By Pierre Baldi, Gianluca Pollastri, Claus A. F. Andersen, Søren Brunak (auth.), Helge Malmgren BA, PhD, MD, Magnus Borga MSc, PhD, Lars Niklasson BSc, MSc, PhD (eds.)
This e-book includes the court cases of the convention ANNIMAB-l, held 13-16 could 2000 in Goteborg, Sweden. The convention used to be geared up through the Society for synthetic Neural Networks in drugs and Biology (ANNIMAB-S), which was once proven to advertise learn inside a brand new and really cross-disciplinary box. Forty-two contributions have been permitted for presentation; as well as those, S invited papers also are integrated. examine inside of medication and biology has frequently been characterized through software of statistical tools for comparing area particular facts. The turning out to be curiosity in man made Neural Networks has not just brought new tools for facts research, but additionally unfolded for improvement of recent versions of organic and ecological platforms. The ANNIMAB-l convention is targeting many of the many makes use of of synthetic neural networks with relevance for medication and biology, in particular: • clinical purposes of synthetic neural networks: for higher diagnoses and consequence predictions from scientific and laboratory info, within the processing of ECG and EEG indications, in clinical snapshot research, and so forth. greater than 1/2 the contributions deal with such clinically orientated concerns. • makes use of of ANNs in biology outdoors scientific medication: for instance, in versions of ecology and evolution, for information research in molecular biology, and (of direction) in versions of animal and human worried platforms and their features. • Theoretical points: fresh advancements in studying algorithms, ANNs when it comes to specialist structures and to conventional statistical systems, hybrid platforms and integrative approaches.
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Extra info for Artificial Neural Networks in Medicine and Biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13–16 May 2000
Artificial neural networks: a prospective tool for the analysis of psychiatric disorders. Journal of Psychiatry £3 Neuroscience, 21(4):239-47, 1996.  R. Dybowski and V. Gant. Artificial neural networks in pathology and medical laboratories. Lancet, 346:1203-1207, 1995. T. Wei, Z. D. R. Madyastha, H. E. Oesterling. Understanding artificial neural networks and exploring their potential applications for the practicing urologist. Urology, 52(2):161-172, 1998.  W. el Deredy. Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: A review.
These models have been very accurate at predicting stimulus-response characteristics, including generalization, supernormality (peak shift) and stimulus intensity effects. Such neural networks exhibit behavior predictive of the behavior of real consumers. Leow  describes a series of increasingly complex neural networks each of which allows a simulated creature to search for food and to evade danger by using olfactory cues. Behaviors such as obstacle avoidance and risk-taking emerge naturally from the networks' interaction with the environment.
Another example is the concept of probabilistic networks; however, unlike an MLP, the nodes of a probabilistic network are related stochastically, and inference can take place in different directions along the network. The stochastic relationships of a probabilistic network are defined by conditional probabilities at the non-terminal nodes, and for a conditional probability with a complex likelihood, an MLP may be an effective model. This idea of using MLPs at the nodes of a probabilistic networks is one approach to implementing data fusion, such as the integration of electrophysiology signals with both radiological images and patient records for decision support.
Artificial Neural Networks in Medicine and Biology: Proceedings of the ANNIMAB-1 Conference, Göteborg, Sweden, 13–16 May 2000 by Pierre Baldi, Gianluca Pollastri, Claus A. F. Andersen, Søren Brunak (auth.), Helge Malmgren BA, PhD, MD, Magnus Borga MSc, PhD, Lars Niklasson BSc, MSc, PhD (eds.)