13024009
Building effective defect-prediction models in practice
Defective software modules cause software failures, increase development and maintenance costs, and decrease customer satisfaction. Effective defect prediction models can help developers focus quality assurance activities on defect-prone modules and thus improve software quality by using resources more efficiently. These models often use static measures obtained from source code, mainly size, coupling, cohesion, inheritance, and complexity measures, which have been associated with risk factors, such as defects and changes.
17890414
Identifying significant genes from microarray data
Microarray technology is a recent development in experimental molecular biology which can produce quantitative expression measurements for thousands of genes in a single, cellular mRNA sample. These many gene expression measurements form a composite profile of the sample, which can be used to differentiate samples from different classes such as tissue types or treatments. However, for the gene expression profile data obtained in a specific comparison, most likely only some of the genes will, be differentially expressed between the classes, while many other genes have similar expression levels. Selecting a list of informative differential genes from these data is important for microarray data analysis. In this paper, we describe a framework for selecting informative genes, called ranking and combination analysis (RAC), which combines various existing informative gene selection methods. We conducted experiments using three data sets and six existing feature selection methods. The results show that the RAC framework is a robust and efficient approach to identify informative gene for microarray data. The combination approach on two selecting methods almost always performed better than the less efficient individual, and in many cases, better than both. More significantly, when considering all three data sets together, the combination approach, on average, outperforms each individual feature selection method. All of these indicate that RCA might be a viable and feasible approach for the microarray gene expression analysis.
17120587
Combination methods in microarray analysis
Microarray technology and experiment can produce thousands or tens of thousands of gene expression measurements in a single cellular mRNA sample. Selecting a list of informative differential genes from these measurement data has been the central problem for microarray analysis. Many methods to identify informative genes have been proposed in the past. However, due to the complexity of biological systems, each proposed method seems to perform nicely in a particular data set or specific experiment. It remains a great challenge to come up with a selection method for a wider spectrum of experiments and a broader variety of data sets. In this paper, we take the approach of method combination using data fusion and rank-score graph which have been used successfully in other application domains such as information retrieval, pattern recognition and tracking, and molecular similarity search. Our method combination is efficient and flexible and can be extended to become a general learning system for microarray gene expression analysis.
35256277
Generalized shuffle-exchange digraphs: Hamiltonian properties
The k-ary n-dimensional shuffle-exchange directed network S(k, n) consists of k/sup n/ nodes such that each node is represented as an k-ary n-tuple vector, a/sub 1/a/sub 2/...a/sub n/, where a/sub i/ is in [0, k-1]. Node a/sub 1/a/sub 2/...a/sub n/ is adjacent to node a/sub 2/a/sub 3/...a/sub n/a/sub 1/ (one shuffle link) and k-1 other nodes a/sub 1/a/sub 2/...a/sub n-1/b, where b/spl isin/[0, k-1] and b/spl ne/a/sub n/ (k-1 exchange links). S(k, n) have been widely used as topologies for VLSI networks, parallel architectures, and communication systems. However, since S(k, n) does not exist for the number of nodes between k/sup n/ and k/sup n+1/, Liu and Hsu have recently proposed a class of digraphs GS(k, N), which generalized S(k, n) to any number N of nodes. They also showed that GS(k, N) retains all the nice properties of S(k, n). In this paper, we survey these combinatorial properties and study Hamiltonian properties of GS(k, N). In particular, we have successfully obtained a Hamiltonian circuit for GS(k, k(k+1)) for any k>2.
267445
Biological Nomenclatures: A Source of Lexical Knowledge and Ambiguity
There has been increased work in developing automated systems that involve natural language processing (NLP) to recognize and extract genomic information from the literature. Recognition and identification of biological entities is a critical step in this process. NLP systems generally rely on nomenclatures and ontological specifications as resources for determining the names of the entities, assigning semantic categories that are consistent with the corresponding ontology, and assignment of identifiers that map to well-defined entities within a particular nomenclature. Although nomenclatures and ontologies are valuable for text processing systems, they were developed to aid researchers and are heterogeneous in structure and semantics. A uniform resource that is automatically generated from diverse resources, and that is designed for NLP purposes would be a useful tool for the field, and would further database interoperability. This paper presents work towards this goal. We have automatically created lexical resources from four model organism nomenclature systems (mouse, fly, worm, and yeast), and have studied performance of the resources within an existing NLP system, GENIES. Using nomenclatures is not straightforward because issues concerning ambiguity, synonymy, and name variations are quite challenging. In this paper we focus mainly on ambiguity. We determined that the number of ambiguous gene names within the individual nomenclatures, across the four nomenclatures, and with general English ranged from 0%-10.18%, 1.187%-20.30%, and 0%-2.49% respectively. When actually processing text, we found the rate of ambiguous occurrences (not counting ambiguities stemming from English words) to range from 2.4%-32.9% depending on the organisms considered.
