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PubMed2002

Artificial neural networks and gene filtering distinguish between global gene expression profiles of Barrett's esophagus and esophageal cancer.

cDNAmicroarrays, combined with bioinformatics analyses, are becomingincreasingly used in current medical research. Existing analytic methods,particularly those that are unsupervised, often have difficulty recognizing subtle differences among predefined subgroups. In contrast, supervised methods, such as Artificial Neural Networks (ANNs), are able to recognize subtly different biological entities. We applied ANNs in a proof-of-principle study of cDNA microarray data in esophageal cancer (CA) and premalignancy. cDNA microarrays, each containing 8064 clones, were hybridized to RNAs from 22 esophageal lesions, including 14 Barrett's esophagus (BA) metaplasias and 8 esophageal carcinomas (3 squamous cell carcinomas and 5 adenocarcinomas). Scanned cDNA microarray data were analyzed using the bioinformatics software Cluster/TreeView, Significance Analysis of Microarrays (SAM), and ANNs. Cluster analysis based on all 8064 clones on the microarrays was unable to correctly distinguish BA specimens from CA specimens. SAM then selected 160 differentially expressed genes between Barrett's and cancer. Cluster analysis based on this reduced set still misclassified 2 Barrett's as cancers. The ANN was trained on 12 samples and tested against the remaining 10 samples. Using the 160 selected genes, the ANN correctly diagnosed all 10 samples in the test set. Finally, the 160 genes selected by SAM may merit further study as biomarkers of neoplastic progression in the esophagus, as well as in elucidating pathological mechanisms underlying BA and CA.

Adenocarcinoma↗
PubMed2006

Learning phonetic categories by tracking movements.

We explore in this study how infants may derive phonetic categories from adult input that are highly variable. Neural networks in the form of self-organizing maps (SOMs; ) were used to simulate unsupervised learning of Mandarin tones. In Simulation 1, we trained the SOMs with syllable-sized continuous F(0) contours, produced by multiple speakers in connected speech, and with the corresponding velocity profiles (D1). No attempt was made to reduce the large amount of variability in the input or to add to the input any abstract features such as height and slope of the F(0) contours. In the testing phase, reasonably high categorization rate was achieved with F(0) profiles, but D1 profiles yielded almost perfect categorization of the four tones. Close inspection of the learned prototypical D1 profile clusters revealed that they had effectively eliminated surface variability and directly reflected articulatory movements toward the underlying targets of the four tones as proposed by . Additional simulations indicated that a further learning step was possible through which D1 prototypes with one-to-one correspondence to the tones were derived from the prototype clusters learned in Simulation 1. Implications of these findings for theories of language acquisition, speech perception and speech production are discussed.

Humans↗
PubMed2005

Gene expression profiling in acute myeloid leukemia.

PURPOSE OF REVIEW: This review deals with the emerging promises of gene expression profiling (GEP) and the currently accumulating knowledge about the classification and the discovery of novel disease entities in clinical acute myeloid leukemia (AML). RECENT FINDINGS: Gene expression profiling studies in AML have shown that known and novel classes of disease can be recognized by unsupervised analyses. Prognostically informative molecular signatures can be deduced. Supervised analyses show that particular clinically relevant subsets of AML can be predicted with high accuracy with minimal sets of genes. SUMMARY: The AML GEP studies published to date show a remarkable level of concordance in findings, especially for similar GEP platforms. This confirms the robustness of the methodology and the promise for future applicability of GEP in clinical diagnostics. For the time being, certain technical hurdles remain to be overcome. These relate, for instance, to the conversion of data between different GEP platforms, the effect of differences between various statistical clustering methods, and the still incomplete understanding of the effect of biologic (eg, morphology) and genetic factors on the expression signature. GEP analyses, perhaps in combination with high-throughput mutation analysis and proteomic approaches, may ultimately result in the establishment of a comprehensive diagnostic approach that will yield a key to the precise pathobiologic nature of AML.

Gene Expression Profiling↗
PubMedDate not supplied

Hierarchical self-organization of minicolumnar receptive fields.

