13032855
Discovering Statistically Significant Clusters by Using Iterative Genetic Algorithms in Gene Expression Data
This study presents an iterative genetic algorithm (GA) approach to find significant clusters in gene expression data. Many heuristic algorithms have been developed to cluster genes on microarray data. However, their results may be dominated by some predefined criteria, such as the number of clusters and initial mean points of clusters. Moreover, most current algorithms assign each one of genes in the dataset into some cluster while some of them are irrelevant to the interested conditions. To automatically cluster informative genes together and discard the irrelevant genes, we consider the clustering problem as maximizing correlativity among genes in a cluster. An iterative GA approach is proposed to find the tightest clusters (no irrelevant genes) and the most appropriate number of clusters. The proposed approach is applied on Hepatocellular carcinoma (HCC) dataset, and 21 significant clusters derived while 336 genes are discarded. The derived clusters have the deserving characteristics of intracluster tightness and intercluster distinctness. Furthermore, our method compares with K-Means heuristics under several clustering validation criteria and performs better than K-Means.
17604511
POINT: a database for the prediction of protein-protein interactions based on the orthologous interactome
One possible path towards understanding the biological function of a target protein is through the discovery of how it interfaces within protein-protein interaction networks. The goal of this study was to create a virtual protein-protein interaction model using the concepts of orthologous conservation (or interologs) to elucidate the interacting networks of a particular target protein. POINT (the prediction of interactome database) is a functional database for the prediction of the human protein-protein interactome based on available orthologous interactome datasets. POINT integrates several publicly accessible databases, with emphasis placed on the extraction of a large quantity of mouse, fruit fly, worm and yeast protein-protein interactions datasets from the Database of Interacting Proteins (DIP), followed by conversion of them into a predicted human interactome. In addition, protein-protein interactions require both temporal synchronicity and precise spatial proximity. POINT therefore also incorporates correlated mRNA expression clusters obtained from cell cycle microarray databases and subcellular localization from Gene Ontology to further pinpoint the likelihood of biological relevance of each predicted interacting sets of protein partners.
1127387
Automatic Symbolic Traffic Scene Analysis Using Belief Networks
Automatic symbolic traffic scene analysis is essential to many areas of IVHS (Intelligent Vehicle Highway Systems). Traffic scene information can be used to optimize traffic flow during busy periods, identify stalled vehicles and accidents, and aid the decision-making of an autonomous vehicle controller. Improvements in technologies for machine vision-based surveillance and high-level symbolic reasoning have enabled us to develop a system for detailed, reliable traffic scene analysis. The machine vision component of our system employs a contour tracker and an affine motion model based on Kalman filters to extract vehicle trajectories over a sequence of traffic scene images. The symbolic reasoning component uses a dynamic belief network to make inferences about traffic events such as vehicle lane changes and stalls. In this paper, we discuss the key tasks of the vision and reasoning components as well as their integration into a working prototype.
6997209
Object Identification in a Bayesian Context
Object identification--the task of deciding that two observed objects are in fact one and the same object--is a fundamental requirement for any situated agent that reasons about individuals. Object identity, as represented by the equality operator between two terms in predicate calculus, is essentially a first-order concept. Raw sensory observations, on the other hand, are essentially propositional-- especially when formulated as evidence in standard probability theory. This paper describes patterns of reasoning that allow identity sentences to be grounded in sensory observations, thereby bridging the gap. We begin by defining a physical event space over which probabilities are defined. We then introduce an identity criterion, which selects those events that correspond to identity between observed objects. From this, we are able to compute the probability that any two objects are the same, given a stream of observations of many objects. We show that the appearance probability, which defines how an object can be expected to appear at subsequent observations given its current appearance, is a natural model for this type of reasoning. We apply the theory to the task of recognizing cars observed by cameras at widely separated sites in a freeway network, with new heuristics to handle the inevitable complexity of matching large numbers of objects and with online learning of appearance probability models. Despite extremely noisy observations, we are able to achieve high levels of performance.
30107270
ImageGrouper: a group-oriented user interface for content-based image retrieval and digital image arrangement
In content-based image retrieval (CBIR), experimental (trial-and-error) query with relevance feedback is essential for successful retrieval. Unfortunately, the traditional user interfaces are not suitable for trying different combinations of query examples. This is because first, these systems assume query examples are always added incrementally. Second, the query and the result display are done on the same workspace. Once the user removes an image from the query examples, the image may disappear from the user interface. In addition, it is difficult to combine the result of different queries. 
 
In this paper, we propose a new interface for Content-based image retrieval named ImageGrouper. ImageGrouper is a Group-Oriented user interface in that all operations are done by creating groups of images. This approach has several advantages. First, the users can interactively compare different combinations of the query examples by dragging and grouping the images on the workspace (Query-by-Group). Because the query results are displayed on another pane, the user can quickly review the results and modify the query. Combining different queries is also easy. Furthermore, the concept of “Image Groups” is also applied for annotating and organizing many images. The Annotation-by-Groups method relieves the user of tedious task of annotating textual information on a large number of images. This method realizes a hierarchical annotation of the images as well as Bulk Annotation. The Organize-by-Group method lets the users manipulate the image groups as “Photo Albums” to organize the images. Finally, the usability of the system is compared with the traditional user interfaces. By incorporating the lessons from the experiments, the usability of ImageGrouper is further improved.
14495930
Experiments with learning opening strategy in the game of go
In this paper, we present an experimental methodology and results for a machine learning approach to learning opening strategy in the game of Go, a game for which the best computer programs play only at the level of an advanced beginning human player. While the evaluation function in most computer Go programs consists of a carefully crafted combination of pattern matchers, expert rules, and selective search, we employ a neural network trained by self-play using temporal difference learning. Our focus is on the sequence of moves made at the beginning of the game. Experimental results indicate that our approach is effective for learning opening strategy, and they also identify higherlevel features of the game that improve the quality of the learned evaluation function.
15741182
Bias Term b in SVMs Again
The paper discusses and presents the use and calculation of the e x- plicit bias term b in the support vector machines (SVMs) within the Iterative Single t raining Data learning Algorithm (ISDA). The approach proposed can be used for both nonlinear classification and nonlinear regression tasks. Unlike the other iterative methods in solvin g the SVMs learning problems containing the huge data sets, such as sequential minimal optimization (SMO) and its variants that must use at least two training data pairs, the algorithms shown here use the single training data based iteration routine for so lving QP learning problem. In this way the various 2 nd order heuristics in choosing the data for an updating is avoided. This makes the proposed ISD learning method remarkably quick. The algorithm can also be thought off as an application of a classic Gauss-Seidel (GS) coordinate ascent procedure and its derivative known as the successive over-relaxation (SOR) algorithm in SVMs learning from huge data sets subject to both the box constraints and the equality ones. (The later coming from min i- mizing the primal objective function in respect to the bias term b). The final solution in a dual domain is not an approximate one, but it is the optimal set of dual variables which would have been obtained by using any of existing and proven QP problem solvers if they only could deal with huge data sets. Proceedings of 12