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PubMed1993

Structure design: an artificial intelligence-based method for the design of molecules under geometrical constraints.

This study presents an algorithm that implements artificial-intelligence techniques for automated, and site-directed drug design. The aim of the method is to link two or more predetermined functional groups into a sensible molecular structure. The proposed designing process mimics the classical manual design method, in which the drug designer sits in front of the computer screen and with the aid of computer graphics attempts to design the new drug. Therefore, the key principle of the algorithm is the parameterization of some criteria that affect the decision-making process carried out by the drug designer. This parameterization is based on the generation of weighting factors that reflect the knowledge and knowledge-based intuition of the drug designer, and thus add further rationalization to the drug design process. The proposed algorithm has been shown to yield a large variety of different structures, of which the drug designer may choose the most sensible. Performance tests indicate that with the proper set of parameters, the method generates a new structure within a short time.

Algorithms↗

PubMed2000

The Turing Test: the first 50 years.

The Turing Test, originally proposed as a simple operational definition of intelligence, has now been with us for exactly half a century. It is safe to say that no other single article in computer science, and few other articles in science in general, have generated so much discussion. The present article chronicles the comments and controversy surrounding Turing's classic article from its publication to the present. The changing perception of the Turing Test over the last 50 years has paralleled the changing attitudes in the scientific community towards artificial intelligence: from the unbridled optimism of 1960s to the current realization of the immense difficulties that still lie ahead. I conclude with the prediction that the Turing Test will remain important, not only as a landmark in the history of the development of intelligent machines, but also with real relevance to future generations of people living in a world in which the cognitive capacities of machines will be vastly greater than they are now.

Journal Article↗

PubMed1985

A program for machine learning of counting criteria: empirical induction of logic-based classification rules.

A program has been developed which derives classification rules from empirical observations and expresses these rules in a knowledge representation format called 'counting criteria'. Decision rules derived in this format are often more comprehensible than rules derived by existing machine learning programs such as AQ11. Use of the program is illustrated by the inference of discrimination criteria for certain types of bacteria based upon their biochemical characteristics. The program may be useful for the conceptual analysis of data and for the automatic generation of prototype knowledge bases for expert systems.

Artificial Intelligence↗

PubMed2000

Information visualisation in clinical medicine using 3D parallel diagrams: a case history.

An information visualisation tool was implemented and tested as a solution to the problem of visualising clinical experience derived from large amounts of formalised clinical data. The tool was based on the idea of dynamic 3D parallel diagrams with support for direct manipulation, an idea similar to the notion of 3D parallel coordinates. The tool was tested on a knowledge base containing about 1500 examinations obtained from different clinics. Clinical practice showed that the basic idea is conceptually appealing to the involved clinicians as the tool can be used for generating and testing of hypothesis.

Artificial Intelligence↗

PubMed1991

Ranking radiotherapy treatment plans using decision-analytic and heuristic techniques.

Radiotherapy treatment optimization is done by generating a set of tentative treatment plans, evaluating them and selecting the plan closest to achieving a set of conflicting treatment objectives. The evaluation of potential plans involves making tradeoffs among competing possible outcomes. Multiattribute decision theory provides a framework for specifying such tradeoffs and using them to select optimal actions. Using these concepts, we have developed a plan-ranking model which ranks a set of tentative treatment plans from best to worst. Heuristics are used to refine this model so that it reflects the clinical condition of the patient being treated and the practice preferences of the physician prescribing the treatment. A figure of merit is computed for each tentative plan, and is used to rank the plans. The approach described is very general and can be used for other medical domains having similar characteristics. The figure of merit can also be used as an objective function by computer programs that attempt to automatically generate an optimal treatment plan.

Artificial Intelligence↗

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↗

PubMed1998

Galen-In-Use: using artificial intelligence terminology tools to improve the linguistic coherence of a national coding system for surgical procedures.

GALEN has developed a language independent common reference model based on a medically oriented ontology and practical tools and techniques for managing healthcare terminology including natural language processing. GALEN-IN-USE is the current phase which applied the modelling and the tools to the development or the updating of coding systems for surgical procedures in different national coding centers co-operating within the European Federation of Coding Centre (EFCC) to create a language independent knowledge repository for multicultural Europe. We used an integrated set of artificial intelligence terminology tools named CLAssification Manager workbench to process French professional medical language rubrics into intermediate dissections and to the Grail reference ontology model representation. From this language independent concept model representation we generate controlled French natural language. The French national coding centre is then able to retrieve the initial professional rubrics with different categories of concepts, to compare the professional language proposed by expert clinicians to the French generated controlled vocabulary and to finalize the linguistic labels of the coding system in relation with the meanings of the conceptual system structure.

