A clustering based hybrid system for mass spectrometry data analysis

Recently, much attention has been given to the mass spectrometry (MS) technology based disease classification, diagnosis, and protein-based biomarker identification. Similar to microarray based investigation, proteomic data generated by such kind of high-throughput experiments are often with high feature-to-sample ratio. Moreover, biological information and pattern are compounded with data noise, redundancy and outliers. Thus, the development of algorithms and procedures for the analysis and interpretation of such kind of data is of paramount importance. In this paper, we propose a hybrid system for analyzing such high dimensional data. The proposed method uses the k-mean clustering algorithm based feature extraction and selection procedure to bridge the filter selection and wrapper selection methods. The potential informative mass/charge (m/z) markers selected by filters are subject to the k-mean clustering algorithm for correlation and redundancy reduction, and a multi-objective Genetic Algorithm selector is then employed to identify discriminative m/z markers generated by k-mean clustering algorithm. Experimental results obtained by using the proposed method indicate that it is suitable for m/z biomarker selection and MS based sample classification.

A generalised data analysis approach for baggage handling systems simulation

Airport baggage handling systems are a critical infrastructure component within major airports, and essential to ensure smooth luggage transfer while preventing dangerous material being loaded onto aircraft. This paper proposes a standard set of measures to assess the expected performance of a baggage handling system through discrete event simulation. These evaluation methods also have application in the study of general network systems. Results from the application of these methods reveal operational characteristics of the studied BHS, in terms of metrics such as peak throughput, in-system time and system recovery time.

Adolescent substance use and educational attainment: an integrative data analysis comparing cannabis and alcohol from three Australasian cohorts

BACKGROUND: The relative contributions of cannabis and alcohol use to educational outcomes are unclear. We examined the extent to which adolescent cannabis or alcohol use predicts educational attainment in emerging adulthood. METHODS: Participant-level data were integrated from three longitudinal studies from Australia and New Zealand (Australian Temperament Project, Christchurch Health and Development Study, and Victorian Adolescent Health Cohort Study). The number of participants varied by analysis (N=2179-3678) and were assessed on multiple occasions between ages 13 and 25. We described the association between frequency of cannabis or alcohol use prior to age 17 and high school non-completion, university non-enrolment, and degree non-attainment by age 25. Two other measures of alcohol use in adolescence were also examined. RESULTS: After covariate adjustment using a propensity score approach, adolescent cannabis use (weekly+) was associated with 1½ to two-fold increases in the odds of high school non-completion (OR=1.60, 95% CI=1.09-2.35), university non-enrolment (OR=1.51, 95% CI=1.06-2.13), and degree non-attainment (OR=1.96, 95% CI=1.36-2.81). In contrast, adjusted associations for all measures of adolescent alcohol use were inconsistent and weaker. Attributable risk estimates indicated adolescent cannabis use accounted for a greater proportion of the overall rate of non-progression with formal education than adolescent alcohol use. CONCLUSIONS: Findings are important to the debate about the relative harms of cannabis and alcohol use. Adolescent cannabis use is a better marker of lower educational attainment than adolescent alcohol use and identifies an important target population for preventive intervention.

Heart failure following cancer therapy in patients with hematological malignancies aged 18 years and under : a linked health data analysis (1996-2009)

Introduction: The causal link between chemotherapy treatments and subsequent cardiotoxicity is well established, particularly for children with hematological malignancies. Little information exists on the characteristics and outcomes for patients with heart failure (HF) after chemotherapy. This study aimed to describe the characteristics, survival and mortality of patients who received chemotherapy for hematological cancer (leukemias, lymphomas and related disorders) before 18 years old and subsequently developed HF compared to those who did not.

Methods: Linked health data (1996-2009) from the Queensland Cancer Registry, Death Registry and Hospital Administration records for HF and chemotherapy admissions were reviewed. From all breast and hematological cancers patients (n=73,158), 15,987 received chemotherapy, including 819 patients aged ≤18 years at time of cancer diagnosis. Patients were categorized as those with an index HF admission (occurred after cancer diagnosis) and those without an index HF admission (non HF).

Results: Of the 819 patients, 3.7% (n=30) had an index HF admission. Median age of HF patients at time of cancer diagnosis was 5 years (IQR 3-12) compared to 7 years (IQR 3-14) in the non HF group (p=0.503). Median follow up from cancer diagnosis was 2.5 years in the HF group compared to 5.42 years in the non HF group (p<0.01). Of those who developed HF, 70% (n=21) had the index admission within 12 months of their cancer diagnosis. Of those with HF, 53.3% (n=16) died (all cause) compared to 14.6% (n=115) with no HF. On adjustment for age, sex and chemotherapy admissions, HF patients had an almost 5 fold increased mortality risk compared to non HF patients (HR 4.91 [95% CI, 2.88-8.36]) (Figure 1).

Conclusions: This study demonstrated that in children with hematological cancers the onset of HF occurred soon after chemotherapy and mortality risk is almost 5 times that of children who do not develop HF. Innovative strategies are still needed for the prevention and management of cardiotoxicity in this population.

