Herb Target Prediction Based on Representation Learning of Symptom related Heterogeneous Network.

Traditional Chinese Medicine (TCM) has received increasing attention as a complementary approach or alternative to modern medicine. However, experimental methods for identifying novel targets of TCM herbs heavily relied on the current available herb-compound-target relationships. In this work, we present an Herb-Target Interaction Network (HTINet) approach, a novel network integration pipeline for herb-target prediction mainly relying on the symptom related associations. HTINet focuses on capturing the low-dimensional feature vectors for both herbs and proteins by network embedding, which incorporate the topological properties of nodes across multi-layered heterogeneous network, and then performs supervised learning based on these low-dimensional feature representations. HTINet obtains performance improvement over a well-established random walk based herb-target prediction method. Furthermore, we have manually validated several predicted herb-target interactions from independent literatures. These results indicate that HTINet can be used to integrate heterogeneous information to predict novel herb-target interactions

Is Crime Contagious?

endogenous effects, social multiplier, arrests, social experiment

Is Crime Contagious?

We test the hypothesis that criminal behavior is “contagious” – or susceptible to what economists term “endogenous effects” – by examining the extent to which lower local-area crime rates decrease arrest rates among individuals. Using data from the Moving to Opportunity (MTO) randomized housing-mobility experiment, in operation since 1994 in five U.S. cities, we exploit the fact that the effect of treatment group assignment yields different types of neighborhood changes across the five demonstration sites and use treatment-site interactions to instrument for measures of post-randomization neighborhood crime rates as well as neighborhood poverty or racial segregation in analysis of individual arrest outcomes. We find no evidence that violence is contagious; neighborhood racial segregation appears to be the most important explanation for across-neighborhood variation in arrests for violent crimes. Our only evidence for contagion comes with less serious crimes. Some estimates suggest an effect for males, but these results are imprecise. We also find evidence that young males are more likely to engage in property crimes when violent crimes are relatively more prevalent within the community. These findings are consistent with a “resource swamping” model in which increases in the prevalence of more serious crimes dilutes the police resources available for deterring less serious crimes.endogenous effects, social multiplier, arrests, social experiment

City-wide Analysis of Electronic Health Records Reveals Gender and Age Biases in the Administration of Known Drug-Drug Interactions

The occurrence of drug-drug-interactions (DDI) from multiple drug dispensations is a serious problem, both for individuals and health-care systems, since patients with complications due to DDI are likely to reenter the system at a costlier level. We present a large-scale longitudinal study (18 months) of the DDI phenomenon at the primary- and secondary-care level using electronic health records (EHR) from the city of Blumenau in Southern Brazil (pop. $\approx 340,000$). We found that 181 distinct drug pairs known to interact were dispensed concomitantly to 12\% of the patients in the city's public health-care system. Further, 4\% of the patients were dispensed drug pairs that are likely to result in major adverse drug reactions (ADR)---with costs estimated to be much larger than previously reported in smaller studies. The large-scale analysis reveals that women have a 60\% increased risk of DDI as compared to men; the increase becomes 90\% when considering only DDI known to lead to major ADR. Furthermore, DDI risk increases substantially with age; patients aged 70-79 years have a 34\% risk of DDI when they are dispensed two or more drugs concomitantly. Interestingly, a statistical null model demonstrates that age- and female-specific risks from increased polypharmacy fail by far to explain the observed DDI risks in those populations, suggesting unknown social or biological causes. We also provide a network visualization of drugs and demographic factors that characterize the DDI phenomenon and demonstrate that accurate DDI prediction can be included in healthcare and public-health management, to reduce DDI-related ADR and costs

Why Students Drop Out of School: A Review of 25 Years of Research

Reviews research on the underlying causes of the high school dropout crisis -- individual and institutional characteristics that predict whether a student is likely to drop out of high school. Discusses student engagement, deviance, and other models

Predicting adverse drug reactions of combined medication from heterogeneous pharmacologic databases

