Fuzzy methods for analysis of microarrays and networks

Bioinformatics involves analyses of biological data such as DNA sequences, microarrays and protein-protein interaction (PPI) networks. Its two main objectives are the identification of genes or proteins and the prediction of their functions. Biological data often contain uncertain and imprecise information. Fuzzy theory provides useful tools to deal with this type of information, hence has played an important role in analyses of biological data. In this thesis, we aim to develop some new fuzzy techniques and apply them on DNA microarrays and PPI networks. We will focus on three problems: (1) clustering of microarrays; (2) identification of disease-associated genes in microarrays; and (3) identification of protein complexes in PPI networks. The first part of the thesis aims to detect, by the fuzzy C-means (FCM) method, clustering structures in DNA microarrays corrupted by noise. Because of the presence of noise, some clustering structures found in random data may not have any biological significance. In this part, we propose to combine the FCM with the empirical mode decomposition (EMD) for clustering microarray data. The purpose of EMD is to reduce, preferably to remove, the effect of noise, resulting in what is known as denoised data. We call this method the fuzzy C-means method with empirical mode decomposition (FCM-EMD). We applied this method on yeast and serum microarrays, and the silhouette values are used for assessment of the quality of clustering. The results indicate that the clustering structures of denoised data are more reasonable, implying that genes have tighter association with their clusters. Furthermore we found that the estimation of the fuzzy parameter m, which is a difficult step, can be avoided to some extent by analysing denoised microarray data. The second part aims to identify disease-associated genes from DNA microarray data which are generated under different conditions, e.g., patients and normal people. We developed a type-2 fuzzy membership (FM) function for identification of diseaseassociated genes. This approach is applied to diabetes and lung cancer data, and a comparison with the original FM test was carried out. Among the ten best-ranked genes of diabetes identified by the type-2 FM test, seven genes have been confirmed as diabetes-associated genes according to gene description information in Gene Bank and the published literature. An additional gene is further identified. Among the ten best-ranked genes identified in lung cancer data, seven are confirmed that they are associated with lung cancer or its treatment. The type-2 FM-d values are significantly different, which makes the identifications more convincing than the original FM test. The third part of the thesis aims to identify protein complexes in large interaction networks. Identification of protein complexes is crucial to understand the principles of cellular organisation and to predict protein functions. In this part, we proposed a novel method which combines the fuzzy clustering method and interaction probability to identify the overlapping and non-overlapping community structures in PPI networks, then to detect protein complexes in these sub-networks. Our method is based on both the fuzzy relation model and the graph model. We applied the method on several PPI networks and compared with a popular protein complex identification method, the clique percolation method. For the same data, we detected more protein complexes. We also applied our method on two social networks. The results showed our method works well for detecting sub-networks and give a reasonable understanding of these communities.

Lifestyle risk factor modification in midlife women with type 2 diabetes : theoretical modelling of perceived barriers

Objective: The aim of this paper is to propose a ‘Perceived barriers and lifestyle risk factor modification model’ that could be incorporated into existing frameworks for diabetes education to enhance lifestyle risk factor education in women. Setting: Diabetes education, community health. Primary argument: ‘Perceived barriers’ is a health promotion concept that has been found to be a significant predictor of health promotion behaviour. There is evidence that women face a range of perceived barriers that prevent them from engaging in healthy lifestyle activities. Despite this, current evidence based models of diabetes education do not explicitly incorporate the concept of perceived barriers. A model of risk factor reduction that incorporates ‘perceived barriers’ is proposed. Conclusion: Although further research is required, current approaches to risk factor reduction in type 2 diabetes could be enhanced by identification and goal setting to reduce an individual’s perceived barriers.

The effect of a high fat meal on postprandial arterial stiffness in men with obesity and type 2 diabetes

Context: Postprandial dysmetabolism is emerging as an important cardiovascular risk factor. Augmentation index (AIx) is a measure of systemic arterial stiffness and independently predicts cardiovascular outcome. Objective: The objective of this study was to assess the effect of a standardized high-fat meal on metabolic parameters and AIx in 1) lean, 2) obese nondiabetic, and 3) subjects with type 2 diabetes mellitus (T2DM). Design and Setting: Male subjects (lean, n = 8; obese, n = 10; and T2DM, n = 10) were studied for 6 h after a high-fat meal and water control. Glucose, insulin, triglycerides, and AIx (radial applanation tonometry) were measured serially to determine the incremental area under the curve (iAUC). Results: AIx decreased in all three groups after a high-fat meal. A greater overall postprandial reduction in AIx was seen in lean and T2DM compared with obese subjects (iAUC, 2251 +/- 1204, 2764 +/- 1102, and 1187 +/- 429% . min, respectively; P < 0.05). The time to return to baseline AIx was significantly delayed in subjects with T2DM (297 +/- 68 min) compared with lean subjects (161 +/- 88 min; P < 0.05). There was a significant correlation between iAUC AIx and iAUC triglycerides (r = 0.50; P < 0.05). Conclusions: Obesity is associated with an attenuated overall postprandial decrease in AIx. Subjects with T2DM have a preserved, but significantly prolonged, reduction in AIx after a high-fat meal. The correlation between AIx and triglycerides suggests that postprandial dysmetabolism may impact on vascular dynamics. The markedly different response observed in the obese subjects compared with those with T2DM was unexpected and warrants additional evaluation.

Bariatric surgery or medicine for type 2 diabetes?

Diet and medical treatment are the standard treatment for type 2 diabetes. In obese subjects with type 2 diabetes, bariatric surgery is effective in resolving diabetes. Two clinical trials comparing bariatric surgery to medical treatment were evaluated. Both the Surgical Treatment And Medications Potentially Eradicate Diabetes Efficiently (STAMPEDE) trial (laparoscopic Roux-En Y gastric bypass and sleeve gastrectomy) and the DIet and medical therapy versus BAriatric SurgerY in type 2 diabetes (DIBASY) trial (laparoscopic gastric bypass and biliopancreatic-diversion) showed that surgery was more effective than medical care in resolving or managing type 2 diabetes. Larger studies, or a compilation of studies, are needed to determine whether one of these procedures is better, or if they are all similarly effective, and this should also be weighed against the risk of the operations.