Artificial Intelligence↗

PubMed2001

Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach.

In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the definition of the information by the medical expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of confidence or doubt in their own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other artificial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

Adult↗

PubMed1994

A C++ framework for developing Medical Logic Modules and an Arden Syntax compiler.

When developing a clinical decision support system that uses knowledge expressed in Arden Syntax, the availability of a robust means of translating Arden Syntax into an executable module becomes critical. This paper describes an approach where Arden Syntax is translated into an intermediate pseudo-Arden language that is in turn compiled and linked to create the executable module. The pseudo-Arden language is defined in C++ using specialized class libraries and preprocessor macros. This approach provides an alternative means of developing the code generator for an Arden Syntax compiler.

Artificial Intelligence↗

PubMed1998

Building cross-thesauri with the support of UMLS.

The absence of a robust system of descriptors (cross-thesaurus) hampers the development of combinatorial terminological systems. We developed a tool (I-BROWSE) to produce a cross-thesaurus by analyzing terminological corpora. To facilitate the work of experts and to produce re-usable results, our application interacts via the Internet with the UMLS Knowledge Sources Server. We applied our tool on 2999 dissections on surgical procedures produced in the project GALEN-IN-USE, as a part of the internal Quality Assurance program. Support from UMLS seems mostly promising about descriptors on , , and . Additional assistance can be given to domain experts on less frequent descriptors on pervasive modifiers. We plan to apply our tool also to production of terminological standards in CEN, as a part of a worldwide process of gradual convergence and transformation of coding systems into second-generation systems and terminological services.

Artificial Intelligence↗

PubMed1994

Use of artificial intelligence to analyze clinical database reduces workload on surgical house staff.

BACKGROUND: The current quantity and diversity of hospital clinical, laboratory, and pharmacy records have resulted in a glut of information, which can be overwhelming to house staff. This study was performed to measure the impact of artificial intelligence analysis of such data on the junior surgical house staff's workload, time for direct patient care, and quality of life. METHODS: A personal computer was interfaced with the hospital computerized patient data system. Artificial intelligence algorithms were applied to retrieve and condense laboratory values, microbiology reports, and medication orders. Unusual laboratory tests were reported without artificial intelligence filtering. RESULTS: A survey of 23 junior house staff showed a requirement for a total of 30.75 man-hours per day, an average of 184.5 minutes per service twice a day for five surgical services each with an average of 40.7 patients, to manually produce a report in contrast to a total of 3.4 man-hours, an average of 20.5 minutes on the same basis (88.9% reduction, p < 0.001), to computer generate and distribute a similarly useful report. Two thirds of the residents reported an increased ability to perform patient care. CONCLUSIONS: Current medical practice has created an explosion of information, which is a burden for surgical house staff. Artificial intelligence preprocessing of the hospital database information focuses attention, eliminates superfluous data, and significantly reduces surgical house staff clerical work, allowing more time for education, research, and patient care.

Artificial Intelligence↗

PubMed1995

Toxicity estimation by chemical substructure analysis: the TOX II program.

The objectives of our work are to develop methodologies capable of identifying the potential environmental health hazards of chemicals. These techniques are particularly useful when it is necessary to evaluate molecules that have not been synthesized as yet, or for which there is little or no toxicological information known. With the help of MULTICASE, an artificial intelligence program capable of uncovering the relationship between the presence of specific substructures in a molecule and its toxicity, and TOX II, a program capable of identifying the existence of such substructures in a new molecule, it is now possible to predict with a reasonable degree of certainty whether a new molecule will be toxic. TOX II will uncover any functionality previously found to be related to toxicity in any organic molecule. The evaluation is extensive and may include its automatically generated metabolites. The scope of TOX II is vast as more than 70 toxicological endpoints can be evaluated.

Artificial Intelligence↗

PubMed1992

Applications of rule-induction in the derivation of quantitative structure-activity relationships.

Recently, methods have been developed in the field of Artificial Intelligence (AI), specifically in the expert systems area using rule-induction, designed to extract rules from data. We have applied these methods to the analysis of molecular series with the objective of generating rules which are predictive and reliable. The input to rule-induction consists of a number of examples with known outcomes (a training set) and the output is a tree-structured series of rules. Unlike most other analysis methods, the results of the analysis are in the form of simple statements which can be easily interpreted. These are readily applied to new data giving both a classification and a probability of correctness. Rule-induction has been applied to in-house generated and published QSAR datasets and the methodology, application and results of these analyses are discussed. The results imply that in some cases it would be advantageous to use rule-induction as a complementary technique in addition to conventional statistical and pattern-recognition methods.

Algorithms↗

PubMed2002

Clinical potential of proteomics in the diagnosis of ovarian cancer.

