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PubMed2026

Plant cis-regulatory grammar: Decoding the multidimensional code of transcriptional regulation for programmable crop engineering.

Cis-regulatory elements (CREs) orchestrate the spatiotemporal precision of gene expression that underlies plant development, adaptation, and domestication. Decoding the cis-regulatory grammar of plant genomes remains a central challenge in modern biology, with profound implications for programmable crop engineering. Here, recent conceptual and technological advances are synthesized to reshape our understanding of plant CREs. This review first argues that CRE function is not only an intrinsic property of DNA sequence alone but also emerges from a multidimensional context, including chromatin accessibility, histone modifications, three-dimensional genome topology, and cell type-specific regulatory landscapes. Furthermore, the convergence of single-cell epigenomics, high-throughput functional assays, and CRISPR-based dissection has begun to unravel this contextual grammar, revealing the computational principles governing transcriptional regulation. Critically, we propose that artificial intelligence (AI) platforms are catalyzing an ongoing transition from descriptive discovery to predictive engineering, wherein these platforms outperform natural evolution in designing synthetic CREs. Finally, a roadmap is outlined toward a plant regulatory grammar foundation model, which will enable truly predictive engineering of gene expression when fine-tuned for specific tasks. Collectively, the integration of single-cell resolution maps, precise genome editing, AI-driven design, and regulatory-compliant delivery systems promises to transform our ability to reprogram plant gene regulation for next-generation agriculture, bridging the gap between foundational regulatory biology and tangible crop improvement.

artificial intelligence↗

PubMedDate not supplied

MICRO-IDEA: improving decisions in epidemiological analysis by a microcomputer.

A microcomputer system has been designed to promote uniform and correct epidemiological data handling in the Local Health Units of the National Health Service in Italy. The system allows to define files of any format, to perform the common data manipulation procedures and to make simple statistical analyses. Two modules, based on artificial intelligence techniques, give the possibility to input from 'experts' and to update domain-specific knowledge concerning facts which characterize the application area covered by the file, and some consultation models which represent the reasoning to follow when solving problems of epidemiological data analysis. A decision support module allows an inexpert user to have access to these models, and to be guided in the choice of the appropriate procedure. The menu-driven interactive approach insures a friendly user-to-system interface, and entails a little training for effective use by health workers inexpert in informatics.

Artificial Intelligence↗

PubMed1986

ANEMIA: an expert consultation system.

Anemia is a knowledge-based consultation program for anemic states. It has been built using an artificial intelligence programming scheme, called EXPERT, which was developed at Rutgers University. At present, ANEMIA is able to provide assistance in the diagnosis and management of 64 disease entities. They include iron deficiency anemias, anemias due to chronic disorders, thalassemias, hemolytic anemias, arigenerative anemias, and a few other miscellaneous conditions. ANEMIA was tested against a data base of 220 retrospective and 100 new cases and the overall accuracy in the diagnostic performance was pretty good. Our intent is that ANEMIA ultimately will be able to serve as surrogate for the hematologist with the nonhematologist physician-user.

Anemia↗

PubMedDate not supplied

Genetic programming assisted stochastic optimization strategies for optimization of glucose to gluconic acid fermentation.

This article presents two hybrid strategies for the modeling and optimization of the glucose to gluconic acid batch bioprocess. In the hybrid approaches, first a novel artificial intelligence formalism, namely, genetic programming (GP), is used to develop a process model solely from the historic process input-output data. In the next step, the input space of the GP-based model, representing process operating conditions, is optimized using two stochastic optimization (SO) formalisms, viz., genetic algorithms (GAs) and simultaneous perturbation stochastic approximation (SPSA). These SO formalisms possess certain unique advantages over the commonly used gradient-based optimization techniques. The principal advantage of the GP-GA and GP-SPSA hybrid techniques is that process modeling and optimization can be performed exclusively from the process input-output data without invoking the detailed knowledge of the process phenomenology. The GP-GA and GP-SPSA techniques have been employed for modeling and optimization of the glucose to gluconic acid bioprocess, and the optimized process operating conditions obtained thereby have been compared with those obtained using two other hybrid modeling-optimization paradigms integrating artificial neural networks (ANNs) and GA/SPSA formalisms. Finally, the overall optimized operating conditions given by the GP-GA method, when verified experimentally resulted in a significant improvement in the gluconic acid yield. The hybrid strategies presented here are generic in nature and can be employed for modeling and optimization of a wide variety of batch and continuous bioprocesses.

