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PubMed2000

A neural network for 500 word vocabulary word spotting using non-uniform units.

We introduce acoustic sub-word units to neural networks for speaker-independent continuous speech recognition. The functions of segmenting input and detecting words are implemented with networks of simple structures. The non-uniform unit which we introduce in this research can model phoneme variations caused by co-articulation spread over several phonemes and between words. These units can be segmented by the network according to stationary and transition parts of speech without iteration or without considering all possible position shifts. A word lexicon can be trained by the network, which can effectively memorize all transcription variations in the training utterances of words. The results of speaker-independent word spotting of 520 words with TIMIT data are described.

Artificial Intelligence↗
PubMed2001

Classification of signal-time curves from dynamic MR mammography by neural networks.

The aim of this study was to test the performance of artificial neural networks for the classification of signal-time curves obtained from breast masses by dynamic MRI. Signal-time courses from 105 parenchyma, 162 malignant, and 102 benign tissue regions were examined. The latter two groups were histopathologically verified. Four neural networks corresponding to different temporal resolutions of the signal-time curves were tested. The resolution ranges from 28 measurements with a temporal spacing of 23s to just 3 measurements taken 1.8, 3, and 10 minutes after contrast medium administration. Discrimination between malignant and benign lesions is best if 28 measurement points are used (sensitivity: 84%, specificity: 81%). The use of three measurement points results in 78% sensitivity and 76% specificity. These results correspond to values obtained by human experts who visually evaluated signal-time curves without considering additional morphologic information. All examined networks yielded poor results for the subclassification of the benign lesions into fibroadenomas and benign proliferative changes. Neural networks can computationally fast distinguish between malignant and benign lesions even when only a few post-contrast measurements are made. More precise specification of the type of the benign lesion will require incorporation of additional morphological or pharmacokinetic information.

Adult↗
PubMed2006

Protein ranking by semi-supervised network propagation.

BACKGROUND: Biologists regularly search DNA or protein databases for sequences that share an evolutionary or functional relationship with a given query sequence. Traditional search methods, such as BLAST and PSI-BLAST, focus on detecting statistically significant pairwise sequence alignments and often miss more subtle sequence similarity. Recent work in the machine learning community has shown that exploiting the global structure of the network defined by these pairwise similarities can help detect more remote relationships than a purely local measure. METHODS: We review RankProp, a ranking algorithm that exploits the global network structure of similarity relationships among proteins in a database by performing a diffusion operation on a protein similarity network with weighted edges. The original RankProp algorithm is unsupervised. Here, we describe a semi-supervised version of the algorithm that uses labeled examples. Three possible ways of incorporating label information are considered: (i) as a validation set for model selection, (ii) to learn a new network, by choosing which transfer function to use for a given query, and (iii) to estimate edge weights, which measure the probability of inferring structural similarity. RESULTS: Benchmarked on a human-curated database of protein structures, the original RankProp algorithm provides significant improvement over local network search algorithms such as PSI-BLAST. Furthermore, we show here that labeled data can be used to learn a network without any need for estimating parameters of the transfer function, and that diffusion on this learned network produces better results than the original RankProp algorithm with a fixed network. CONCLUSION: In order to gain maximal information from a network, labeled and unlabeled data should be used to extract both local and global structure.

Algorithms↗
PubMedDate not supplied

Empirical decompositions of overall AIDS epidemics in local epidemics using generalized neural networks.

A generalized neural network was adapted for the simulation of processes strongly dependent upon the history, imposed by the inner own history of an individual neuronal activation. This involves the dependence of the neural network parameters upon the cumulated values of the corresponding neuron activations. When in the neural network weakly coupled blocks with strong inner couplings can be identified, the activation wave on the entire network (associated with the overall epidemic) can be decomposed into quasi-independent intra-block local activation waves, with characteristic delays between them (corresponding to the simultaneous and successive local epidemics). Special simulations on strongly connex neural network determine the typical local activation waves for various block parameters and the mentioned delays between two such successive activation waves in two coupled blocks. Another type of neural network is used to achieve the empirical decomposition of the overall epidemic into simultaneous (corresponding to a layer) and successive local epidemics (corresponding to the various epidemic waves, associated with different layers). A simpler approximative algorithm for the estimation of the number of the mentioned simultaneous local typical epidemics is also presented.