© 2018 The Author(s). Background: Early and accurate identification of potential adverse drug reactions (ADRs) for combined medication is vital for public health. Existing methods either rely on expensive wet-lab experiments or detecting existing associations from related records. Thus, they inevitably suffer under-reporting, delays in reporting, and inability to detect ADRs for new and rare drugs. The current application of machine learning methods is severely impeded by the lack of proper drug representation and credible negative samples. Therefore, a method to represent drugs properly and to select credible negative samples becomes vital in applying machine learning methods to this problem. Results: In this work, we propose a machine learning method to predict ADRs of combined medication from pharmacologic databases by building up highly-credible negative samples (HCNS-ADR). Specifically, we fuse heterogeneous information from different databases and represent each drug as a multi-dimensional vector according to its chemical substructures, target proteins, substituents, and related pathways first. Then, a drug-pair vector is obtained by appending the vector of one drug to the other. Next, we construct a drug-disease-gene network and devise a scoring method to measure the interaction probability of every drug pair via network analysis. Drug pairs with lower interaction probability are preferentially selected as negative samples. Following that, the validated positive samples and the selected credible negative samples are projected into a lower-dimensional space using the principal component analysis. Finally, a classifier is built for each ADR using its positive and negative samples with reduced dimensions. The performance of the proposed method is evaluated on simulative prediction for 1276 ADRs and 1048 drugs, comparing using four machine learning algorithms and with two baseline approaches. Extensive experiments show that the proposed way to represent drugs characterizes drugs accurately. With highly-credible negative samples selected by HCNS-ADR, the four machine learning algorithms achieve significant performance improvements. HCNS-ADR is also shown to be able to predict both known and novel drug-drug-ADR associations, outperforming two other baseline approaches significantly. Conclusions: The results demonstrate that integration of different drug properties to represent drugs are valuable for ADR prediction of combined medication and the selection of highly-credible negative samples can significantly improve the prediction performance

How Does Household Income Affect Child Personality Traits and Behaviors?

Existing research has investigated the effect of early childhood educational interventions on the child's later-life outcomes. These studies have found limited impact of supplementary programs on children's cognitive skills, but sustained effects on personality traits. We examine how a positive change in unearned household income affects children's emotional and behavioral health and personality traits. Our results indicate that there are large beneficial effects of improved household financial wellbeing on children's emotional and behavioral health and positive personality trait development. Moreover, we find that these effects are most pronounced for children who are lagging behind their peers in these measures before the intervention. Increasing household incomes reduce differences across adolescents with different levels of initial emotional-behavioral symptoms and personality traits. We also examine potential channels through which the increased household income may contribute to these positive changes. Parenting and relationships within the family appear to be an important mechanism. We also find evidence that a sub-sample of the population moves to census tracts with better income levels and educational attainment

IST Austria Thesis

Expression of genes is a fundamental molecular phenotype that is subject to evolution by different types of mutations. Both the rate and the effect of mutations may depend on the DNA sequence context of a particular gene or a particular promoter sequence. In this thesis I investigate the nature of this dependence using simple genetic systems in Escherichia coli. With these systems I explore the evolution of constitutive gene expression from random starting sequences at different loci on the chromosome and at different locations in sequence space. First, I dissect chromosomal neighborhood effects that underlie locus-dependent differences in the potential of a gene under selection to become more highly expressed. Next, I find that the effects of point mutations in promoter sequences are dependent on sequence context, and that an existing energy matrix model performs poorly in predicting relative expression of unrelated sequences. Finally, I show that a substantial fraction of random sequences contain functional promoters and I present an extended thermodynamic model that predicts promoter strength in full sequence space. Taken together, these results provide new insights and guides on how to integrate information on sequence context to improve our qualitative and quantitative understanding of bacterial gene expression, with implications for rapid evolution of drug resistance, de novo evolution of genes, and horizontal gene transfer

Graph Representation Learning in Biomedicine

Biomedical networks are universal descriptors of systems of interacting elements, from protein interactions to disease networks, all the way to healthcare systems and scientific knowledge. With the remarkable success of representation learning in providing powerful predictions and insights, we have witnessed a rapid expansion of representation learning techniques into modeling, analyzing, and learning with such networks. In this review, we put forward an observation that long-standing principles of networks in biology and medicine -- while often unspoken in machine learning research -- can provide the conceptual grounding for representation learning, explain its current successes and limitations, and inform future advances. We synthesize a spectrum of algorithmic approaches that, at their core, leverage graph topology to embed networks into compact vector spaces, and capture the breadth of ways in which representation learning is proving useful. Areas of profound impact include identifying variants underlying complex traits, disentangling behaviors of single cells and their effects on health, assisting in diagnosis and treatment of patients, and developing safe and effective medicines