Stephen Swift

Consensus clustering and functional interpretation of gene-expression data

Microarray analysis using clustering algorithms can suffer from lack of inter-method consistency in assigning related gene-expression profiles to clusters. Obtaining a consensus set of clusters from a number of clustering methods should improve confidence in gene-expression analysis. Here we introduce consensus clustering, which provides such an advantage. When coupled with a statistically based gene functional analysis, our method allowed the identification of novel genes regulated by NFκB and the unfolded protein response in certain B-cell lymphomas.

The interpretation and utility of three cohesion metrics for object-oriented design

The concept of cohesion in a class has been the subject of various recent empirical studies and has been measured using many different metrics. In the structured programming paradigm, the software engineering community has adopted an informal yet meaningful and understandable definition of cohesion based on the work of Yourdon and Constantine. The object-oriented (OO) paradigm has formalised various cohesion measures, but the argument over the most meaningful of those metrics continues to be debated. Yet achieving highly cohesive software is fundamental to its comprehension and thus its maintainability. In this article we subject two object-oriented cohesion metrics, CAMC and NHD, to a rigorous mathematical analysis in order to better understand and interpret them. This analysis enables us to offer substantial arguments for preferring the NHD metric to CAMC as a measure of cohesion. Furthermore, we provide a complete understanding of the behaviour of these metrics, enabling us to attach a meaning to the values calculated by the CAMC and NHD metrics. In addition, we introduce a variant of the NHD metric and demonstrate that it has several advantages over CAMC and NHD. While it may be true that a generally accepted formal and informal definition of cohesion continues to elude the OO software engineering community, there seems considerable value in being able to compare, contrast, and interpret metrics which attempt to measure the same features of software.

A weighted sum validity function for clustering with a hybrid niching genetic algorithm

Clustering is inherently a difficult problem, both with respect to the construction of adequate objective functions as well as to the optimization of the objective functions. In this paper, we suggest an objective function called the Weighted Sum Validity Function (WSVF), which is a weighted sum of the several normalized cluster validity functions. Further, we propose a Hybrid Niching Genetic Algorithm (HNGA), which can be used for the optimization of the WSVF to automatically evolve the proper number of clusters as well as appropriate partitioning of the data set. Within the HNGA, a niching method is developed to preserve both the diversity of the population with respect to the number of clusters encoded in the individuals and the diversity of the subpopulation with the same number of clusters during the search. In addition, we hybridize the niching method with the k-means algorithm. In the experiments, we show the effectiveness of both the HNGA and the WSVF. In comparison with other related genetic clustering algorithms, the HNGA can consistently and efficiently converge to the best known optimum corresponding to the given data in concurrence with the convergence result. The WSVF is found generally able to improve the confidence of clustering solutions and achieve more accurate and robust results.

A Constrained Evolutionary Computation Method for Detecting Controlling Regions of Cortical Networks

Controlling regions in cortical networks, which serve as key nodes to control the dynamics of networks to a desired state, can be detected by minimizing the eigenratio R and the maximum imaginary part σ of an extended connection matrix. Until now, optimal selection of the set of controlling regions is still an open problem and this paper represents the first attempt to include two measures of controllability into one unified framework. The detection problem of controlling regions in cortical networks is converted into a constrained optimization problem (COP), where the objective function R is minimized and σ is regarded as a constraint. Then, the detection of controlling regions of a weighted and directed complex network (e.g., a cortical network of a cat), is thoroughly investigated. The controlling regions of cortical networks are successfully detected by means of an improved dynamic hybrid framework (IDyHF). Our experiments verify that the proposed IDyHF outperforms two recently developed evolutionary computation methods in constrained optimization field and some traditional methods in control theory as well as graph theory. Based on the IDyHF, the controlling regions are detected in a microscopic and macroscopic way. Our results unveil the dependence of controlling regions on the number of driver nodes I and the constraint r. The controlling regions are largely selected from the regions with a large in-degree and a small out-degree. When r = + ∞, there exists a concave shape of the mean degrees of the driver nodes, i.e., the regions with a large degree are of great importance to the control of the networks when I is small and the regions with a small degree are helpful to control the networks when I increases. When r = 0, the mean degrees of the driver nodes increase as a function of I. We find that controlling σ is becoming more important in controlling a cortical network with increasing I. The methods and results of detecting controlling regions in this paper would promote the coordination and information consensus of various kinds of real-world complex networks including transportation networks, genetic regulatory networks, and social networks, etc.