Acquired Immunodeficiency Syndrome↗
PubMed1995

A hybrid neural and statistical classifier system for histopathologic grading of prostatic lesions.

Neural network and statistical classification methods were applied to derive an objective grading for moderately and poorly differentiated lesions of the prostate, based on characteristics of the nuclear placement patterns. A partly trained multilayer neural network was used as a feature extractor. A hybrid classifier system using a quadratic Bayesian classifier applied to these features allowed grade assignment consensus with visual diagnosis in 96% of fields from a training set of 500 fields and in 77% of 130 fields of a test set.

Humans↗
PubMed1998

Generalization and exclusive allocation of credit in unsupervised category learning.

A new way of measuring generalization in unsupervised learning is presented. The measure is based on an exclusive allocation, or credit assignment, criterion. In a classifier that satisfies the criterion, input patterns are parsed so that the credit for each input feature is assigned exclusively to one of multiple, possibly overlapping, output categories. Such a classifier achieves context-sensitive, global representations of pattern data. Two additional constraints, sequence masking and uncertainty multiplexing, are described; these can be used to refine the measure of generalization. The generalization performance of EXIN networks, winner-take-all competitive learning networks, linear decorrelator networks, and Nigrin's SONNET-2 network are compared.

Computer Simulation↗
PubMed1998

A model of cortical associative memory based on a horizontal network of connected columns.

An attractor network model of cortical associative memory functions has been constructed and simulated. By replacing the single cell as the functional unit by multiple cells in cortical columns connected by long-range fibers, the model is improved in terms of correspondence with cortical connectivity. The connectivity is improved, since the original dense and symmetric connectivity of a standard recurrent network becomes sparse and asymmetric at the cell-to-cell level. Our simulations show that this kind of network, with model neurons of the Hodgkin-Huxley type arranged in columns, can operate as an associative memory in much the same way as previous models having simpler connectivity. The network shows attractor-like behaviour and performs the standard assembly operations despite differences in the dynamics introduced by the more detailed cell model and network structure. Furthermore, the model has become sufficiently detailed to allow evaluation against electrophysiological and anatomical observations. For instance, cell activities comply with experimental findings and reaction times are within biological and psychological ranges. By introducing a scaling model we demonstrate that a network approaching experimentally reported neuron numbers and synaptic distributions also could work like the model studied here.

Action Potentials↗
PubMed1993

Multiple disorder diagnosis with adaptive competitive neural networks.

Backpropagation neural networks have repeatedly been used for diagnostic problem-solving, but have not been demonstrated to work well when multiple disorders are present. We hypothesized that letting nodes in a backpropagation neural network compete to be part of a diagnostic solution would produce better performance than the use of existing backpropagation methods. To test this hypothesis, we derived an error backpropagation learning rule that can be used with competitive units (competitive backpropagation). Artificial neural networks were then trained using both this new learning rule and standard error backpropagation on a specific medical diagnosis problem: identification of the location of damage in the brain given a set of examination findings. Training samples included solely 'prototypical' cases where a single location of damage is present. The trained networks were then tested with atypical cases where the manifestations of more than one disorder were present or only a single manifestation was present. Networks employing competition among units were found to perform qualitatively better with these multiple-disorder cases than standard networks and also to perform better on single-manifestation cases. The reasons for this are explained. The competitive backpropagation learning rule described here provides a promising new tool for adaptive diagnostic problem-solving.

Diagnosis, Computer-Assisted↗
PubMed1997

The connectionist simulation of aphasic naming.