Artificial Intelligence↗

PubMed1987

Computer simulations of neural information processing and the schizophrenia-mania dichotomy.

Recent developments in artificial intelligence use computer simulations of complex neural systems to model associative memory and gestalt-seeking during cognition. Perturbations imposed on such computer simulations caused catastrophic breakdowns of neural functioning. The resulting cognitive disturbances assumed two forms, one "schizophreniclike" and the other "maniclike." The former was induced by memory overload and resulted in misperceptions, loose associations, and also parasitic processing states that pathologically controlled the flow of associations. The latter was caused by increased randomness of neural activity, which induced "jumps" from one gestalt to another. The relationship between this differential model of psychotic disturbances and other studies of schizophrenia and mania were explored.

Arousal↗

PubMed2006

The computational neurobiology of learning and reward.

Following the suggestion that midbrain dopaminergic neurons encode a signal, known as a 'reward prediction error', used by artificial intelligence algorithms for learning to choose advantageous actions, the study of the neural substrates for reward-based learning has been strongly influenced by computational theories. In recent work, such theories have been increasingly integrated into experimental design and analysis. Such hybrid approaches have offered detailed new insights into the function of a number of brain areas, especially the cortex and basal ganglia. In part this is because these approaches enable the study of neural correlates of subjective factors (such as a participant's beliefs about the reward to be received for performing some action) that the computational theories purport to quantify.

Animals↗

PubMed2002

Case-based reasoning (CBR) for multicomponent analysis using sensor arrays: application to water quality evaluation.

This paper reports by the first time the use of a simple, convenient and improved method of artificial intelligence (case-based reasoning, CBR) for multicomponent analysis, involving the data treatment afforded by electrochemical sensor arrays. The suitability of this method has been shown in aqueous solutions containing mixtures of ions of different nature and concentration, being applied in this case to a rapid recognition and evaluation of mineral water samples chosen as an illustrative example. The convenience of such a procedure opens the way to dispose of a versatile tool applicable for obtaining analytical information about ions present in a wide range of other multicomponent systems, and also to ascribe properties inherent to the examined samples.

Artificial Intelligence↗

PubMed1993

Automated decision making: the role of expert computer systems in the future of optometry.

BACKGROUND: Artificial intelligence (AI) refers to the ability of a machine, or system, or computer program to make intelligent decisions using the same methods that humans would use. By definition, and expert system (ES) is a computer program that uses knowledge and inference procedures to solve problems which would otherwise require the application of human expertise. METHODS: The role of expert systems in current and future applications in a number of fields were reviewed. RESULTS: Expert systems have and will be making an impact in many fields, including optometry in the future. CONCLUSIONS: Expert systems present an opportunity for the profession of optometry to become more efficient or accurate, provided that their development cost could be justified.

Artificial Intelligence↗

PubMedDate not supplied

Knowledge-based systems in medicine--a Nordic research and development programme.

A Nordic research and development programme, 'KBS in Medicine' (KUSIN-MEDICINE), was run in 1986-89. Its main goal was to acquire an understanding of applying knowledge-based techniques in medicine and of the limitations of present-day artificial intelligence (AI) methodologies. The programme comprised four experimental installation sites (Tampere in Finland, Uppsala and Linköping in Sweden, and Aalborg in Denmark) each prototyping in one or more medical domains. The programme was financed by the Nordic Fund for Technological and Industrial Development, by national funds for applied research and by a number of industries. Prototype decision support systems were produced in the following domains: intensive care (Tampere, Uppsala, Linköping, Aalborg), clinical chemistry (Tampere, Uppsala) and clinical neurophysiology (Aalborg in collaboration with Turku and Uppsala). These served to transfer this technology to industry and helped to identify limitations of this technology.

