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PubMed1997

Artificial evolution.

Most of us know about specific biotechnologies but may be less aware of the underlying process. This essay analyzes that process and speculates on its meaning. It introduces the term artificial evolution, here defined as the controlled micromanipulation of genetic information from one generation to the next, where the first variational step is engineered and the second selection step is insured by humankind. This is qualitatively different from natural evolution. The characteristics of this artificial mode of evolution are immediacy, as opposed to Darwin's law of gradualism, transclass descent, unlike Darwin's common descent, identity, as opposed to variety, and an artificial rate of mutational change, as opposed to a natural one. It constitutes evolution out of evolution, and redoubles our ethical responsibility for the future.

Animals↗

PubMed1990

Integrated expert systems and videodisc in surgical pathology: an overview.

We present an overview of our 6-year experience in the design of expert systems for anatomic pathology. Our practical goal is to help practicing pathologists with learning, teaching, and the task of diagnosis by providing them with dynamic expert knowledge by means of a personal computer. This project could only be undertaken by first addressing a scientific goal: to characterize the problem-solving strategies that expert pathologists use in making a diagnosis and to state them in the logical terms of computer science. Our approach has been to build systems first for experimentation and then for use. The result of our work is an integrated computer-based approach that handles expert knowledge as formal relationships and morphologic images and that uses a number of logical strategies to provide multiple perspectives on diagnostic tasks. Configured as a pathologist's workstation, this approach can be expected to enhance the performance of trained general pathologists and pathologists in training. Lymph node pathology has been used as the prototype domain for this research, but care has been taken to seek a generalized authoring and inference structure that can be applied to other areas of pathology by changing the contents but not the structure itself. Excursions into various surgical pathology specialties suggest that the ways the system is constructed and exercised is fundamentally robust. Such computer-based expert systems can be expected to generate a new standard in the practice of pathology--based on the "gold standard" of classical morphology, but including the coordinated use of new methods from immunology and molecular biology in a multidisciplinary approach to diagnosis when these techniques are relevant. The benefits from this technology can be expected to be widespread with the evolution, refinement, and diffusion of these systems.

Artificial Intelligence↗

PubMed2006

Extraction and use of linguistic patterns for modelling medical guidelines.

OBJECTIVE: The quality of knowledge updates in evidence-based medical guidelines can be improved and the effort spent for updating can be reduced if the knowledge underlying the guideline text is explicitly modelled using the so-called linguistic guideline patterns, mappings between a text fragment and a formal representation of its corresponding medical knowledge. METHODS AND MATERIAL: Ontology-driven extraction of linguistic patterns is a method to automatically reconstruct the control knowledge captured in guidelines, which facilitates a more effective modelling and authoring of medical guidelines. We illustrate by examples the use of this method for generating and instantiating linguistic patterns in the text of a guideline for treatment of breast cancer, and evaluate the usefulness of these patterns in the modelling of this guideline. RESULTS: We developed a methodology for extracting and using linguistic patterns in guideline formalization, to aid the human modellers in guideline formalization and reduce the human modelling effort. Using automatic transformation rules for simple linguistic patterns, a good recall (between 72% and 80%) is obtained in selecting the procedural knowledge relevant for the guideline model, even though the precision of the guideline model generated automatically covers only between 20% and 35% of the human-generated guideline model. These results indicate the suitability of our method as a pre-processing step in medical guideline formalization. CONCLUSIONS: Modelling and authoring of medical texts can benefit from our proposed method. As pre-requisites for generating automatically a skeleton of the guideline model from the procedural part of the guideline text, to aid the human modeller, the medical terminology used by the guideline must have a good overlap with existing medical thesauri and its procedural knowledge must obey linguistic regularities that can be mapped into the control constructs of the target guideline modelling language.

Artificial Intelligence↗

PubMed1999

A memory-based self-generated basis function neural network.

The paper presents a novel memory-based Self-Generated Basis Function Neural Network (SGBFN) that is composed of small CMACs. The SGBFN requires much smaller memory space than the conventional CMAC and has an excellent learning convergence property compared to multilayer neural networks. Each CMAC in the new structure takes a subset of problem inputs as its inputs. Several CMACs that have different subsets of inputs form a submodule and a group of submodules form a neural network. The output of a submodule is the product of its CMACs' outputs. Each submodule implements a self-generated basis function, which is developed during the learning. The output of the neural network is the sum of the outputs from the submodules. Using only a subset of inputs in each CMAC significantly reduces the required memory space in high-dimensional modeling. With the same size of memory, the new structure is able to achieve a much smaller learning error compared to the conventional CMAC.