Generating Feasible Transition Paths for Testing from an Extended Finite State Machine (EFSM)

The problem of testing from an extended finite state machine (EFSM) can be expressed in terms of finding suitable paths through the EFSM and then deriving test data to follow the paths. A chosen path may be infeasible and so it is desirable to have methods that can direct the search for appropriate paths through the EFSM towards those that are likely to be feasible. However, generating feasible transition paths (FTPs) for model based testing is a challenging task and is an open research problem. This paper introduces a novel fitness metric that analyzes data flow dependence among the actions and conditions of the transitions of a path in order to estimate its feasibility. The proposed fitness metric is evaluated by being used in a genetic algorithm to guide the search for FTPs.

An empirical study of the robustness of two module clustering fitness functions

Two of the attractions of search-based software engineering (SBSE) derive from the nature of the fitness functions used to guide the search. These have proved to be highly robust (for a variety of different search algorithms) and have yielded insight into the nature of the search space itself, shedding light upon the software engineering problem in hand.This paper aims to exploit these two benefits of SBSE in the context of search based module clustering. The paper presents empirical results which compare the robustness of two fitness functions used for software module clustering: one (MQ) used exclusively for module clustering. The other is EVM, a clustering fitness function previously applied to time series and gene expression data.The results show that both metrics are relatively robust in the presence of noise, with EVM being the more robust of the two. The results may also yield some interesting insights into the nature of software graphs.

A meta-analysis of relationships between organizational characteristics and IT innovation adoption in organizations

Adoption of IT in organizations is influenced by a wide range of factors in technology, organization, environment, and individuals. Researchers have identified several factors that either facilitate or hinder innovation adoption. Studies have produced inconsistent and contradictory outcomes. We performed a meta-analysis of ten organizational factors to determine their relative impact and strength. We aggregated their findings to determine the magnitude and direction of the relationship between organizational factors and IT innovation adoption. We found organizational readiness to be the most significant attribute and also found a moderately significant relationship between IT adoption and IS department size. Our study found weak significance of IS infrastructure, top management support, IT expertise, resources, and organizational size on IT adoption of technology while formalization, centralization, and product champion were found to be insignificant attributes. We also examined stage of innovation, type of innovation, type of organization, and size of organization as moderator conditions affecting the relationship between the organizational variables and IT adoption.

Stochastic Dynamic Modeling of Short Gene Expression Time-Series Data

In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, both the model parameters and the actual values of the gene expression levels can be identified simultaneously. Moreover, the algorithm can deal with the sparse parameter identification and the noisy data in an efficient way. It is also shown that the EM algorithm can handle the microarray gene expression data with large number of variables but a small number of observations. The gene expression stochastic dynamic models for four real-world gene expression data sets are constructed to demonstrate the advantages of the introduced algorithm. Several indices are proposed to evaluate the models of inferred gene regulatory networks, and the relevant biological properties are discussed.

An integrated search-based approach for automatic testing from extended finite state machine (EFSM) models

Context: The extended finite state machine (EFSM) is a modelling approach that has been used to represent a wide range of systems. When testing from an EFSM, it is normal to use a test criterion such as transition coverage. Such test criteria are often expressed in terms of transition paths (TPs) through an EFSM. Despite the popularity of EFSMs, testing from an EFSM is difficult for two main reasons: path feasibility and path input sequence generation. The path feasibility problem concerns generating paths that are feasible whereas the path input sequence generation problem is to find an input sequence that can traverse a feasible path. Objective: While search-based approaches have been used in test automation, there has been relatively little work that uses them when testing from an EFSM. In this paper, we propose an integrated search-based approach to automate testing from an EFSM. Method: The approach has two phases, the aim of the first phase being to produce a feasible TP (FTP) while the second phase searches for an input sequence to trigger this TP. The first phase uses a Genetic Algorithm whose fitness function is a TP feasibility metric based on dataflow dependence. The second phase uses a Genetic Algorithm whose fitness function is based on a combination of a branch distance function and approach level. Results: Experimental results using five EFSMs found the first phase to be effective in generating FTPs with a success rate of approximately 96.6%. Furthermore, the proposed input sequence generator could trigger all the generated feasible TPs (success rate=100%). Conclusion: The results derived from the experiment demonstrate that the proposed approach is effective in automating testing from an EFSM.

RGFGA: An Efficient Representation and Crossover for Grouping Genetic Algorithms

There is substantial research into genetic algorithms that are used to group large numbers of objects into mutually exclusive subsets based upon some fitness function. However, nearly all methods involve degeneracy to some degree. We introduce a new representation for grouping genetic algorithms, the restricted growth function genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman optimisation method and a well-established statistical clustering algorithm, with encouraging results.