3069530
HykGene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data
MOTIVATION
Recent studies have shown that microarray gene expression data are useful for phenotype classification of many diseases. A major problem in this classification is that the number of features (genes) greatly exceeds the number of instances (tissue samples). It has been shown that selecting a small set of informative genes can lead to improved classification accuracy. Many approaches have been proposed for this gene selection problem. Most of the previous gene ranking methods typically select 50-200 top-ranked genes and these genes are often highly correlated. Our goal is to select a small set of non-redundant marker genes that are most relevant for the classification task.


RESULTS
To achieve this goal, we developed a novel hybrid approach that combines gene ranking and clustering analysis. In this approach, we first applied feature filtering algorithms to select a set of top-ranked genes, and then applied hierarchical clustering on these genes to generate a dendrogram. Finally, the dendrogram was analyzed by a sweep-line algorithm and marker genes are selected by collapsing dense clusters. Empirical study using three public datasets shows that our approach is capable of selecting relatively few marker genes while offering the same or better leave-one-out cross-validation accuracy compared with approaches that use top-ranked genes directly for classification.


AVAILABILITY
The HykGene software is freely available at http://www.cs.dartmouth.edu/~wyh/software.htm


CONTACT
wyh@cs.dartmouth.edu


SUPPLEMENTARY INFORMATION
Supplementary material is available from http://www.cs.dartmouth.edu/~wyh/hykgene/supplement/index.htm.
15617422
R*-Histograms: efficient representation of spatial relations between objects of arbitrary topology
Representation of relative spatial relations between objects is often required in many multimedia database applications because spatial relations between objects in an image convey important information about the image. Quantitative representation of spatial relations taking into account shape, size, orientation and distance is often required. The R-Histogram is such a quantitative representation of spatial relations between two objects. However, this method only considers pixels on the object boundary, assuming that the objects are homeomorphic to a 2-ball. For objects with more complicated topology, we propose in this paper the R*-Histogram, a new extension to the R-Histogram. The R*-Histogram generalizes the R-Histogram by taking into account all the pixels in the objects. We also introduce an efficient O(kN log N) time algorithm to compute the R*-Histogram, which is asymptotically faster than the original O(N<sup>2</sup>) time algorithm for the R-Histogram even when k=O(n). Here, N=n<sup>2</sup> denotes the number of pixels in the processed n x n image and k is the number of different directions considered. The effectiveness of the R*-Histogram is evaluated empirically with a Query By Example (QBE) system on a database of 2000 synthetic images containing objects with complicated shape and topology. Experiments have shown that the similarly search results match human intuition very well.
12928468
Distributed goal recognition algorithms for modular robots
Modular robots are systems composed of a number of independent units that can be reconfigured to fit the task at hand. When the modules are computationally independent, they form a large distributed system with no central controller. We are concerned with the ability of such modular robots to easily recognize the achievement (or lack thereof) of a given goal configuration. We present algorithms for a class of 2D and 3D modular robots, along with correctness and running time analysis. We have successfully implemented the 2D algorithm on the second-generation Crystalline Atomic robot, a self-reconfigurable modular robot under development in our laboratory and we present implementation details and experimental results.
46267544
Neighboring Feature Clustering
In spectral datasets, such as those consisting of MR spectral data derived from MS lesions, neighboring features tend to be highly correlated, suggesting the data lie on some low-dimensional space. Naturally, finding such low-dimensional space is of interest. Based on this real-life problem, this paper extracts an abstract problem, neighboring feature clustering (NFC). Noticeably different from traditional clustering schemes where the order of features doesn't matter, NFC requires that a cluster consist of neighboring features, that is features that are adjacent in the original feature ordering. NFC is then reduced to a piece-wise linear approximation problem. We use minimum description length (MDL) method to solve this reduced problem. The algorithm we proposed works well on synthetic datasets. NFC is an abstract problem. With minor changes, it can be applied to other fields where the problem of finding piece-wise neighboring groupings in a set of unlabeled data arises.
27423282
Visualization of DNA/RNA Structure using Temporal CGRs
We present a new DNA/RNA sequence visualization technique, Temporal Chaos Game Representation (TCGR), which is an extension of Frequency Chaos Game Representation (FCGR). Unlike earlier CGR variants, TCGR illustrates the distribution of nucleotide sub-patterns at different positions within a sequence, and it can be applied to sets of sequences as well as individual sequences. TCGRs are suitable for short or long sequences. Preliminary experiments examining patterns of nucleotide occurrences in micro-RNA data illustrate not only the informative nature of the images, but clearly indicate the variability of distributions based on position, and the similarity and differences of distributions across various species
8343773
A spatio-temporal multi-modal data management and analysis environment for tracking MS lesions
We describe the development of a system that automates data collection, metadata extraction and analysis of spatio-temporal multi-modal data, combining data management and data analysis to provide an efficient resource for clinicians. Though the system is extensible to many applications, the current focus is on managing Multiple Sclerosis (MS) lesion data, which are disparate streams of image, numeric, and text data. In order to discover patterns of MS pathology and plan early and effective treatment, multispectral magnetic resonance (MR) image streams collected over time need to be correlated efficiently with each other and with patient performance and clinical data streams.
1861851
Isolated regions in video coding
Different types of prediction are applied in modern video coding. While predictive coding improves compression efficiency, the propagation of transmission errors becomes more likely. In addition, predictive coding brings difficulties to other aspects of video coding, including random access, parallel processing, and scalability. In order to combat the negative effects, video coding schemes introduce mechanisms such as slices and intracoding, to limit and break the prediction. This paper proposes the use of the isolated regions coding tool that jointly limits in-picture prediction and interprediction on a region-of-interest basis. The tool can be used to provide random access points from non-intrapictures and to respond to intrapicture update requests. Furthermore, it can be applied as an error-robust macroblock mode decision method and can be used in combination with unequal error protection. Finally, it enables mixing of scenes, which is useful in coding of masked scene transitions.
378133
LFM-Pro: a tool for detecting significant local structural sites in proteins
MOTIVATION
The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features.


