paper presents a Web interface for the CBR system IDEM (image and diagnosis from examples in medicine) in the domain of breast pathology. Thanks to the definition of a similarity measure between the descriptions of cases we propose a flexible querying of the case-base and a quantitative browsing among cases through similarity links. The resemblance rates provided by the system argue for the quality and the relevancy of the retrieved data. The flexibility of the querying process is robust to missing information and could be adapted to a daily practice. The CBR approach is a promising method for a clinical relevant and an efficient retrieval of reference images and diagnosis clues through Internet.

Artificial Intelligence↗