We study self-organization of receptive fields (RFs) of cortical minicolumns. Input driven self-organization is induced by Hebbian synaptic plasticity of afferent fibers to model minicolumns based on spiking neurons and background oscillations. If input in the form of spike patterns is presented during learning, the RFs of minicolumns hierarchically specialize to increasingly small groups of similar RFs in a series of nested group subdivisions. In a number of experiments we show that the system finds clusters of similar spike patterns, that it is capable of evenly cover the input space if the input is continuously distributed, and that it extracts basic features from input consisting of superpositions of spike patterns. With a continuous version of the bars test we, furthermore, demonstrate the system's ability to evenly cover the space of extracted basic input features. The hierarchical nature and its flexibility with respect to input distinguishes the presented type of self-organization from others including similar but non-hierarchical self-organization as discussed in [Lucke J., & von der Malsburg, C. (2004). Rapid processing and unsupervised learning in a model of the cortical macrocolumn. Neural Computation 16, 501-533]. The capabilities of the presented system match crucial properties of the plasticity of cortical RFs and we suggest it as a model for their hierarchical formation.

Animals↗
PubMed2001

Segmentation of medical ultrasonic image using hybrid neural network.

Objective. To solve one of the most difficult problems in multi-dimensional reconstruction of medical ultrasonic images: image segmentation. Method. A new segmental method based on hybrid neural network was presented in this paper. The hybrid neural network comprised two phases. The first phase was Kohonens self-organization neural network, which was used to segment and label the image coarsely. The feature vectors of those pixels within a specified distance from the cluster centers were employed to train the second phase--a three-layer perception network using back-propagation (BP) technique. Then the trained BP network was used to label every pixel of the image. In the end, a post-processing stage was used to remove the small isolated points and smooth out the contours of the segmented image. Result. The segmented image had smooth continuous edges, few noises or speckles, and the contour of ventricle was clear and accurate. Conclusion. Our method could segment the ultrasonic images accurately and effectively, and had a lot of advantages compared to traditional methods. The unsupervised segmentation problems could be solved using supervised methods.

Cardiovascular Physiological Phenomena↗
PubMed2006

Global exponential stability of multitime scale competitive neural networks with nonsmooth functions.

In this paper, we study the global exponential stability of a multitime scale competitive neural network model with nonsmooth functions, which models a literally inhibited neural network with unsupervised Hebbian learning. The network has two types of state variables, one corresponds to the fast neural activity and another to the slow unsupervised modification of connection weights. Based on the nonsmooth analysis techniques, we prove the existence and uniqueness of equilibrium for the system and establish some new theoretical conditions ensuring global exponential stability of the unique equilibrium of the neural network. Numerical simulations are conducted to illustrate the effectiveness of the derived conditions in characterizing stability regions of the neural network.

Algorithms↗
PubMed1997

Medical image analysis with fuzzy models.

This paper updates several recent surveys on the use of fuzzy models for segmentation and edge detection in medical image data. Our survey is divided into methods based on supervised and unsupervised learning (that is, on whether there are or are not labelled data available for supervising the computations), and is organized first and foremost by groups (that we know of!) that are active in this area. Our review is aimed more towards 'who is doing it' rather than 'how good it is'. This is partially dictated by the fact that direct comparisons of supervised and unsupervised methods is somewhat akin to comparing apples and oranges. There is a further subdivision into methods for two- and three-dimensional data and/or problems. We do not cover methods based on neural-like networks or fuzzy reasoning systems. These topics are covered in a recently published companion survey by keller et al.

Algorithms↗
PubMed2006

Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo.

MOTIVATION: Gene expression profiling is a powerful approach to identify genes that may be involved in a specific biological process on a global scale. For example, gene expression profiling of mutant animals that lack or contain an excess of certain cell types is a common way to identify genes that are important for the development and maintenance of given cell types. However, it is difficult for traditional computational methods, including unsupervised and supervised learning methods, to detect relevant genes from a large collection of expression profiles with high sensitivity and specificity. Unsupervised methods group similar gene expressions together while ignoring important prior biological knowledge. Supervised methods utilize training data from prior biological knowledge to classify gene expression. However, for many biological problems, little prior knowledge is available, which limits the prediction performance of most supervised methods. RESULTS: We present a Bayesian semi-supervised learning method, called BGEN, that improves upon supervised and unsupervised methods by both capturing relevant expression profiles and using prior biological knowledge from literature and experimental validation. Unlike currently available semi-supervised learning methods, this new method trains a kernel classifier based on labeled and unlabeled gene expression examples. The semi-supervised trained classifier can then be used to efficiently classify the remaining genes in the dataset. Moreover, we model the confidence of microarray probes and probabilistically combine multiple probe predictions into gene predictions. We apply BGEN to identify genes involved in the development of a specific cell lineage in the C. elegans embryo, and to further identify the tissues in which these genes are enriched. Compared to K-means clustering and SVM classification, BGEN achieves higher sensitivity and specificity. We confirm certain predictions by biological experiments. AVAILABILITY: The results are available at http://www.csail.mit.edu/~alanqi/projects/BGEN.html.

