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At least 307 records · Page 17Linked to original sources
PubMed2005

Selection of molecular descriptors with artificial intelligence for the understanding of HIV-1 protease peptidomimetic inhibitors-activity.

Quantitative Structure Activity Relationship (QSAR) techniques are used routinely by computational chemists in drug discovery and development to analyze datasets of compounds. Quantitative numerical methods like Partial Least Squares (PLS) and Artificial Neural Networks (ANN) have been used on QSAR to establish correlations between molecular properties and bioactivity. However, ANN may be advantageous over PLS because it considers the interrelations of the modeled variables. This study focused on the HIV-1 Protease (HIV-1 Pr) inhibitors belonging to the peptidomimetic class of compounds. The main objective was to select molecular descriptors with the best predictive value for antiviral potency (Ki). PLS and ANN were used to predict Ki activity of HIV-1 Pr inhibitors and the results were compared. To address the issue of dimensionality reduction, Genetic Algorithms (GA) were used for variable selection and their performance was compared against that of ANN. Finally, the structure of the optimum ANN achieving the highest Pearson's-R coefficient was determined. On the basis of Pearson's-R, PLS and ANN were compared to determine which exhibits maximum performance. Training and validation of models was performed on 15 random split sets of the master dataset consisted of 231 compounds. For each compound 192 molecular descriptors were considered. The molecular structure and constant of inhibition (Ki) were selected from the NIAID database. Study findings suggested that non-covalent interactions such as hydrophobicity, shape and hydrogen bonding describe well the antiviral activity of the HIV-1 Pr compounds. The significance of lipophilicity and relationship to HIV-1 associated hyperlipidemia and lipodystrophy syndrome warrant further investigation.

Algorithms↗
PubMed2004

Prediction of apatite lattice constants from their constituent elemental radii and artificial intelligence methods.

Lattice constants (LCs) of all possible 96 apatite compounds, A(5)(BO(4))(3)C, constituted by A[double bond]Ba(2+), Ca(2+), Cd(2+), Pb(2+), Sr(2+), Mn(2+); B[double bond]As(5+), Cr(5+), P(5+), V(5+); and C[double bond]F(1-), Cl(1-), Br(1-), OH(1-), are predicted from their elemental ionic radii, using pattern recognition (PR) and artificial neural networks (ANN) techniques. In particular, by a PR study it is demonstrated that ionic radii predominantly govern the LCs of apatites. Furthermore, by using ANN techniques, prediction models of LCs a and c are developed, which reproduce well the measured LCs (R(2)=0.98). All the literature reported on 30 pure and 22 mixed apatite compounds are collected and used in the present work. LCs of all possible 66 new apatites (assuming they exist) are estimated by the developed ANN models. These proposed new apatites may be of interest to biomedical research especially in the design of new apatite biomaterials for bone remodeling. Similarly these techniques may also be applied in the study of interface growth behaviors involving other biomaterials.

Algorithms↗
PubMed1993

Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks).

The ability to supervise and control a highly non-linear and time variant bioprocess is of considerable importance to the biotechnological industries which are continually striving to obtain higher yields and improved uniformity of production. Two AI methodologies aimed at contributing to the overall intelligent monitoring and control of bioprocess operations are discussed. The development and application of a real-time knowledge-based system to provide supervisory control of fed-batch bioprocesses is reviewed. The system performs sensor validation, fault detection and diagnosis and incorporates relevant expertise and experience drawn from both bioprocess engineering and control engineering domains. A complementary approach, that of artificial neural networks is also addressed. The development of neural network modelling tools for use in bioprocess state estimation and inferential control are reviewed. An attractive characteristic of neural networks is that with the appropriate topology any non-linear functional relationship can be modelled, hence significantly reducing model-process mismatch. Results from industrial applications are presented.

Artificial Intelligence↗
PubMed1989

Optimizing radiologic workup: an artificial intelligence approach.

The increasing complexity of diagnostic imaging is presenting an ever expanding variety of radiologic test options to clinicians. As a result, it is becoming more difficult for referring physicians to select an appropriate sequence of tests. The current economic pressures on medicine make it particularly important that resources be used judiciously. Radiologic workup often involves a sequence of tests that lead from presenting signs and symptoms to a definitive diagnosis or intervention. This sequence ideally begins with simple, inexpensive, safe, non-invasive tests and progresses to more complex, expensive, and hazardous tests only if the simpler tests are insufficient to establish a diagnosis. DxCON is a developmental artificial intelligence-based computer system that gives advice to physicians about the optimum sequencing of radiologic tests. DxCON evaluates basic clinical information and a physician's proposed workup plan. The system then creates an analysis of the strengths and weaknesses of his plan. The domain chosen to explore computer-based workup advice is the radiologic workup of obstructive jaundice.

Cholestasis↗
PubMed2005

Sign language recognition and translation: a multidisciplined approach from the field of artificial intelligence.

