A two-step drug repositioning method based on a protein-protein interaction network of genes shared by two diseases and the similarity of drugs

The present study proposed a two-step drug repositioning method based on a protein-protein interaction (PPI) network of two diseases and the similarity of the drugs prescribed for one of the two. In the proposed method, first, lists of disease related genes were obtained from a meta-database called Genotator. Then genes shared by a pair of diseases were sought. At the first step of the method, if a drug having its target(s) in the PPI network, the drug was deemed a repositioning candidate. Because targets of many drugs are still unknown, the similarities between the prescribed drugs for a specific disease were used to infer repositioning candidates at the second step. As a first attempt, we applied the proposed method to four different types of diseases: hypertension, diabetes mellitus, Crohn disease, and autism. Some repositioning candidates were found both at the first and second steps.     

Modelling developmental instability in relation to individual fitness: a fully Bayesian latent variable model approach

Since the influential paper by Palmer and Strobeck in 1986, the statistical analysis of fluctuating asymmetry and developmental stability has received much attention. Most studies deal with one of the following four difficulties: (i) correcting for bias in asymmetry estimates due to measurement error; (ii) quantifying sampling error in the estimation of individual developmental stability using individual asymmetry; (iii) the detection of directional asymmetry and antisymmetry; and (iv) combining data from several traits. Yet, few studies have focused on statistical properties of estimating a relationship between individual developmental stability and other factors (e.g. fitness). In this paper I introduce a fully Bayesian model in which the unobservable individual developmental stability is treated as a latent variable. The latter is then related to individual fitness. I show by means of the analysis of simulated data that this approach has several advantages over traditional techniques. First, the method provides unbiased (but slightly less accurate) estimates of slopes between developmental stability and fitness taking all sources of error into account. Secondly, it allows proper investigation of non‐linear associations. Finally, the model allows unbiased estimation of unobserved fitness of individuals that have been measured on left and right side.