RESUMEN
An artificial neural network (ANN) solution is described for the recognition of domains in protein sequences. A query sequence is first compared to a reference database of domain sequences by use of and the output data, encoded in the form of six parameters, are forwarded to feed-forward artificial neural networks with six input and six hidden units with sigmoidal transfer function. The recognition is based on the distribution of scores precomputed for the known domain groups in a database versus database comparison. Applications to the prediction of function are discussed.
Asunto(s)
Biología Computacional/métodos , Redes Neurales de la Computación , Estructura Terciaria de Proteína/fisiología , Análisis de Secuencia de Proteína/métodos , Bases de Datos FactualesRESUMEN
A functional model of the basal ganglia-thalamocortical (BTC) loops is described. In our modeling effort, we try to minimize the complexity of our starting hypotheses. For that reason, we call this type of modeling Ockham's razor modeling. We have the additional constraint that the starting assumptions should not contradict experimental findings about the brain. First assumption: The brain lacks direct representation of paths but represents directions (called speed fields in control theory). Then control should be concerned with speed-field tracking (SFT). Second assumption: Control signals are delivered upon differencing in competing parallel channels of the BTC loops. This is modeled by extending SFT with differencing that gives rise to the robust Static and Dynamic State (SDS) feedback-controlling scheme. Third assumption: Control signals are expressed in terms of a gelatinous medium surrounding the limbs. This is modeled by expressing parameters of motion in parameters of the external space. We show that corollaries of the model fit properties of the BTC loops. The SDS provides proper identification of motion related neuronal groups of the putamen. Local minima arise during the controlling process that works in external space. The model explains the presence of parallel channels as the means to avoiding such local minima. Stability conditions of the SDS predict that the initial phase of learning is mostly concerned with selection of sign for the inverse dynamics. The model provides a scalable controller. State description in external space instead of configurational space reduces the dimensionality problem. Falsifying experiment is suggested. Computer experiments demonstrate the feasibility of the approach. We argue that the resulting scheme has a straightforward connectionist representation exhibiting population coding and Hebbian learning properties.
Asunto(s)
Ganglios Basales/fisiología , Corteza Cerebral/fisiología , Aprendizaje/fisiología , Modelos Neurológicos , Tálamo/fisiología , Vías Nerviosas/fisiología , Neuronas/fisiologíaRESUMEN
Reinforcement learning is the problem of generating optimal behavior in a sequential decision-making environment given the opportunity of interacting with it. Many algorithms for solving reinforcement-learning problems work by computing improved estimates of the optimal value function. We extend prior analyses of reinforcement-learning algorithms and present a powerful new theorem that can provide a unified analysis of such value-function-based reinforcement-learning algorithms. The usefulness of the theorem lies in how it allows the convergence of a complex asynchronous reinforcement-learning algorithm to be proved by verifying that a simpler synchronous algorithm converges. We illustrate the application of the theorem by analyzing the convergence of Q-learning, model-based reinforcement learning, Q-learning with multistate updates, Q-learning for Markov games, and risk-sensitive reinforcement learning.
Asunto(s)
Algoritmos , Inteligencia Artificial , Aprendizaje , Refuerzo en PsicologíaRESUMEN
We investigated a method for a fully automatic identification and interpretation process for clustered microcalcifications in mammograms. Mammographic films of 100 patients containing microcalcifications with known histology were digitized and preprocessed using standard techniques. Microcalcifications detected by an artificial neural network (ANN) were clustered and some cluster features served as the input of another ANN trained to differentiate between typical and atypical clusters, while others were fed into an ANN trained on typical clusters to evaluate these lesions. The measured sensitivity for the detection of grouped microcalcifications was 0.98. For the task of differentiation between typical and atypical clusters an Az value of 0.87 was computed, while for the diagnosis an Az value of 0.87 with a sensitivity of 0.97 and a specificity of 0.47 was obtained. The results show that a fully automatic computer system was developed for the identification and interpretation of clustered microcalcitications in mammograms with the ability to differentiate most benign lesions from malignant ones in an automatically selected subset of cases.
Asunto(s)
Neoplasias de la Mama/diagnóstico por imagen , Calcinosis/diagnóstico por imagen , Diagnóstico por Computador , Mamografía/métodos , Enfermedades de la Mama/diagnóstico por imagen , Errores Diagnósticos , Estudios de Evaluación como Asunto , Femenino , Humanos , Mamografía/estadística & datos numéricos , Redes Neurales de la Computación , Diseño de SoftwareRESUMEN
The sixth release of the SBASE protein domain library sequences contains 130 703 annotated and crossreferenced entries corresponding to structural, functional, ligand-binding and topogenic segments of proteins. The entries were grouped based on standard names (2312 groups) and futher classified on the basis of the BLAST similarity (2463 clusters). Automated searching with BLAST and a new sequence-plot representation of local domain similarities are available at the WWW-server http://www.icgeb.trieste.it/sbase. A mirror site is at http://sbase.abc.hu/sbase. The database is freely available by anonymous 'ftp' file transfer from ftp.icgeb.trieste.it
Asunto(s)
Secuencia de Aminoácidos , Bases de Datos Factuales , Conformación Proteica , Proteínas/química , Almacenamiento y Recuperación de la Información , Internet , Proteínas/clasificación , Proteínas/fisiología , Homología de Secuencia de AminoácidoRESUMEN
A fully self-organizing neural network approach to low-dimensional control problems is described. We consider the problem of learning to control an object and solving the path planning problem at the same time. Control is based on the path planning model that follows the gradient of the stationary solution of a diffusion process working in the state space. Previous works are extended by introducing a self-organizing multigrid-like discretizing structure to represent the external world. Diffusion is simulated within a recurrent neural network built on this multigrid system. The novelty of the approach is that the diffusion on the multigrid is fast. Moreover, the diffusion process on the multigrid fits well the requirements of the path planning: it accelerates the diffusion in large free space regions while still keeps the resolution in small bottleneck-like labyrinths along the path. Control is achieved in the usual way: associative learning identifies the inverse dynamics of the system in a direct fashion. To this end there are introduced interneurons between neighboring discretizing units that detect the strength of the steady-state diffusion and forward control commands to the control neurons via modifiable connections. This architecture forms the Multigrid Position-and-Direction-to-Action (MPDA) map. The architecture integrates reactive path planning and continuous motion control. It is also shown that the scheme leads to population coding for the actual command vector.