Artificial Intelligence↗

PubMed1998

Virtual reality in neuroscience: a survey.

Some research projects have begun to test the possibility of using Virtual Environments (VEs) for research in neuroscience, neurosurgery and for the study and rehabilitation of human cognitive and functional activities. In fact, VEs let users navigate and interact with computer-generated 3-D environments in real time, allowing for the control of complex stimulus presentations. VEs enable the neuroscientist to present a wide variety of controlled stimuli and to measure and monitor a wide variety of responses made by the user. This paper highlights recent and ongoing research related to the applications of VEs in the neuroscience arena. In particular it focuses on the European and US applications in this field.

Artificial Intelligence↗

PubMed1996

Prism: a new approach to radiotherapy planning software.

PURPOSE: We describe the capabilities and performance of Prism, an innovative new radiotherapy planning system with unusual features and design. The design and implementation strategies are intended to assure high quality and clinical acceptability. The features include Artificial Intelligence tools and special support for multileaf collimator (MLC) systems. The design provides unusual flexibility of operation and ease of expansion. METHODS AND MATERIALS: We have implemented Prism, a three-dimensional (3D) radiotherapy treatment-planning system on standard commercial workstations with the widely available X window system. The design and implementation use ideas taken from recent software engineering research, for example, the use of behavioral entity-relationship modeling and the "Mediator Method" instead of ad-hoc programming. The Prism system includes the usual features of a 3D planning system, including Beam's Eye View and the ability to simulate any treatment geometry possible with any standard radiotherapy accelerator. It includes a rule-based expert system for automated generation of the planning target volume as defined in ICRU Report 50. In addition, it provides special support for planning treatments with a multileaf collimator (MLC). We also implemented a Radiotherapy Treatment Planning Tools Foundation for Prism, so that we are able to use software tools form other institutions without any source code modification. RESULTS: The Prism system has been in clinical operation at the University of Washington since July 1994 and has been installed at several other clinics. The system is run simultaneously by several users, each with their own workstation operating from a common networked database and software. In addition to the dosimetrists, the system is used by radiation oncologists to define tumor and target volumes and by radiation therapists to select treatment setups to load into a computer controlled accelerator. CONCLUSIONS: Experience with the installation and operation has shown the design to be effective as both a clinical and research tool. Integration of software tools has eased the development and significantly enhanced the clinical usability of the system. The design has been shown to be a sound basis for further innovation in radiation treatment planning software and for research in the treatment planning process.

Computer Communication Networks↗

PubMed1997

Design and validation of an intelligent patient monitoring and alarm system based on a fuzzy logic process model.

The process of patient care performed by an anaesthesiologist during high invasive surgery requires fundamental knowledge of the physiologic processes and a long standing experience in patient management to cope with the inter-individual variability of the patients. Biomedical engineering research improves the patient monitoring task by providing technical devices to measure a large number of a patient's vital parameters. These measurements improve the safety of the patient during the surgical procedure, because pathological states can be recognised earlier, but may also lead to an increased cognitive load of the physician. In order to reduce cognitive strain and to support intra-operative monitoring for the anaesthesiologist an intelligent patient monitoring and alarm system has been proposed and implemented which evaluates a patient's haemodynamic state on the basis of a current vital parameter constellation with a knowledge-based approach. In this paper general design aspects and evaluation of the intelligent patient monitoring and alarm system in the operating theatre are described. The validation of the inference engine of the intelligent patient monitoring and alarm system was performed in two steps. Firstly, the knowledge base was validated with real patient data which was acquired online in the operating theatre. Secondly, a research prototype of the whole system was implemented in the operating theatre. In the first step, the anaesthetists were asked to enter a state variable evaluation before a drug application or any other intervention on the patient into a recording system. These state variable evaluations were compared to those generated by the intelligent alarm system on the same vital parameter constellations. Altogether 641 state variable evaluations were entered by six different physicians. In total, the sensitivity of alarm recognition is 99.3%, the specificity is 66% and the predictability is 45%. The second step was performed using a research prototype of the system in anaesthesiological routine. The evaluation of 684 events yielded a sensitivity, specificity and predictability of the alarm recognition of more than 99%.

Anesthesiology↗

PubMed2000

Evaluation of DIABNET, a decision support system for therapy planning in gestational diabetes.