Artificial Intelligence↗

PubMed2001

Modelling protein side-chain conformations using constraint logic programming.

Side-chain placement is an important sub-task in protein modelling. Selecting conformations for side-chains is a difficult problem because of the large search space to be explored. This problem can be addressed using constraint logic programming (CLP), which is an artificial intelligence technique developed to solve large combinatorial search problems. The side-chain placement problem can be expressed as a CLP program in which rotamer conformations are used as values for finite domain variables, and bad steric contacts involving rotamers are represented as constraints. This paper introduces the concept of null rotamers, and shows how these can be used in implementing a novel iterative approach. We present results that compare the accuracy of models constructed using different rotamer libraries and different domain variable enumeration heuristics. The results obtained using this CLP-based approach compare favourably with those obtained by other methods.

Algorithms↗

PubMed1997

Does diesel exhaust cause human lung cancer?

Recent reviews of epidemiological evidence on the relation between exposure to diesel exhaust (DE) and lung cancer risk have reached conflicting conclusions, ranging from belief that there is sufficient evidence to conclude that DE is a human lung carcinogen (California EPA, 1994) to conclusions that there is inadequate evidence to support a causal association between DE and human lung cancer (Muscat and Wynder, 1995). Individual studies also conflict, with both increases and decreases in relative risks of lung cancer mortality being cited with 95% statistical confidence. On balance, reports of elevated risk outnumber reports of reduced risk. This paper reexamines the evidence linking DE exposures to lung cancer risk. After briefly reviewing animal data and biological mechanisms, it surveys the relevant epidemiological literature and examines possible explanations for the discrepancies. These explanations emphasize the distinction between statistical associations, which have been found in many studies, and causal associations, which appear not to have been established. Methodological threats to valid causal inference are identified and new approaches for controlling them are proposed using recent techniques from artificial intelligence (AI) and computational statistics. These threats have not been adequately controlled for in previous epidemiological studies. They provide plausible noncausal explanations for the reported increases in relative risks, making it impossible to infer causality between DE exposure and lung cancer risk from these studies. A key contribution is to show how recent techniques developed in the AI-and-statistics literature can help clarify the causal interpretation of complex multivariate data sets used in epidemiological risk assessments. Applied to the key study of Garshick et al. (1988), these methods show that DE concentration has no positive causal association with occupational lung cancer mortality risk.

Animals↗

PubMed1993

Development of an expert system for pediatric auditory brainstem response interpretation.

Expert systems are computer programs which incorporate artificial intelligence technology and are created to emulate the decision-making abilities of human experts. The advantage of such systems lies in their ability to capture and model expert problem solving knowledge in a domain and make it available to an unlimited number of consumers in an economic and efficient way. The purpose of this project was to develop an expert system to interpret infant auditory brainstem response data as entered by the user. The resulting system provides diagnostic conclusions regarding hearing status, type of hearing loss, and brainstem function at an accuracy level equal to that of a human expert.

Artificial Intelligence↗

PubMed2024

Drug repurposing in status epilepticus.

The treatment of status epilepticus (SE) has changed little in the last 20 years, largely because of the high risks and costs of new drug development for SE. Moreover, SE poses specific challenges to drug development, such as patient diversity, logistical hurdles, and the need for acute treatment strategies that differ from chronic seizure prevention. This has reduced the appetite of industry to develop new drugs in this area. Drug repurposing is an attractive approach to address this unmet need. It offers significant advantages, including reduced development time, lower costs, and higher success rates, compared to novel drug development. Here I demonstrate how novel methods integrating biological knowledge and computational methods can be applied to drug repurposing in status epilepticus. Biological approaches focus on addressing mechanisms underlying drug resistance in SE (using for example ketamine, tacrolimus and safinamide) and longer-term consequences (using for example omaveloxolone, celecoxib and losartan). Additionally, artificial intelligence platforms, such as ChatGPT, can rapidly generate promising drug lists, while in silico methods can analyze gene expression changes to predict molecular targets. Combining AI and in silico approaches has identified several candidate drugs, including metformin, sirolimus and riluzole, for SE treatment. Despite the promise of repurposing, challenges remain, such as intellectual property issues and regulatory barriers. Nonetheless, drug repurposing presents a viable solution to the high costs and slow progress of traditional drug development for SE. This paper is based on a presentation made at the 9th London-Innsbruck Colloquium on Status Epilepticus and Acute Seizures, in April 2024.

