Background: Alzheimer's is a type of dementia for which treatment and cure are yet to be discovered. However, by modifying lifestyle, daily food consumption, and drinking habit, it is possible to lower the risk of development and severity of Alzheimer's Disease (AD). Earlier studies on finding the impact of food consumption on AD development mainly focused on comparing the intake of each type of food between two groups. This study aims to view the patterns of food taken by AD patients and healthy people from an angle of food balance. Results: This study performed a multiple-factor analysis (MFA), an extension of principal component analysis (PCA). The result showed that wine intake of the AD cohort contributed to the factors (components) in the same way as red meat but in the opposite direction in the components found with the healthy control (HC) cohort. There were more types of food included in the factors for HC than they were in the factors for AD. The amount (gram/day) of meat and processed food consumed by AD patients are highly correlated (RV=0.73). However, no high correlation was found between any groups of food consumed by HC individuals. Conclusion: This study revealed a new angle on analyzing food patterns associated with diseases. It has been observed that the AD cohort consumed more processed food and red meat but less wine than the healthy group. MFA analysis indicated that the variety or balance of food intake might be the key associated with AD development. More comprehensive and extensive analysis of the impact of food balance on health is strongly encouraged.

Electrocardiogram (ECG) signal analysis is widely used to diagnose various cardiac and non-cardiac diseases. Detecting abnormalities on ECG is critical for preventing the onset of life-threatening cardiac arrhythmias. This paper proposed a method based on deep convolutional neural network (DCNN) to detect abnormal heartbeats such as ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB). The proposed model was trained and validated on two large-sample PhysioNet’s MIT-BIH datasets. A separate test result showed overall accuracy of 96% on distinguishing three types of heartbeats VEB, SVEB, and other heartbeats which are not ectopic beat (NOTEB).

Background: Alzheimer's is a common neurodegenerative disease that predominantly affects the elderly. However, the leading causes for the development of Alzheimer's disease (AD) are yet to be identified, and early detection and disease progression intervention are studied to help slow down its deterioration. Recognizing the possible threat of AD in individuals before neurodegenerative changes start to take effect may contribute to the discovery of preventive actions. This study aims to identify socio-demographic, lifestyle, and neuropsychological factors that may contribute to AD development. Results: This study performed the Pearson's chi-squared test for categorical and one-way ANOVA for continuous variables to analyse the association of risk factors for developing AD from two separate cohorts. The patients in the first cohort have no record of the AD development period. In the second cohort, all the AD patients developed dementia within 36 months. Marital status, occupation, APOE4 genotype, Geriatric Depression Scale, and Functional Questionnaire Assessment score were significantly different between normal control and AD patients from both cohorts. The prediction models developed with either cohort showed high performance in ROC (receiver operating characteristic) measurements. Conclusion: This study investigated the effects of socio-demographic and neuropsychological risk factors in AD development from two different cohorts. The significant risk factors from both cohorts can contribute to developing an Alzheimer's risk app that can be potentially reliable and easily accessible.

Cancer is the leading cause of death in Australia. It is estimated that more than 130,000 cases will be diagnosed with cancer in 2017 and further, that the estimated number of deaths will be around 50,000. If we look at the time series data of cancer incidence and cancer mortality it seems there is a very high significant correlation between these variables. This may be spurious and misinterpreted which is quite often the case in epidemiological studies. In this paper we have introduced the concept of co-integration and shown that although cancer incidences are increasing very fast, cancer mortality is not increasing that fast. This paper demonstrated that there is no long term relationship or co-integration between these two variables. However, there exists a short-run causal relationship from cancer to mortality for the cases of lung and prostate cancers. The impulse response function reveals that the mortality peaks at second year and the effect gradually disappear after 8 years.

Colorectal cancer is a heterogeneous disease. Its response to targeted therapies is associated with various factors, and the treatment effect differ significantly between individuals. Personalize medical treatment (PMT), which takes into consideration of individual patient characteristics, is the most effective way to deal with this issue. Patient similarity and clustering analysis is an important part in PMT. Earlier works mainly focused on similarity computation among the patients but overlook to preserve relationships. This paper presents a formal concept analysis-based approach for computing the similarity between colorectal cancer patients. The approach not only does the clustering of patients based on their similarity but also can preserve the relations between clusters in hierarchical structural form. This would allow us to build a knowledge base which is helpful for clinicians to take fast and effective decision for treatment and care of colorectal cancer patient.

BACKGROUND: Prediction of patient outcome in medical intensive care units (ICU) may help for development and investigation of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical outcome of ICU patients. These scores are calculated from clinical physiological and biochemical characteristics of patients. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker of poor clinical prognosis. HRV can be measured from the electrocardiogram non-invasively and monitored in real time. HRV has been identified as a promising 'electronic biomarker' of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients admitted to ICU, with significant morbidity and mortality, and often difficult to predict outcomes. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission across the first 24 h in the ICU in severe TBI patients to develop a patient outcome prediction system. RESULTS: A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures with a genetic algorithm for feature selection. The result (AUC: 0.77) was compared with earlier reported scoring systems (highest AUC: 0.76), encouraging further development and practical application. CONCLUSIONS: The prediction models built with different feature sets indicated that HRV based parameters may help predict brain injury patient outcome better than the previously adopted illness severity scores.