DIABNET is a knowledge-based system designed to aid doctors with therapy planning in gestational diabetes. The system core is a qualitative model, implemented by a Causal Probabilistic Network, that is able to detect the insulin effectiveness on a daily basis. DIABNET analyses monitoring data and proposes quantitative changes in insulin therapy and qualitative diet modifications. This paper proposes an evaluation methodology to assess the system performance when working in a real scenario. The methodology manages the absence of a gold standard and includes: a subjective analysis based on questionnaires and an objective analysis based on a quantitative comparison of the system's and experts' proposals. The paper also shows the results of two experiments in which expert diabetologists evaluated the therapeutical advice provided by DIABNET during the follow up of 9 patients with gestational diabetes. DIABNET detected the need of a therapy modification in 92% of the cases showing its appropriateness for automatic alarm generation. Around 80% of the proposals were accepted by experts. The evaluation results are encouraging and allow characterisation of the system's performance when proposing therapy modifications. Evaluation in its turn helps to refine the knowledge managed by DIABNET and enables us to look towards the further clinical use of DIABNET as a decision tool in gestational diabetes integrated in a telemedicine service.

Artificial Intelligence↗

PubMed1996

Qualitative process modeling of cell-cell-pathogen interactions in the immune system.

We present a general cell behavior modelling system for immune functions, based on qualitative process theory. Model system heuristics integrate known characteristics and processes of cells, pathogens, antibodies, antigens, and cytokines. The system generates qualitative simulations of primary immune responses to bacterial and viral infections. Model heuristics can be mapped to a free-running cellular automata-like simulation that demonstrates quantitative behaviors of actor populations. This system serves as a tool for testing assumptions about cellular functions in immunity, as well the basis for a qualitative discovery system that may infer new process heuristics based on evaluation of experimental data.

Artificial Intelligence↗

PubMedDate not supplied

Integrating model-based decision support in a multi-modal reasoning system for managing type 1 diabetic patients.

We present a multi-modal reasoning (MMR) methodology that integrates case-based reasoning (CBR), rule-based reasoning (RBR) and model-based reasoning (MBR), meant to provide physicians with a reliable decision support tool in the context of type 1 diabetes mellitus management. In particular, we have implemented a decision support system that is able to jointly exploit a probabilistic model of the glucose-insulin system at the steady state, a RBR system for suggestion generation and a CBR system for patient's profiling. The integration of the CBR, RBR and MBR paradigms allows for an optimized exploitation of all the available information, and for the definition of a therapy properly tailored to the patient's needs, overcoming the single approaches limitations. The system has been tested both on simulated and on real patients' data.

Artificial Intelligence↗

PubMed1995

A computer system for analysis and integrated description of regulation of the molecular-genetic system of interferon induction and action.

A new theoretical approach to elaboration of an information-analytical integrated knowledge base containing data on regulation and function of biological systems is presented. The knowledge base incorporates: (i) a reference database containing experimental data on the structural-functional organization of a biological system; (ii) a dynamic mathematical model for analysis of the evolution of the system over time; and (iii) an interpretation module of simulation results. Application of this approach to theoretical investigation of the interferon system in the case of viral infection is discussed. The approach is specific in that it uses mathematical modeling technology, which allows one to generate mathematical models of different degrees of complexity in the analysis of the diverse aspects of biological system behavior. This approach allows one not only to store and to treat available experimental data, but also to acquire new knowledge about the behavior of a biological system. The proposed approach is implemented as a computer system for the IBM PC and compatibles.

Algorithms↗

PubMed2000

Staging of cervical cancer with soft computing.

This paper describes a way of designing a hybrid decision support system in soft computing paradigm for detecting the different stages of cervical cancer. Hybridization includes the evolution of knowledge-based subnetwork modules with genetic algorithms (GA's) using rough set theory and the Interactive Dichotomizer 3 (ID3) algorithm. Crude subnetworks obtained via rough set theory and the ID3 algorithm are evolved using GA's. The evolution uses a restricted mutation operator which utilizes the knowledge of the modular structure, already generated, for faster convergence. The GA tunes the network weights and structure simultaneously. The aforesaid integration enhances the performance in terms of classification score, network size and training time, as compared to the conventional multilayer perceptron. This methodology also helps in imposing a structure on the weights, which results in a network more suitable for extraction of logical rules and human interpretation of the inferencing procedure.

Algorithms↗

PubMedDate not supplied

Predicting molecular formulas of fragment ions with isotope patterns in tandem mass spectra.