In recent years, research has progressed steadily in regard to the use of computers to recognize and render sign language. This paper reviews significant projects in the field beginning with finger-spelling hands such as "Ralph" (robotics), CyberGloves (virtual reality sensors to capture isolated and continuous signs), camera-based projects such as the CopyCat interactive American Sign Language game (computer vision), and sign recognition software (Hidden Markov Modeling and neural network systems). Avatars such as "Tessa" (Text and Sign Support Assistant; three-dimensional imaging) and spoken language to sign language translation systems such as Poland's project entitled "THETOS" (Text into Sign Language Automatic Translator, which operates in Polish; natural language processing) are addressed. The application of this research to education is also explored. The "ICICLE" (Interactive Computer Identification and Correction of Language Errors) project, for example, uses intelligent computer-aided instruction to build a tutorial system for deaf or hard-of-hearing children that analyzes their English writing and makes tailored lessons and recommendations. Finally, the article considers synthesized sign, which is being added to educational material and has the potential to be developed by students themselves.

Artificial Intelligence↗
PubMed2001

Medical target prediction from genome sequence: combining different sequence analysis algorithms with expert knowledge and input from artificial intelligence approaches.

By exploiting the rapid increase in available sequence data, the definition of medically relevant protein targets has been improved by a combination of: (i) differential genome analysis (target list): and (ii) analysis of individual proteins (target analysis). Fast sequence comparisons, data mining, and genetic algorithms further promote these procedures. Mycobacterium tuberculosis proteins were chosen as applied examples.

Algorithms↗
PubMed1988

An artificial intelligence approach to the study of the structural moieties relevant to drug-receptor interactions in aldose reductase inhibitors.

The computer-automated structure evaluation program has been used to study 482 compounds relevant to the inhibition of the aldose reductase enzyme. Major activating/inactivating fragments were generated automatically. The significance of these molecular descriptors with respect to the activity of the compounds is discussed.

Aldehyde Reductase↗
PubMed1993

[Theory and practice in developing a system of artificial intelligence for recognizing histologic specimens].

A system is described for an automatic analysis and recognition of a fresh and reference histologic material. The system includes a microscope, TV camera and personal computer. The system proved applicable in identification of two tumor types: astrocytomas and oligodendrogliomas. The system can be used in pathology, histology, embryology, cytology, histochemistry, clinical laboratory microscopy and in borderline fields.

Artificial Intelligence↗
PubMed1993

Artificial intelligence approach in analysis of DNA sequences.

We present an approach for designing a knowledge-based system, called Sequence Acquisition In Context (SAIC), that will be able to cooperate with a biologist in the analysis of DNA sequences. The main task of the system is the acquisition of the expert knowledge that the biologist uses for solving ambiguities from gel autoradiograms, with the aim of re-using it later for solving similar ambiguities. The various types of expert knowledge constitute what we call the contextual knowledge of the sequence analysis. Contextual knowledge deals with the unavoidable problems that are common in the study of the living material (eg noise on data, difficulties of observations). Indeed, the analysis of DNA sequences from autoradiograms belongs to an emerging and promising area of investigation, namely reasoning with images. The SAIC project is developed in a theoretical framework that is shared with other applications. Not all tasks have the same importance in each application. We use this observation for designing an intelligent assistant system with three applications. In the SAIC project, we focus on knowledge acquisition, human-computer interaction and explanation. The project will benefit research in the two other applications. We also discuss our SAIC project in the context of large international projects that aim to re-use and share knowledge in a repository.

Artificial Intelligence↗
PubMed2001

[Evaluation of systemic activity of the brain by means of an artificial intelligence model].

The model based on the systems representation of informational brain mechanisms reproduces the principal stages of intellectual activities: afferent synthesis, decision making, acceptor of results of actions. The imitation of behavior of subjects in the special experimental environment with the model allows quantitative estimation of parameters of systemic stages in normal subjects of different ages and subjects with different brain dysfunctions, such as arteriosclerosis, schizophrenia, etc.

Adolescent↗
PubMed1989

Three-dimensional techniques and artificial intelligence in thallium-201 cardiac imaging.

Three-dimensional reconstruction techniques including bull's-eye polar-coordinate maps, surface rendering, and surface modeling have been developed that help interpreting physicians assimilate complex 3-D tomographic data. Comparison of patient data with normal files highlights myocardial perfusion abnormalities, thus facilitating their recognition. In addition, AI systems that use heuristically defined rules derived from an expert knowledge base assist inexperienced observers in drawing conclusions regarding scan abnormalities.

Artificial Intelligence↗
PubMed1993

Neuroethics vs neurophysiologically and neuropsychologically uninformed influences in child-rearing, education, emerging hunter-gatherers, and artificial intelligence models of the brain.

Potentially negative long-term consequences in four areas are emphasized, if specific neuromaturational, neurophysiological, and neuropsychological facts within a neurodevelopmental and ecological context are neglected in normal functional levels of child development and maturational lag of the frontal lobe system in "Attention Deficit Disorder," in education (reading/writing and arithmetic), in assessment of cognitive functioning in hunter-gatherer populations, specifically modified in the service of their survival, and in constructing computer models of the brain, neglecting consciousness and intentionality as criticized recently by Searle.

Artificial Intelligence↗