2276859
Dynamically Generating a Protein Entity Dictionary Using Online Resources
With the overwhelming amount of biological knowledge stored in free text, natural language processing (NLP) has received much attention recently to make the task of managing information recorded in free text more feasible. One requirement for most NLP systems is the ability to accurately recognize biological entity terms in free text and the ability to map these terms to corresponding records in databases. Such task is called biological named entity tagging. In this paper, we present a system that automatically constructs a protein entity dictionary, which contains gene or protein names associated with UniProt identifiers using online resources. The system can run periodically to always keep up-to-date with these online resources. Using online resources that were available on Dec. 25, 2004, we obtained 4,046,733 terms for 1,640,082 entities. The dictionary can be accessed from the following website: http://biocreative.ifsm.umbc.edu/biothesaurus/.
14126485
Efficient and scalable filtering of graph-based metadata
RDF Site Summaries constitute an application of RDF on the Web that has considerably grown in popularity. However, the way RSS systems operate today limits their scalability. Current RSS feed arregators follow a pull-based architecture model, which is not going to scale with the increasing number of RSS feeds becoming available on the Web. In this paper, we introduce G-ToPSS, a scalable publish/subscribe system for selective information dissemination. G-ToPSS only sends newly updated information to the interested user and follows a push-based architecture model. G-ToPSS is particularly well suited for applications that deal with large-volume content distribution from diverse sources. G-ToPSS allows use of an ontology as a way to provide additional information about the data disseminated. We have implemented and experimentally evaluated G-ToPSS and we provide results demonstrating its scalability compared to alternative approaches. In addition, we describe an application of G-ToPSS and RSS to a Web-based content management system that provides an expressive, efficient, and convenient update notification dissemination system.
7016782
Adaptive Linking between Text and Photos Using Common Sense Reasoning
In a hypermedia authoring task, an author often wants to set up meaningful connections between different media, such as text and photographs. To facilitate this task, it is helpful to have a software agent dynamically adapt the presentation of a media database to the user's authoring activities, and look for opportunities for annotation and retrieval. However, potential connections are often missed because of differences in vocabulary or semantic connections that are "obvious" to people but that might not be explicit.ARIA (Annotation and Retrieval Integration Agent) is a software agent that acts an assistant to a user writing e-mail or Web pages. As the user types a story, it does continuous retrieval and ranking on a photo database. It can use descriptions in the story to semi-automatically annotate pictures. To improve the associations beyond simple keyword matching, we use natural language parsing techniques to extract important roles played by text, such as "who, what, where, when". Since many of the photos depict common everyday situations such as weddings or recitals, we use a common sense knowledge base, Open Mind, to fill in semantic gaps that might otherwise prevent successful associations.
507681
A Novel Approach to Model Generation for Heterogeneous Data Classification
Ensemble methods such as bagging and boosting have been successfully applied to classification problems. Two important issues associated with an ensemble approach are: how to generate models to construct an ensemble, and how to combine them for classification. In this paper, we focus on the problem of model generation for heterogeneous data classification. If we could partition heterogeneous data into a number of homogeneous partitions, we will likely generate reliable and accurate classification models over the homogeneous partitions. We examine different ways of forming homogeneous subsets and propose a novel method that allows a data point to be assigned multiple times in order to generate homogeneous partitions for ensemble learning. We present the details of the new algorithm and empirical studies over the UCI benchmark datasets and datasets of image classification, and show that the proposed approach is effective for heterogeneous data classification.
46794790
Analysis of Hidden Representations by Greedy Clustering
The hidden layer of backpropagation neural networks (NNs) holds the key to the networks' success in solving pattern classification problems. The units in the hidden layer encapsulate the network's internal representations of the outside world described by the input data. this paper, the hidden representations of trained networks are investigated by means simple greedy clustering algorithm. This clustering algorithm is applied to networks have been trained to solve well-known problems: the monks problems, the 5-bit problem and the contiguity problem. The results from applying the algorithm to problems with known concepts provide us with a better understanding of NN learning. These also explain why NNs achieve higher predictive accuracy than that of decision-tree methods. The results of this study can be readily applied to rule extraction from Production rules are extracted for the parity and the monks problems, as well as benchmark data set: Pima Indian diabetes diagnosis. The extracted rules from the Indi...
17436749
Feature Transformation and Multivariate Decision Tree Induction
Univariate decision trees (UDT's) have inherent problems of replication, repetition, and fragmentation. Multivariate decision trees (MDT's) have been proposed to overcome some of the problems. Close examination of the conventional ways of building MDT's, however, reveals that the fragmentation problem still persists. A novel approach is suggested to minimize the fragmentation problem by separating hyperplane search from decision tree building. This is achieved by feature transformation. Let the initial feature vector be x, the new feature vector after feature transformation T is y, i.e., y = T(x). We can obtain an MDTb y (1) building a UDT on y; and (2) replacing new features y at each node with the combinations of initial features x. We elaborate on the advantages of this approach, the details of T, and why it is expected to perform well. Experiments are conducted in order to confirm the analysis, and results are compared to those of C4.5, OC1, and CART.