A number of different approaches have been proposed to predict elemental component formulas (or molecular formulas) of molecular ions in low and medium resolution mass spectra. Most of them rely on isotope patterns, enumerate all possible formulas for an ion, and exclude certain formulas violating chemical constraints. However, these methods cannot be well generalized to the component prediction of fragment ions in tandem mass spectra. In this paper, a new method, FFP (Fragment ion Formula Prediction), is presented to predict elemental component formulas of fragment ions. In the FFP method, the prediction of the best formulas is converted into the minimization of the distance between theoretical and observed isotope patterns. And, then, a novel local search model is proposed to generate a set of candidate formulas efficiently. After the search, FFP applies a new multiconstraint filtering to exclude as many invalid and improbable formulas as possible. FFP is experimentally compared with the previous enumeration methods, and shown to outperform them significantly. The results of this paper can help to improve the reliability of de novo in the identification of peptide sequences.

Algorithms↗

PubMed2006

Artificial consciousness, artificial emotions, and autonomous robots.

Nowadays for robots, the notion of behavior is reduced to a simple factual concept at the level of the movements. On another hand, consciousness is a very cultural concept, founding the main property of human beings, according to themselves. We propose to develop a computable transposition of the consciousness concepts into artificial brains, able to express emotions and consciousness facts. The production of such artificial brains allows the intentional and really adaptive behavior for the autonomous robots. Such a system managing the robot's behavior will be made of two parts: the first one computes and generates, in a constructivist manner, a representation for the robot moving in its environment, and using symbols and concepts. The other part achieves the representation of the previous one using morphologies in a dynamic geometrical way. The robot's body will be seen for itself as the morphologic apprehension of its material substrata. The model goes strictly by the notion of massive multi-agent's organizations with a morphologic control.

Adaptation, Psychological↗

PubMed2003

Screening of high production volume chemicals for estrogen receptor binding activity (II) by the MultiCASE expert system.

A structurally and functionally diverse and cross-validated quantitative structure-activity knowledge database generated by the MultiCASE expert system was used to screen 2526 high production volume chemicals (HPVCs) for their estrogen receptor binding activity. 73 HPVCs were found to contain structural features or biophores that have been documented as having the ability to bind to the estrogen receptor. Potential chemicals were ranked according to their quantitatively predicted ER binding potential and the details of the biophores found in them are discussed.

Animals↗

PubMed2003

Learning rule-based models of biological process from gene expression time profiles using gene ontology.

MOTIVATION: Microarray technology enables large-scale inference of the participation of genes in biological process from similar expression profiles. Our aim is to induce classificatory models from expression data and biological knowledge that can automatically associate genes with novel hypotheses of biological process. RESULTS: We report a systematic supervised learning approach to predicting biological process from time series of gene expression data and biological knowledge. Biological knowledge is expressed using gene ontology and this knowledge is associated with discriminatory expression-based features to form minimal decision rules. The resulting rule model is first evaluated on genes coding for proteins with known biological process roles using cross validation. Then it is used to generate hypotheses for genes for which no knowledge of participation in biological process could be found. The theoretical foundation for the methodology based on rough sets is outlined in the paper, and its practical application demonstrated on a data set previously published by Cho et al. (Nat. Genet., 27, 48-54, 2001). AVAILABILITY: The Rosetta system is available at http://www.idi.ntnu.no/~aleks/rosetta. SUPPLEMENTARY INFORMATION: http://www.lcb.uu.se/~hvidsten/bioinf_cho/

Algorithms↗

PubMed2000

Mapping of protein surface cavities and prediction of enzyme class by a self-organizing neural network.

An automated computer-based method for mapping of protein surface cavities was developed and applied to a set of 176 metalloproteinases containing zinc cations in their active sites. With very few exceptions, the cavity search routine detected the active site among the five largest cavities and produced reasonable active site surfaces. Cavities were described by means of solvent-accessible surface patches. For a given protein, these patches were calculated in three steps: (i) definition of cavity atoms forming surface cavities by a grid-based technique; (ii) generation of solvent accessible surfaces; (iii) assignment of an accessibility value and a generalized atom type to each surface point. Topological correlation vectors were generated from the set of surface points forming the cavities, and projected onto the plane by a self-organizing network. The resulting map of 865 enzyme cavities displays clusters of active sites that are clearly separated from the other cavities. It is demonstrated that both fully automated recognition of active sites, and prediction of enzyme class can be performed for novel protein structures at high accuracy.

Algorithms↗

PubMed2005

Multiple model regression estimation.

This paper presents a new learning formulation for multiple model estimation (MME). Under this formulation, training data samples are generated by several (unknown) statistical models. Hence, most existing learning methods (for classification or regression) based on a single model formulation are no longer applicable. We describe a general framework for MME. Then we introduce a constructive support vector machine (SVM)-based methodology for multiple regression estimation. Several empirical comparisons using synthetic and real-life data sets are presented to illustrate the proposed approach for multiple model regression formulation.

Algorithms↗