Algorithms↗
PubMed2007

Phylogenetic tree information aids supervised learning for predicting protein-protein interaction based on distance matrices.

BACKGROUND: Protein-protein interactions are critical for cellular functions. Recently developed computational approaches for predicting protein-protein interactions utilize co-evolutionary information of the interacting partners, e.g., correlations between distance matrices, where each matrix stores the pairwise distances between a protein and its orthologs from a group of reference genomes. RESULTS: We proposed a novel, simple method to account for some of the intra-matrix correlations in improving the prediction accuracy. Specifically, the phylogenetic species tree of the reference genomes is used as a guide tree for hierarchical clustering of the orthologous proteins. The distances between these clusters, derived from the original pairwise distance matrix using the Neighbor Joining algorithm, form intermediate distance matrices, which are then transformed and concatenated into a super phylogenetic vector. A support vector machine is trained and tested on pairs of proteins, represented as super phylogenetic vectors, whose interactions are known. The performance, measured as ROC score in cross validation experiments, shows significant improvement of our method (ROC score 0.8446) over that of using Pearson correlations (0.6587). CONCLUSION: We have shown that the phylogenetic tree can be used as a guide to extract intra-matrix correlations in the distance matrices of orthologous proteins, where these correlations are represented as intermediate distance matrices of the ancestral orthologous proteins. Both the unsupervised and supervised learning paradigms benefit from the explicit inclusion of these intermediate distance matrices, and particularly so in the latter case, which offers a better balance between sensitivity and specificity in the prediction of protein-protein interactions.

Computational Biology↗
PubMed2003

Finding unexpected patterns in microarray data.

We describe the performance of a protocol based on the sequential application of unsupervised and supervised methods to analyze microarray samples defined by a combination of factors. Correspondence analysis is used to visualize the emerging patterns of three set of novel or previously published data: photoreceptor mutants of Arabidopsis grown under different light/dark conditions, Arabidopsis exposed to different types of biotic and abiotic stress, and human acute leukemia. We find, for instance, that light has a dramatic effect on plants despite the absence of the four major photoreceptors, that bacterial-, fungal-, and viral-induced responses converge at later stages of attack, and that sample preparation procedures used in different hospitals have large effects on transcriptome patterns. We use canonical discriminant analysis to identify the genes associated with these patters and hierarchical clustering to find groups of coregulated genes that are easily visualized in a second round of correspondence analysis and ordered tables. The unconventional combination of standard descriptive multivariate methods offers a previously unrecognized tool to uncover unexpected information.

Arabidopsis↗
PubMed2005

Combined morphological-spectral unsupervised image segmentation.

The goal of segmentation is to partition an image into disjoint regions, in a manner consistent with human perception of the content. For unsupervised segmentation of general images, however, there is the competing requirement not to make prior assumptions about the scene. Here, a two-stage method for general image segmentation is proposed, which is capable of processing both textured and nontextured objects in a meaningful fashion. The first stage extracts texture features from the subbands of the dual-tree complex wavelet transform. Oriented median filtering is employed, to circumvent the problem of texture feature response at step edges in the image. From the processed feature images, a perceptual gradient function is synthesised, whose watershed transform provides an initial segmentation. The second stage of the algorithm groups together these primitive regions into meaningful objects. To achieve this, a novel spectral clustering technique is proposed, which introduces the weighted mean cut cost function for graph partitioning. The ability of the proposed algorithm to generalize across a variety of image types is demonstrated.

Algorithms↗
PubMed2004

Hierarchical community classification and assessment of aquatic ecosystems using artificial neural networks.

Benthic macroinvertebrate communities in stream ecosystems were assessed hierarchically through two-level classification methods of unsupervised learning. Two artificial neural networks were implemented in combination. Firstly, the self-organizing map (SOM) was used to reduce the dimension of community data, and secondly, the adaptive resonance theory (ART) was subsequently applied to the SOM to further classify the groups in different scales. Hierarchical grouping in community data efficiently reflected the impact of the environmental factors such as topographic conditions, levels of pollution, and sampling location and time across different scales. New community data not included in the training process were used to test the trained network model. The input data were appropriately grouped at different hierarchical levels by the trained networks, and correspondingly revealed the impact of environmental disturbances and temporal dynamics of communities. The hierarchical clusters based on a two-level classification method could be useful for assessing ecosystem quality and community variations caused by environmental disturbances.

Animals↗
PubMed2005

NDRAM: nonlinear dynamic recurrent associative memory for learning bipolar and nonbipolar correlated patterns.