The need for specific and sensitive markers of ovarian cancer is critical. Finding a sensitive and specific test for its detection has an important public health impact. Currently, there are no effective screening options available for patients with ovarian cancer. CA-125, the most widely used biomarker for ovarian cancer, does not have a high positive predictive value and it is only effective when used in combination with other diagnostic tests. However, pathologic changes taking place within the ovary may be reflected in biomarker patterns in the serum. Combination of mass spectra generated by new proteomic technologies, such as surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) and artificial-intelligence-based informatic algorithms, have been used to discover a small set of key protein values and discriminate normal from ovarian cancer patients. Serum proteomic pattern analysis might be applied ultimately in medical screening clinics, as a supplement to the diagnostic work-up and evaluation.

Biomarkers, Tumor↗

PubMed2026

The Rise of Plant Pan-Genomes: From Genome Variation to Predictive Breeding.

Plant pan-genomics is entering a new phase beyond genome variation discovery, requiring a shift from cataloguing genomic diversity toward understanding how variation generates biological function and breeding value. Here, we propose that the future of plant pan-genomics will be shaped by three conceptual transitions. First, structural variation (SV), presence-absence variation (PAV), and haplotype diversity should be interpreted not merely as genomic differences, but as regulatory components that influence gene networks, chromatin organization, and complex traits. Second, the expansion from species-level pan-genomes to genus-level super pan-genomes provides an evolutionary framework for uncovering adaptive genetic modules preserved in wild relatives and overlooked during domestication. Third, integrating pan-genomes with pan-omics, three-dimensional genome analyses, and artificial intelligence will enable the transformation of genomic variation into predictive models for crop improvement. We further propose that the ultimate value of pan-genomes lies not in generating increasingly complete genome collections, but in establishing a mechanistic bridge between genome diversity, biological function, and breeding decisions. This transition will move crop improvement from empirical selection toward rational genome design, where evolutionary diversity can be systematically interpreted, predicted, and engineered.

Journal Article↗

PubMed1990

Knowledge-based prediction of protein structures.

We propose a knowledge-based approach to the prediction of protein structures in cases where there is no sequence-homology to proteins with known spatial structure. Using methods from Artificial Intelligence we attempt to take into account long-range interactions within the prediction process. This allows not only the assignment of secondary but also of supersecondary structure elements. In particular, the patterns used as conditions of prediction rules are generated by learning methods from information contained in the Protein Data Base. Patterns on higher levels of the protein structure hierarchy are used as constraints to reduce the combinatorial search space. These patterns may also be used to search for specified structure motifs by interactive retrieval.

Artificial Intelligence↗

PubMedDate not supplied

An artificial intelligence approach to DNA sequence feature recognition.

The ultimate goal of the Human Genome project is to extract the biologically relevant information recorded in the estimated 100,000 genes encoded by the 3 x 10(9) bases of the human genome. This necessitates development of reliable computer-based methods capable of analysing and correctly identifying genes in the vast amounts of DNA-sequence data generated. Such tools may save time and labour by simplifying, for example, screening of cDNA libraries. They may also facilitate the localization of human disease genes by identifying candidate genes in promising regions of anonymous DNA sequence.

Artificial Intelligence↗

PubMed1976

Towards the simulation of clinical cognition. Taking a present illness by computer.

Remarkably little is known about the cognitive processes which are employed in the solution of clinical problems. This paucity of information is probably accounted for in large part by the lack of suitable analytic tools for the study of the physician's thought processes. Here we report on the use of the computer as a laboratory for the study of clinical cognition. Our experimental approach has consisted of several elements. First, cognitive insights gained from the study of clinicians' behavior were used to develop a computer program designed to take the present illness of a patient with edema. The program was then tested with a series of prototypical cases, and the present illnesses generated by the computer were compared to those taken by the clinicians in our group. Discrepant behavior on the part of the program was taken as a stimulus for further refinement of the evolving cognitive theory of the present illness. Corresponding refinements were made in the program, and the process of testing and revision was continued until the program's behavior closely resembled that of the clinicians. The advances in computer science that made this effort possible include "goal-directed" programming, pattern-matching and a large associative memory, all of which are products of research in the field known as "artificial intelligence". The information used by the program is organized in a highly connected set of associations which is used to guide such activities as checking the validity of facts, generating and testing hypotheses, and constructing a coherent picture of the patient. As the program pursues its interrelated goals of information gathering and diagnosis, it uses knowledge of diseases and pathophysiology, as well as "common sense", to dynamically assemble many small problem-solving strategies into an integrated history-taking process. We suggest that the present experimental approach will facilitate accomplishment of the long-term goal of disseminating clinical expertise via the computer.

Computers↗