7867544
A statistical method for identifying differential gene-gene co-expression patterns
MOTIVATION
To understand cancer etiology, it is important to explore molecular changes in cellular processes from normal state to cancerous state. Because genes interact with each other during cellular processes, carcinogenesis related genes may form differential co-expression patterns with other genes in different cell states. In this study, we develop a statistical method for identifying differential gene-gene co-expression patterns in different cell states.


RESULTS
For efficient pattern recognition, we extend the traditional F-statistic and obtain an Expected Conditional F-statistic (ECF-statistic), which incorporates statistical information of location and correlation. We also propose a statistical method for data transformation. Our approach is applied to a microarray gene expression dataset for prostate cancer study. For a gene of interest, our method can select other genes that have differential gene-gene co-expression patterns with this gene in different cell states. The 10 most frequently selected genes, include hepsin, GSTP1 and AMACR, which have recently been proposed to be associated with prostate carcinogenesis. However, genes GSTP1 and AMACR cannot be identified by studying differential gene expression alone. By using tumor suppressor genes TP53, PTEN and RB1, we identify seven genes that also include hepsin, GSTP1 and AMACR. We show that genes associated with cancer may have differential gene-gene expression patterns with many other genes in different cell states. By discovering such patterns, we may be able to identify carcinogenesis related genes.
11367648
Inferring protein-protein interactions through high-throughput interaction data from diverse organisms
MOTIVATION
Identifying protein-protein interactions is critical for understanding cellular processes. Because protein domains represent binding modules and are responsible for the interactions between proteins, computational approaches have been proposed to predict protein interactions at the domain level. The fact that protein domains are likely evolutionarily conserved allows us to pool information from data across multiple organisms for the inference of domain-domain and protein-protein interaction probabilities.


RESULTS
We use a likelihood approach to estimating domain-domain interaction probabilities by integrating large-scale protein interaction data from three organisms, Saccharomyces cerevisiae, Caenorhabditis elegans and Drosophila melanogaster. The estimated domain-domain interaction probabilities are then used to predict protein-protein interactions in S.cerevisiae. Based on a thorough comparison of sensitivity and specificity, Gene Ontology term enrichment and gene expression profiles, we have demonstrated that it may be far more informative to predict protein-protein interactions from diverse organisms than from a single organism.


AVAILABILITY
The program for computing the protein-protein interaction probabilities and supplementary material are available at http://bioinformatics.med.yale.edu/interaction.
7720203
A low-cost rescheduling policy for efficient mapping of workflows on grid systems
Workflow management is emerging as an important service in Grid computing. A simple model that can be used for the representation of certain workflows is a directed acyclic graph. Although many heuristics have been proposed to schedule such graphs on heterogeneous environments, most of them assume accurate prediction of computation and communication costs. This limits their direct applicability to a dynamically changing environment, such as the Grid. In this environment, an initial schedule may be built based on estimates, but run-time rescheduling may be needed to improve application performance. This paper presents a low-cost rescheduling policy, which considers rescheduling at a few, carefully selected points during the execution. This policy achieves performance results, which are comparable with those achieved by a policy that dynamically attempts to reschedule before the execution of every task.
15074010
A Modular Reduction Method for k-NN Algorithm with Self-recombination Learning
A difficulty faced by existing reduction techniques for k-NN algorithm is to require loading the whole training data set. Therefore, these approaches often become inefficient when they are used for solving large-scale problems. To overcome this deficiency, we propose a new method for reducing samples for k-NN algorithm. The basic idea behind the proposed method is a self-recombination learning strategy, which is originally designed for combining classifiers to speed up response time by reducing the number of base classifiers to be checked and improve the generalization performance by rearranging the order of training samples. Experimental results on several benchmark problems indicate that the proposed method is valid and efficient.
17735073
On problems and conjectures on adjointly equivalent graphs
For a graph G, let h(G,x) denote its adjoint polynomial and @b(G) denote the minimum real root of h(G,x). Two graphs H and G are said to be adjointly equivalent if h(H,x)=h(G,x). Let F"1={G|@b(G)>-4} and F"2={G|@b(G)>=-4}. In this paper, we give a necessary and sufficient condition for two graphs H and G in F"i to be adjointly equivalent, where i=1,2. We also solve some problems and conjectures proposed by Dong et al. (Discrete Math. 258 (2002) 303-321).
1139392
Pathway analysis using random forests classification and regression
MOTIVATION
Although numerous methods have been developed to better capture biological information from microarray data, commonly used single gene-based methods neglect interactions among genes and leave room for other novel approaches. For example, most classification and regression methods for microarray data are based on the whole set of genes and have not made use of pathway information. Pathway-based analysis in microarray studies may lead to more informative and relevant knowledge for biological researchers.


RESULTS
In this paper, we describe a pathway-based classification and regression method using Random Forests to analyze gene expression data. The proposed methods allow researchers to rank important pathways from externally available databases, discover important genes, find pathway-based outlying cases and make full use of a continuous outcome variable in the regression setting. We also compared Random Forests with other machine learning methods using several datasets and found that Random Forests classification error rates were either the lowest or the second-lowest. By combining pathway information and novel statistical methods, this procedure represents a promising computational strategy in dissecting pathways and can provide biological insight into the study of microarray data.


AVAILABILITY
Source code written in R is available from http://bioinformatics.med.yale.edu/pathway-analysis/rf.htm.
8780756
A Modular k-Nearest Neighbor Classification Method for Massively Parallel Text Categorization
This paper presents a Min-Max modular k-nearest neighbor (M3-k-NN) classification method for massively parallel text categorization. The basic idea behind the method is to decompose a large-scale text categorization problem into a number of smaller two-class subproblems and combine all of the individual modular k-NN classifiers trained on the smaller two-class subproblems into an M3-k-NN classifier. Our experiments in text categorization demonstrate that M3-k-NN is much faster than conventional k-NN, and meanwhile the classification accuracy of M3-k-NN is slightly better than that of the conventional k-NN. In practical, M3-k-NN has intimate relationship with high order k-NN algorithm; therefore, in theoretical sense, the reliability of M3-k-NN has been supported to some extend.