Prediction of patient outcome in medical intensive care units (ICU) may help for development of early interventional strategies. Several ICU scoring systems have been developed and are used to predict clinical deterioration of ICU patients. These scores are calculated from characteristics of patients and clinical records. Heart rate variability (HRV) is a correlate of cardiac autonomic regulation and has been evident as a marker in many critical diseases. It can be measured based on electrocardiogram (ECG) which is non-invasive and can be real time monitored. HRV has been identified as a promising `electronic biomarker' of disease severity. Traumatic brain injury (TBI) is a subset of critically ill patients, admitted to ICU. Changes of HRV for brain injured patients have been reported in several studies. This study aimed to utilize the continuous HRV collection from admission for the first 24 hours in the ICU in severe TBI patients, and develop a patient outcome prediction system. A feature extraction strategy was applied to measure the HRV fluctuation during time. A prediction model was developed based on HRV measures collected for the first day of patient admission to the ICU. The result was compared with current evaluated ones, and showed promising result for further development and potential for practical application.

BACKGROUND: Obesity is associated with chronic activation of the immune system and an altered gut microbiome, leading to increased risk of chronic disease development. As yet, no biomarker profile has been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlation-based network approach to identify existing patterns of immune-microbiome interactions in obesity. RESULTS: The current study performed correlation-based network analysis on five different datasets obtained from 11 obese with metabolic syndrome (MetS) and 12 healthy weight men. These datasets included: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese with MetS group had a denser network (total number of edges, n = 369) compared to the healthy network (n = 299). Within the obese with MetS network, biomarkers from the immune cell abundance group was found to be correlated to biomarkers from all four other datasets. Conversely in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. These observations suggest high involvement of immune cells in obese with MetS individuals. There were also three key hubs found among immune cells in the obese with MetS networks involving regulatory T cells, neutrophil and cytotoxic cell abundance. No hubs were present in the healthy network. CONCLUSION: These results suggest a more complex interaction of inflammatory markers in obesity, with high connectivity of immune cells in the obese with MetS network compared to the healthy network. Three key hubs were identified in the obese with MetS network, involving Treg, neutrophils and cytotoxic cell abundance. Compared to a t-test, the network approach offered more meaningful results when comparing obese with MetS and healthy weight individuals, demonstrating its superiority in exploratory analysis.

BACKGROUND: Principal components analysis (PCA) is often used to find characteristic patterns associated with certain diseases by reducing variable numbers before a predictive model is built, particularly when some variables are correlated. Usually, the first two or three components from PCA are used to determine whether individuals can be clustered into two classification groups based on pre-determined criteria: control and disease group. However, a combination of other components may exist which better distinguish diseased individuals from healthy controls. Genetic algorithms (GAs) can be useful and efficient for searching the best combination of variables to build a prediction model. This study aimed to develop a prediction model that combines PCA and a genetic algorithm (GA) for identifying sets of bacterial species associated with obesity and metabolic syndrome (Mets). RESULTS: The prediction models built using the combination of principal components (PCs) selected by GA were compared to the models built using the top PCs that explained the most variance in the sample and to models built with selected original variables. The advantages of combining PCA with GA were demonstrated. CONCLUSIONS: The proposed algorithm overcomes the limitation of PCA for data analysis. It offers a new way to build prediction models that may improve the prediction accuracy. The variables included in the PCs that were selected by GA can be combined with flexibility for potential clinical applications. The algorithm can be useful for many biological studies where high dimensional data are collected with highly correlated variables.

Elemental metabolomics is quantification and characterization of total concentration of chemical elements in biological samples and monitoring of their changes. Recent advances in inductively coupled plasma mass spectrometry have enabled simultaneous measurement of concentrations of > 70 elements in biological samples. In living organisms, elements interact and compete with each other for absorption and molecular interactions. They also interact with proteins and nucleotide sequences. These interactions modulate enzymatic activities and are critical for many molecular and cellular functions. Testing for concentration of > 40 elements in blood, other bodily fluids and tissues is now in routine use in advanced medical laboratories. In this article, we define the basic concepts of elemental metabolomics, summarize standards and workflows, and propose minimum information for reporting the results of an elemental metabolomics experiment. Major statistical and informatics tools for elemental metabolomics are reviewed, and examples of applications are discussed. Elemental metabolomics is emerging as an important new technology with applications in medical diagnostics, nutrition, agriculture, food science, environmental science and multiplicity of other areas.