The simulation of language disorders using interactive activation (IA) networks and connectionist systems is discussed. An existing IA account of aphasic naming is described, in which two network parameters (decay rate and connection strength) are varied to fit the error production of an aphasic patient. Fairly similar results can be obtained through modification of additional parameters, including the so-called "shared weight increase factor" linking lexical and semantic units. This leads us to consider simulation of aphasic naming using connectionist networks which do not require explicit variation of network parameters. A modular connectionist architecture is presented, in which semantic-lexical and phonological knowledge are instantiated using self-organizing Kohonen maps, while connections between them are implemented using Hebbian networks; a linear connectionist network (Madaline) is used to simulate nonword repetition. The Hebbian connections are lesioned in order to reproduce the patient's naming errors.

Aphasia↗
PubMed2004

Massively parallel networks for edge localization and contour integration--adaptable relaxation approach.

The paper describes a new adaptive neural network for edge-based pattern extraction problems. In the model, each neuron represents an edge with continuous state variables describing its location and orientation. The post-synaptic distribution is dependent on the state variables. Each neuron adjusts its state to increase its membrane potential, which results in highly adaptive dynamics of the synaptic weight distribution. The network allocates multiple neurons with different orientation modes for each edge. The strategy allows accurate modeling of multi-modal distributions at key-points such as corners and junctions. As a result, the network delineates edges at sub-pixel accuracy while preserving key-points. It is also capable of processing a sequence of images and following moving objects. The network is extended for contour integration and key-point detection tasks. The paper presents experiments conducted on both synthetic and non-synthetic data to demonstrate the effectiveness of the technique.

Artificial Intelligence↗
PubMed2005

Prediction of inter-residue contacts map based on genetic algorithm optimized radial basis function neural network and binary input encoding scheme.

Inter-residue contacts map prediction is one of the most important intermediate steps to the protein folding problem. In this paper, we focus on the problem of protein inter-residue contacts map prediction based on neural network technique. Firstly, we use a genetic algorithm (GA) to optimize the radial basis function widths and hidden centers of a radial basis function neural network (RBFNN), then a novel binary encoding scheme is employed to train the network for the purpose of learning and predicting the inter-residue contacts patterns of protein sequences got from the protein data bank (PDB). The experimental evidence indicates the utility of our proposed encoding strategy and GA optimized RBFNN. Moreover, the simulation results demonstrate that the network got a better performance for these proteins, whose residue length falls into the area of (100, 300), and the predicted accuracy with a contact threshold of 7 Angstroms scores higher than the other 3 values with 5, 6, and 8 Angstroms.

Algorithms↗
PubMed1993

A tutorial introduction to stochastic simulation algorithms for belief networks.

Belief networks combine probabilistic knowledge with explicit information about conditional independence assumptions. A belief network consists of a directed acyclic graph in which the nodes represent variables and the edges express relationships of conditional dependence. When information about one variable's state is given to the network in the form of evidence, an update algorithm computes the posterior marginal probability distributions for the remaining variables in the network. Many algorithms for performing this inference task have been proposed. Exact algorithms report precise results for some classes of networks, but take exponential time (in the number of nodes) both in the worst case and for many interesting networks. Stochastic simulation algorithms estimate the posterior marginal probability distribution for many graph topologies that would require exponential time when using an exact algorithm. Nonetheless, for some belief networks, stochastic simulation algorithms are also known to have exponential worst case performance. This article describes at a tutorial level several stochastic simulation algorithms for belief networks, and illustrates them on some simple examples. In addition, the theoretical and empirical performance of the algorithms is briefly surveyed.

Algorithms↗
PubMed1991

Application of neural computing in pharmaceutical product development.

Neural computing technology is capable of solving problems involving complex pattern recognition. This technology is applied here to pharmaceutical product development. The most commonly used computational algorithm, the delta back-propagation network, was utilized to recognize the complex relationship between the formulation variables and the in vitro drug release parameters for a hydrophilic matrix capsule system. This new computational technique was also compared with the response surface methodology (RSM). Artificial neural network (ANN) analysis was able to predict the response values for a series of validation experiments more precisely than RSM. ANN may offer an alternative to RSM because it allows for the development of a system that can incorporate literature and experimental data to solve common problems in the pharmaceutical industry.

Algorithms↗
PubMed2025

Development and validation of a deep learning model based on cascade mask regional convolutional neural network to noninvasively and accurately identify human round spermatids.