This paper presents a new unsupervised attractor neural network, which, contrary to optimal linear associative memory models, is able to develop nonbipolar attractors as well as bipolar attractors. Moreover, the model is able to develop less spurious attractors and has a better recall performance under random noise than any other Hopfield type neural network. Those performances are obtained by a simple Hebbian/anti-Hebbian online learning rule that directly incorporates feedback from a specific nonlinear transmission rule. Several computer simulations show the model's distinguishing properties.

Algorithms↗
PubMed2004

Recursive unsupervised learning of finite mixture models.

There are two open problems when finite mixture densities are used to model multivariate data: the selection of the number of components and the initialization. In this paper, we propose an online (recursive) algorithm that estimates the parameters of the mixture and that simultaneously selects the number of components. The new algorithm starts with a large number of randomly initialized components. A prior is used as a bias for maximally structured models. A stochastic approximation recursive learning algorithm is proposed to search for the maximum a posteriori (MAP) solution and to discard the irrelevant components.

Algorithms↗
PubMed2004

Unsupervised learning of a finite mixture model based on the Dirichlet distribution and its application.

This paper presents an unsupervised algorithm for learning a finite mixture model from multivariate data. This mixture model is based on the Dirichlet distribution, which offers high flexibility for modeling data. The proposed approach for estimating the parameters of a Dirichlet mixture is based on the maximum likelihood (ML) and Fisher scoring methods. Experimental results are presented for the following applications: estimation of artificial histograms, summarization of image databases for efficient retrieval, and human skin color modeling and its application to skin detection in multimedia databases.

Algorithms↗
PubMed2001

Classification of rapeseed and soybean oils by use of unsupervised pattern-recognition methods and neural networks.

Unsupervised pattern-recognition methods and Kohonen neural networks have been applied to the classification of rapeseed and soybean oil samples according to their type and quality by use of chemical and physical properties (density, refractive index, saponification value, and iodine and acid numbers) and thermal properties (thermal decomposition temperatures) as variables. A multilayer feed-forward (MLF) neural network (NN) has been used to select the most important variables for accurate classification of edible oils. To accomplish this task different neural networks architectures trained by back propagation of error method, using chemical, physical, and thermal properties as inputs, were employed. The network with the best performance and the smallest root mean squared (RMS) error was chosen. The results of MLF network sensitivity analysis enabled the identification of key properties, which were again used as variables in principal components analysis (PCA), cluster analysis (CA), and in Kohonen self-organizing feature maps (SOFM) to prove their reliability.

Cluster Analysis↗
PubMed2004

A statistical model for contours in images.

In this paper, we describe a statistical model for the gradient vector field of the gray level in images validated by different experiments. Moreover, we present a global constrained Markov model for contours in images that uses this statistical model for the likelihood. Our model is amenable to an Iterative Conditional Estimation (ICE) procedure for the estimation of the parameters; our model also allows segmentation by means of the Simulated Annealing (SA) algorithm, the Iterated Conditional Modes (ICM) algorithm, or the Modes of Posterior Marginals (MPM) Monte Carlo (MC) algorithm. This yields an original unsupervised statistical method for edge-detection, with three variants. The estimation and the segmentation procedures have been tested on a total of 160 images. Those tests indicate that the model and its estimation are valid for applications that require an energy term based on the log-likelihood ratio. Besides edge-detection, our model can be used for semiautomatic extraction of contours, localization of shapes, non-photo-realistic rendering; more generally, it might be useful in various problems that require a statistical likelihood for contours.

Algorithms↗
PubMed1998

Plasticity of directional place fields in a model of rodent CA3.

We propose a computational model of the CA3 region of the rat hippocampus that is able to reproduce the available experimental data concerning the dependence of directional selectivity of the place cell discharge on the environment and on the spatial task. The main feature of our model is a continuous, unsupervised Hebbian learning dynamics of recurrent connections, which is driven by the neuronal activities imposed upon the network by the environment-dependent external input. In our simulations, the environment and the movements of the rat are chosen to mimic those commonly observed in neurophysiological experiments. The environment is represented as local views that depend on both the position and the heading direction of the rat. We hypothesize that place cells are intrinsically directional, that is, they respond to local views. We show that the synaptic dynamics in the recurrent neural network rapidly modify the discharge correlates of the place cells: Cells tend to become omnidirectional place cells in open fields, while their directionality tends to get stronger in radial-arm mazes. We also find that the synaptic learning mechanisms account for other properties of place cell activity, such as an increase in the place cell peak firing rates as well as clustering of place fields during exploration. Our model makes several experimental predictions that can be tested using current techniques.

Animals↗