Tel Aviv University

About: Tel Aviv University is a education organization based out in Tel Aviv, Israel. It is known for research contribution in the topics: Population & Medicine. The organization has 47791 authors who have published 115959 publications receiving 3904391 citations. The organization is also known as: TAU & Universiṭat Tel-Aviv.

Associating Genes and Protein Complexes with Disease via Network Propagation

Abstract: A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE's predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.

Epidemiology of gestational diabetes mellitus and its association with Type 2 diabetes.

Abstract: Gestational diabetes (GDM) is defined as carbohydrate intolerance that begins or is first recognized during pregnancy. Although it is a well-known cause of pregnancy complications, its epidemiology has not been studied systematically. Our aim was to review the recent data on the epidemiology of GDM, and to describe the close relationship of GDM to prediabetic states, in addition to the risk of future deterioration in insulin resistance and development of overt Type 2 diabetes. We found that differences in screening programmes and diagnostic criteria make it difficult to compare frequencies of GDM among various populations. Nevertheless, ethnicity has been proven to be an independent risk factor for GDM, which varies in prevalence in direct proportion to the prevalence of Type 2 diabetes in a given population or ethnic group. There are several identifiable predisposing factors for GDM, and in the absence of risk factors, the incidence of GDM is low. Therefore, some authors suggest that selective screening may be cost-effective. Importantly, women with an early diagnosis of GDM, in the first half of pregnancy, represent a high-risk subgroup, with an increased incidence of obstetric complications, recurrent GDM in subsequent pregnancies, and future development of Type 2 diabetes. Other factors that place women with GDM at increased risk of Type 2 diabetes are obesity and need for insulin for glycaemic control. Furthermore, hypertensive disorders in pregnancy and afterwards may be more prevalent in women with GDM. We conclude that the epidemiological data suggest an association between several high-risk prediabetic states, GDM, and Type 2 diabetes. Insulin resistance is suggested as a pathogenic linkage. It is possible that improving insulin sensitivity with diet, exercise and drugs such as metformin may reduce the risk of diabetes in individuals at high risk, such as women with polycystic ovary syndrome, impaired glucose tolerance, and a history of GDM. Large controlled studies are needed to clarify this issue and to develop appropriate diabetic prevention strategies that address the potentially modifiable risk factors.

Fractional Fourier transforms and their optical implementation. II

Abstract: The linear transform kernel for fractional Fourier transforms is derived. The spatial resolution and the space–bandwidth product for propagation in graded-index media are discussed in direct relation to fractional Fourier transforms, and numerical examples are presented. It is shown how fractional Fourier transforms can be made the basis of generalized spatial filtering systems: Several filters are interleaved between several fractional transform stages, thereby increasing the number of degrees of freedom available in filter synthesis.