INTRODUCTION: The difficulty of identifying human round spermatids (hRSs) has impeded applications of the human round spermatid injection (ROSI) technique. RSs can be accurately screened through flow cytometric analysis utilizing the Hoechst fluorescence profile reflecting DNA, but this method is not suitable for isolating hRSs due to the toxicity associated with Hoechst staining. OBJECTIVE: To evaluate the capacity of a deep learning model grounded in a cascade mask region-based convolutional neural network (R-CNN) for the noninvasive and accurate identification of hRSs. METHODS: In this study, we presented the development and validation of a deep learning model for identifying hRSs through the analysis of 3457 optical light microscope images of sorted hRSs obtained via flow cytometric analysis. The model's accuracy and specificity were evaluated by calculating the mean average precision (mAP). Furthermore, a double-blind experiment was conducted to access the reliability of the proposed model in accurately identifying hRSs. It detected the expression of protamine (PRM1) and/or peanut lectin (PNA), which are established markers for RSs. RESULTS: Our deep learning-based model demonstrated a high precision, achieving a mAP of over 0.80 for isolating hRSs in test datasets. The expression of PRM1 and/or PNA was observed in all cells noninvasively selected by our AI model during an independent double-blind test. This phenomenon confirmed the accuracy and effectiveness of the proposed model. The model's capability for noninvasive and accurate isolation of hRSs among spermatogenic cells highlighted its robustness and generalizability for clinical applications. CONCLUSION: The deep learning AI model based on a cascade R-CNN has the ability to accurately identify hRSs among spermatogenic cells. The application of this noninvasive method, which requires no additional procedures in clinical practice, is able to facilitate the widespread implementation of ROSI technique. Therefore, it can provide patients with spermatogenic arrest the opportunity to become biological fathers.

Humans↗
PubMed1997

Electronic nose for microbial quality classification of grains.

The odour of grains is in many countries the primary criterion of fitness for consumption. However, smelling of grain for quality grading should be avoided since inhalation of mould spores or toxins may be hazardous to the health and determinations of the off-odours are subjective. An electronic nose, i.e. a gas sensor array combined with a pattern recognition routine might serve as an alternative. We have used an electronic nose consisting of a sensor array with different types of sensors. The signal pattern from the sensors is collected by a computer and further processed by an artificial neural network (ANN) providing the pattern recognition system. Samples of oats, rye and barley with different odours and wheat with different levels of ergosterol, fungal and bacterial colony forming units (cfu) were heated in a chamber and the gas in the chamber was led over the sensory array. The ANN could predict the odour classes of good, mouldy, weakly and strongly musty oats with a high degree of accuracy. The ANN also indicated the percentage of mouldy barley or rye grains in mixtures with fresh grains. In wheat a high degree of correlation between ANN predictions and measured ergosterol as well as with fungal and bacterial cfu was observed. The electronic nose can be developed to provide a simple and fast method for quality classification of grain and is likely to find applications also in other areas of food mycology.

Avena↗
PubMed2002

Community structure in social and biological networks.

A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of community structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find community boundaries. We test our method on computer-generated and real-world graphs whose community structure is already known and find that the method detects this known structure with high sensitivity and reliability. We also apply the method to two networks whose community structure is not well known--a collaboration network and a food web--and find that it detects significant and informative community divisions in both cases.

Algorithms↗
PubMed2000

A developmental model for the evolution of artificial neural networks.

We present a model of decentralized growth and development for artificial neural networks (ANNs), inspired by developmental biology and the physiology of nervous systems. In this model, each individual artificial neuron is an autonomous unit whose behavior is determined only by the genetic information it harbors and local concentrations of substrates. The chemicals and substrates, in turn, are modeled by a simple artificial chemistry. While the system is designed to allow for the evolution of complex networks, we demonstrate the power of the artificial chemistry by analyzing engineered (handwritten) genomes that lead to the growth of simple networks with behaviors known from physiology. To evolve more complex structures, a Java-based, platform-independent, asynchronous, distributed genetic algorithm (GA) has been implemented that allows users to participate in evolutionary experiments via the World Wide Web.

Algorithms↗