Animals↗

PubMed2005

Improved fingerprint identification with supervised filtering enhancement.

An important step in the fingerprint identification system is the reliable extraction of distinct features from fingerprint images. Identification performance is directly related to the enhancement of fingerprint images during or after the enrollment phase. Among the various enhancement algorithms, artificial-intelligence-based feature-extraction techniques are attractive owing to their adaptive learning properties. We present a new supervised filtering technique that is based on a dynamic neural-network approach to develop a robust fingerprint enhancement algorithm. For pattern matching, a joint transform correlation (JTC) algorithm has been incorporated that offers high processing speed for real-time applications. Because the fringe-adjusted JTC algorithm has been found to yield a significantly better correlation output compared with alternate JTCs, we used this algorithm for the identification process. Test results are presented to verify the effectiveness of the proposed algorithm.

Algorithms↗

PubMed1992

Quality control in nerve conduction studies with coupled knowledge-based system approach.

Contemporary equipment used for nerve conduction studies is usually capable of computerized measurement of latency, amplitude, duration, and area of nerve and muscle action potentials and resulting conduction velocities. Abnormalities can be due to technical error or disease. Identification of technical error is a major element of quality control in electromyography, and artificial intelligence could be useful for this purpose. We have developed a coupled knowledge-based prototype system (QUALICON) to assess the correctness of recording and stimulating characteristics in routine conduction studies. QUALICON extracts numeric features from CMAPs or SNAPs, which are translated into symbolic form to drive a Bayesian network. The network uses high-level knowledge to infer the quality of stimulating and recording electrode placement as well as polarity and stimulus strength making recommendations as to the likely technical error when abnormal potentials are detected. A preliminary assessment shows that QUALICON performs as well as manual assessment performed by professionals.

Action Potentials↗

PubMed1992

Model-based reasoning in biomedicine.

Significant advances have been achieved in model-based reasoning in the past decade, rendering it one of the most active branches of artificial intelligence. Research on model-based reasoning focuses on two major goals: being able to predict and explain device behavior, mostly using qualitative representations. Viewed from the same perspective, many research issues in biomedicine share the same outlook: being able to predict the behavior of a certain biological system, and being able to explain why the system behaves as it does (or will behave as it is predicted to). Qualitative modeling, with its wide margins for ambiguity, provides an excellent toolset for the simulation of biological systems which do not lend themselves easily to mathematical modeling. This article presents a review of recent advances in model-based reasoning in biomedicine.

Artificial Intelligence↗

PubMed1998

Hospitexte: towards a document-based hypertextual electronic medical record.

The patient record is a repository for knowledge about a patient. Work in Artificial Intelligence and knowledge representation has evidenced the intrinsic difficulty of formalizing knowledge for computer processing. It is therefore not a surprise that most attempts at computerizing the patient record have only had a limited degree of success or applicability. We claim that this is due to the fact that medicine is an empirical domain, and thus fundamentally resists formalization. Therefore, the only way medical knowledge can be fully expressed is through natural languages which is indeed what clinicians actually use. We proposed and designed an electronic medical record which adheres to this hypothesis and where structured documents play a prominent role.

Artificial Intelligence↗

PubMed1987

WIZARD: AI in conformational analysis.

A program which utilizes the techniques of Artificial Intelligence and Expert Systems to solve problems in the area of Conformational Analysis is described. The program searches conformational space in a systematic fashion, based on the technique known as heuristic state-space search. The program proceeds by recognizing conformational units, assigning one or more conformational templates to each unit, and joining them to form conformational suggestions. These suggestions are criticized to discover logical inconsistencies, and any resulting stresses are resolved. The resulting conformational suggestions are sometimes accurate enough for immediate use, or may be further refined by a numerical program. The latter combination is shown to be quite efficient compared to purely numerical conformational search techniques.

Artificial Intelligence↗