There are always two sides to a story: the use of social dramas as a mode of data analysis in information systems

With the continually evolving social nature of information systems research there is a need to identify different “modes of analysis” (Myers, 1997) to uncover our understanding of the complex, messy and often chaotic nature of human factors. One suggested mode of analysis is that of social dramas, a tool developed in the anthropological discipline by Victor Turner. The use of social dramas also utilises the work by Goffman (1959; 1997) and enables the researcher to investigate events from the front stage, reporting obvious issues in systems implementation, and from the back stage, identifying the hidden aspects of systems implementation and the underpinning discourses. A case study exploring the social dramas involved in systems selection and implementation has been provided to support the use of this methodological tool.

Generating best evidence from qualitative research: the role of data analysis

Objective: To outline the importance of the clarity of data analysis in the doing and reporting of interview-based qualitative research.

Approach: We explore the clear links between data analysis and evidence. We argue that transparency in the data analysis process is integral to determining the evidence that is generated. Data analysis must occur concurrently with data collection and comprises an ongoing process of 'testing the fit' between the data collected and analysis. We discuss four steps in the process of thematic data analysis: immersion, coding, categorising and generation of themes.

Conclusion: Rigorous and systematic analysis of qualitative data is integral to the production of high-quality research. Studies that give an explicit account of the data analysis process provide insights into how conclusions are reached while studies that explain themes anchored to data and theory produce the strongest evidence.

A panel data analysis of the military expenditure-external debt nexus : evidence from six middle eastern countries

While a number of studies examine the nexus between military expenditure and economic growth, little consideration has been give to the effect of military expenditure on external debt. This article examines the impact of military expenditure and income on external debt for a panel of six Middle Eastern countries - Oman, Syria, Yemen, Bahrain, Iran, and Jordan - over the period 1988 to 2002. The Middle East represents an interesting study of the effect of military expenditure on external debt because it has one of the highest rates of arms imports in the world and it is one of the most indebted regions in the world. The study first establishes whether there is a long-run relationship between military expenditure, income, and external debt in the six countries using a panel unit root and panel cointegration framework and then proceeds to estimate the long-run and short-run effects of military expenditure and income on external debt. The study finds that external debt is elastic with respect to military expenditure in the long run and inelastic with respect to military expenditure in the short run. For the panel of six Middle Eastern countries, in the long run a 1% increase in military expenditure results in between a 1.1 % and 1.6% increase in external debt, while a 1% increase in income reduces external debt by between 0.6% and 0.8%, depending on the specific estimator employed. In the short run, a 1% increase in military expenditure increases external debt by 0.2%, while the effect of income on external debt is statistically insignificant.

A panel data analysis of the determinants of oil consumption : the case of Australia

The goal of this paper is to examine the determinants of oil consumption for a panel consisting of six Australian States and one territory, namely Queensland, New South Wales, Victoria, Tasmania, South Australia, Western Australia, and the Northern territory, for the period 1985–2006. We find that oil consumption, oil prices and income are panel cointegrated. We estimate long-run elasticities and find that oil prices have had a statistically insignificant impact on oil consumption, while income has had a statistically significant positive effect on oil consumption.

Tourism and economic growth: a panel data analysis for Pacific Island countries

The contribution of tourism to the economic growth of Pacific Island countries (PICs) has achieved significance in the past decade. The shift in the economic policies of the PICs from the late 1980s has been decisively away from import substitution and agriculture to urban-based manufacturing and services sectors. Tourism is the main component of the services sector in the PICs. The contribution of tourism to economic growth in Fiji, Tonga, the Solomon Islands and Papua New Guinea is expected to grow. The authors use panel data for the four PICs to test the long-run relationship between real GDP and real tourism exports. They find support for panel cointegration and the results suggest that a 1% increase in tourism exports increases GDP by 0.72% in the long run and by 0.24% in the short run.

An agent-based hybrid system for microarray data analysis

This article reports our experience in agent-based hybrid construction for microarray data analysis. The contributions are twofold: We demonstrate that agent-based approaches are suitable for building hybrid systems in general, and that a genetic ensemble system is appropriate for microarray data analysis in particular. Created using an agent-based framework, this genetic ensemble system for microarray data analysis excels in both sample classification accuracy and gene selection reproducibility.

An embedded two-layer feature selection approach for microarray data analysis

Feature selection is an important technique in dealing with application problems with large number of variables and limited training samples, such as image processing, combinatorial chemistry, and microarray analysis. Commonly employed feature selection strategies can be divided into filter and wrapper. In this study, we propose an embedded two-layer feature selection approach to combining the advantages of filter and wrapper algorithms while avoiding their drawbacks. The hybrid algorithm, called GAEF (Genetic Algorithm with embedded filter), divides the feature selection process into two stages. In the first stage, Genetic Algorithm (GA) is employed to pre-select features while in the second stage a filter selector is used to further identify a small feature subset for accurate sample classification. Three benchmark microarray datasets are used to evaluate the proposed algorithm. The experimental results suggest that this embedded two-layer feature selection strategy is able to improve the stability of the selection results as well as the sample classification accuracy.

The productivity-wage and productivity-employment nexus : a panel data analysis of Indian manufacturing

This article investigates the long-run relationship between labour productivity and employment, and between labour productivity and real wages in the case of the Indian manufacturing sector. The panel data set consists of 17 two-digit manufacturing industries for the period 1973–1974 to 1999–2001. We find that productivity-wages and productivity-employment are panel cointegrated for all industries. We find that both employment and real wages exert a positive effect on labour productivity. We argue that flexible labour market has a significant influence on manufacturing productivity, employment and real wages in the case of Indian manufacturing.