RESULTS
We propose Local Feature Mining in Proteins (LFM-Pro) as a framework for automatically discovering family-specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and biochemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on trypsin-like serine proteases family of proteins and on a challenging classification dataset via comparison with DALI. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features.


AVAILABILITY
The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro.
1536242
Substitution Delone Sets 

Abstract. Substitution Delone set families are families of Delone sets X =(X1, . . ., Xn) which satisfy the inflation functional equation
 X_i = \bigvee_{j=1}^m ( \mbox{\phvr A} (X_j) + {\cal D}_$ij$ ) , \qquad 1 \le i \le m, 
 in which A is an expanding matrix, i.e., all of the eigenvalues of A fall outside the unit circle. Here the Dij are finite sets of vectors in Rd and V denotes union that counts multiplicity. This paper characterizes families X=(X1, . . ., Xn) that satisfy an inflation functional equation, in which each Xi is a multiset (set with multiplicity) whose underlying set is discrete. It then studies the subclass of Delone set solutions, and gives necessary conditions on the coefficients of the inflation functional equation for such solutions X to exist. It relates Delone set solutions to a narrower subclass of solutions, called self-replicating multi-tiling sets, which arise as tiling sets for self-replicating multi-tilings.

13902697
A Novel Rule Ordering Approach in Classification Association Rule Mining
A Classification Association Rule (CAR), a common type of mined knowledge in Data Mining, describes an implicative co-occurring relationship between a set of binary-valued data-attributes (items) and a pre-defined class, expressed in the form of an "antecedent $\Rightarrow$ (consequent-class" rule. Classification Association Rule Mining (CARM) is a recent Classification Rule Mining (CRM) approach that builds an Association Rule Mining (ARM) based classifier using CARs. Regardless of which particular methodology is used to build it, a classifier is usually presented as an ordered CAR list, based on an applied rule ordering strategy. Five existing rule ordering mechanisms can be identified: (1) Confi-dence-Support-size_of_Antecedent (CSA), (2) size_of_Antecedent-Confidence-Support (ACS), (3) Weighted Relative Accuracy (WRA), (4) Laplace Accuracy, and (5) (i¾?2Testing. In this paper, we divide the above mechanisms into two groups: (i) pure "support-confidence" framework like, and (ii) additive score assigning like. We consequently propose a hybrid rule ordering approach by combining one approach taken from (i) and another approach taken from (ii). The experimental results show that the proposed rule ordering approach performs well with respect to the accuracy of classification.
3044680
On autonomous computing and cognitive processes
This paper explores the approaches to implement intelligent behaviors by biological organisms, silicon automata, and computing systems. Autonomous computing is introduced as the latest and advanced computing techniques built upon routine, algorithmic, and adaptive systems. The theory and philosophy behind autonomous computing is cognitive informatics. In other words, autonomous computing systems are applications of cognitive informatics. A layered reference model of the brain (LRMB) and its cognitive mechanisms and processes are described in this talk, which form the foundation for designing and implementing autonomous computing systems. Real-time process algebra (RTPA) is introduced to formally and rigorously describe autonomous computing systems and cognitive behaviors. It is believed that applications of cognitive informatics and autonomous computing result in the development of new generation computing architectures and information processing systems.