Biomarkers discovery research requires the integrated analyses of a variety of the data across multiple domains, including clinical data, pathology data, gene expression, epigenetic data. Proper analysis can help understand the biological mechanism and better interpret the impact of the markers to disease. Realising the nature of the data in biomedical research and translational biomedicine, we developed a data analysis pipeline with a set of computational functions and an integrated method that can serve as a template for many biomarkers discovery research. The data analysis pipeline was developed with the data collected to identify biomarkers associated with obesity related disease. The set of functions included in the analysis template were used for finding the biomarkers and their combinatorial effect associated with obesity. The functions were developed in the general way that can be extended to other study easily.

BACKGROUND: Alzheimer's disease is a multifactorial disorder that may be diagnosed earlier using a combination of tests rather than any single test. Search algorithms and optimization techniques in combination with model evaluation techniques have been used previously to perform the selection of suitable feature sets. Previously we successfully applied GA with LR to neuropsychological data contained within the The Australian Imaging, Biomarkers and Lifestyle (AIBL) study of aging, to select cognitive tests for prediction of progression of AD. This research addresses an Adaptive Genetic Algorithm (AGA) in combination with LR for identifying the best biomarker combination for prediction of the progression to AD. RESULTS: The model has been explored in terms of parameter optimization to predict conversion from healthy stage to AD with high accuracy. Several feature sets were selected - the resulting prediction moddels showed higher area under the ROC values (0.83-0.89). The results has shown consistency with some of the medical research reported in literature. CONCLUSION: The AGA has proven useful in selecting the best combination of biomarkers for prediction of AD progression. The algorithm presented here is generic and can be extended to other data sets generated in projects that seek to identify combination of biomarkers or other features that are predictive of disease onset or progression.

BACKGROUND: Assessment of risk and early diagnosis of Alzheimer's disease (AD) is a key to its prevention or slowing the progression of the disease. Previous research on risk factors for AD typically utilizes statistical comparison tests or stepwise selection with regression models. Outcomes of these methods tend to emphasize single risk factors rather than a combination of risk factors. However, a combination of factors, rather than any one alone, is likely to affect disease development. Genetic algorithms (GA) can be useful and efficient for searching a combination of variables for the best achievement (eg. accuracy of diagnosis), especially when the search space is large, complex or poorly understood, as in the case in prediction of AD development. RESULTS: Multiple sets of neuropsychological tests were identified by GA to best predict conversions between clinical categories, with a cross validated AUC (area under the ROC curve) of 0.90 for prediction of HC conversion to MCI/AD and 0.86 for MCI conversion to AD within 36 months. CONCLUSIONS: This study showed the potential of GA application in the neural science area. It demonstrated that the combination of a small set of variables is superior in performance than the use of all the single significant variables in the model for prediction of progression of disease. Variables more frequently selected by GA might be more important as part of the algorithm for prediction of disease development.

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at .

In this paper we present an approach based on the application of an evolutionary algorithm to optimally tune the parameters of a novel technique for effective Web information retrieval. Context matching is a context-based technique for the ad-hoc retrieval of Web documents that relies on a number of inter-related parameters that define the nature of the context it uses. Its aim is to dynamically generate a context-based measure of term significance during retrieval that can be used as an indicator of document relevancy and ultimately contribute to a documents rank score. But the optimal setting of context matching parameters is an important aspect of the technique to ensure effective retrieval. Thus, the goal of this paper is to investigate the use of an evolutionary algorithm for the optimization of context matching parameters and compare its performance to an iterative technique that exhaustively explores combinations of parameters. We show how the most effective settings for parameters are obtained efficiently through the evolutionary algorithm. We also show how context matching, through the use of these optimized parameters, achieves effective retrieval results on benchmark data that are a significant improvement on previously published results.

Digital mammography is one of the most suitable methods for early detection of breast cancer. In uses digital mammograms to find suspicious areas. However, it is very difficult to distinguish benign and malignant cases, especially for the small size lesions in the early stage of cancer. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists. This work proposes a neural-genetic algorithm for feature selection in conjunction with neural network based classifier. It also combined the computer-extracted statistical features from the mammogram with the human-extracted features for classifying different types of small breast abnormalities. It obtained 90.5% accuracy rate for calcification cases and 87.2% for mass cases with difference feature subsets. The obtained results show that different types of breast abnormality should use different features for classification.

Digital mammography is one of the most suitable methods for early detection of breast cancer. It uses digital mammograms to find suspicious areas containing benign and malignant microcalcifications. However, it is very difficult to distinguish benign and malignant microcalcifications. This is reflected in the high percentage of unnecessary biopsies that are performed and many deaths caused by late detection or misdiagnosis. A computer based feature selection and classification system can provide a second opinion to the radiologists in assessment of microcalcifications. The research proposes and investigates a neural-genetic algorithm for feature selection in conjunction with neural and statistical classifiers to classify microcalcification patterns in digital mammograms. The obtained results show that the proposed approach is able to find an appropriate feature subset and neural classifier achieves better results than two statistical models.