Constructing Biological Pathways by a Two-Step Counting Approach

Networks are widely used in biology to represent the relationships between genes and gene functions. In Boolean biological models, it is mainly assumed that there are two states to represent a gene: on-state and off-state. It is typically assumed that the relationship between two genes can be characterized by two kinds of pairwise relationships: similarity and prerequisite. Many approaches have been proposed in the literature to reconstruct biological relationships. In this article, we propose a two-step method to reconstruct the biological pathway when the binary array data have measurement error. For a pair of genes in a sample, the first step of this approach is to assign counting numbers for every relationship and select the relationship with counting number greater than a threshold. The second step is to calculate the asymptotic p-values for hypotheses of possible relationships and select relationships with a large p-value. This new method has the advantages of easy calculation for the counting numbers and simple closed forms for the p-value. The simulation study and real data example show that the two-step counting method can accurately reconstruct the biological pathway and outperform the existing methods. Compared with the other existing methods, this two-step method can provide a more accurate and efficient alternative approach for reconstructing the biological network

The Generalized Bayes Method for High-Dimensional Data Recognition with Applications to Audio Signal Recognition

High-dimensional data recognition problem based on the Gaussian Mixture model has useful applications in many area, such as audio signal recognition, image analysis, and biological evolution. The expectation-maximization algorithm is a popular approach to the derivation of the maximum likelihood estimators of the Gaussian mixture model (GMM). An alternative solution is to adopt a generalized Bayes estimator for parameter estimation. In this study, an estimator based on the generalized Bayes approach is established. A simulation study shows that the proposed approach has a performance competitive to that of the conventional method in high-dimensional Gaussian mixture model recognition. We use a musical data example to illustrate this recognition problem. Suppose that we have audio data of a piece of music and know that the music is from one of four compositions, but we do not know exactly which composition it comes from. The generalized Bayes method shows a higher average recognition rate than the conventional method. This result shows that the generalized Bayes method is a competitor to the conventional method in this real application

MicroRNA, Diabetes Mellitus and Colorectal Cancer

Diabetes mellitus (DM) is an endocrinological disorder that is due to either the pancreas not producing enough insulin, or the body does not respond appropriately to insulin. There are many complications of DM such as retinopathy, nephropathy, and peripheral neuropathy. In addition to these complications, DM was reported to be associated with different cancers. In this review, we discuss the association between DM and colorectal cancer (CRC). CRC is the third most commonly diagnosed cancer worldwide that mostly affects older people, however, its incidence and mortality are rising among young people. We discuss the relationship between DM and CRC based on their common microRNA (miRNA) biomarkers. miRNAs are non-coding RNAs playing important functions in cell differentiation, development, regulation of cell cycle, and apoptosis. miRNAs can inhibit cell proliferation and induce apoptosis in CRC cells. miRNAs also can improve glucose tolerance and insulin sensitivity. Therefore, investigating the common miRNA biomarkers of both DM and CRC can shed a light on how these two diseases are correlated and more understanding of the link between these two diseases can help the prevention of both DM and CRC

Improved confidence estimators for the multivariate normal confidence set. Statistica Sinica 2000

Abstract: Traditionally, the constant coverage probability estimator, confidence coefficient, is used to report the confidence of a multivariate normal confidence set

Anti-NMDA Receptor Encephalitis and Vaccination

Anti-N-methyl-d-aspartate (Anti-NMDA) receptor encephalitis is an acute autoimmune neurological disorder. The cause of this disease is often unknown, and previous studies revealed that it might be caused by a virus, vaccine or tumor. It occurs more often in females than in males. Several cases were reported to be related to vaccination such as the H1N1 vaccine and tetanus/diphtheria/pertussis and polio vaccines. In this study, we reported an anti-NMDA receptor encephalitis case that may be caused by Japanese encephalitis vaccination. To investigate the association between anti-NMDA receptor encephalitis and vaccination, we analyzed the phylogenetic relationship of the microRNAs, which significantly regulate these vaccine viruses or bacteria, and the phylogenetic relationship of these viruses and bacteria. This reveals that anti-NMDA receptor encephalitis may be caused by Japanese encephalitis vaccination, as well as H1N1 vaccination or tetanus/diphtheria/pertussis and polio vaccinations, from the phylogenetic viewpoint

MicroRNAs, Parkinson’s Disease, and Diabetes Mellitus

Parkinson’s disease (PD) is a neurodegenerative disorder that affects 1% of the population over the age of 60. Diabetes Mellitus (DM) is a metabolic disorder that affects approximately 25% of adults over the age of 60. Recent studies showed that DM increases the risk of developing PD. The link between DM and PD has been discussed in the literature in relation to different mechanisms including mitochondrial dysfunction, oxidative stress, and protein aggregation. In this paper, we review the common microRNA (miRNA) biomarkers of both diseases. miRNAs play an important role in cell differentiation, development, the regulation of the cell cycle, and apoptosis. They are also involved in the pathology of many diseases. miRNAs can mediate the insulin pathway and glucose absorption. miRNAs can also regulate PD-related genes. Therefore, exploring the common miRNA biomarkers of both PD and DM can shed a light on how these two diseases are correlated, and targeting miRNAs is a potential therapeutic opportunity for both diseases

Anti-NMDA Receptor Encephalitis and Vaccination

Anti-N-methyl-d-aspartate (Anti-NMDA) receptor encephalitis is an acute autoimmune neurological disorder. The cause of this disease is often unknown, and previous studies revealed that it might be caused by a virus, vaccine or tumor. It occurs more often in females than in males. Several cases were reported to be related to vaccination such as the H1N1 vaccine and tetanus/diphtheria/pertussis and polio vaccines. In this study, we reported an anti-NMDA receptor encephalitis case that may be caused by Japanese encephalitis vaccination. To investigate the association between anti-NMDA receptor encephalitis and vaccination, we analyzed the phylogenetic relationship of the microRNAs, which significantly regulate these vaccine viruses or bacteria, and the phylogenetic relationship of these viruses and bacteria. This reveals that anti-NMDA receptor encephalitis may be caused by Japanese encephalitis vaccination, as well as H1N1 vaccination or tetanus/diphtheria/pertussis and polio vaccinations, from the phylogenetic viewpoint

Biomaterials in Medical Applications

Natural biomaterials are materials extracted from living organisms or their by-products [...

Modified p-values for one-sided testing in restricted parameter spaces

For testing the mean of a normal distribution, the p-value, derived from the uniformly most powerful test, is usually used as evidence against the null hypothesis. However, the p-value only depends on the hypothesis assumption, but not on the bounds of the parameter space. When the parameter space is restricted, the information of the restriction will not be sufficiently utilized if we still use the usual p-value as evidence against the null hypothesis. In this paper, a modified p-value, based on the bounds of the parameter space for one-sided hypothesis testing, is proposed. Theoretical and simulation studies show that the modified p-value has better performance than the usual p-value from theoretical and simulation studies.Hypothesis testing p-value Bayes estimators Restricted parameter space

Coverage probability of prediction intervals for discrete random variables

Prediction interval is a widely used tool in industrial applications to predict the distribution of future observations. The exact minimum coverage probability and the average coverage probability of the conventional prediction interval for a discrete random variable have not been accurately derived in the literature. In this paper, procedures to compute the exact minimum confidence levels and the average confidence levels of the prediction intervals for a discrete random variable are proposed. These procedures are illustrated with examples and real data applications. Based on these procedures, modified prediction intervals with the minimum coverage probability or the average coverage probability close to the nominal level can be constructed.