Going the distance for protein function prediction: a new distance metric for protein interaction networks

Due to an error introduced in the production process, the x-axes in the first panels of Figure 1 and Figure 7 are not formatted correctly. The correct Figure 1 can be viewed here: http://dx.doi.org/10.1371/annotation/343bf260-f6ff-48a2-93b2-3cc79af518a9In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.MC, HZ, NMD and LJC were supported in part by National Institutes of Health (NIH) R01 grant GM080330. JP was supported in part by NIH grant R01 HD058880. This material is based upon work supported by the National Science Foundation under grant numbers CNS-0905565, CNS-1018266, CNS-1012910, and CNS-1117039, and supported by the Army Research Office under grant W911NF-11-1-0227 (to MEC). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Probabilistic Hierarchical Forecasting with Deep Poisson Mixtures

Hierarchical forecasting problems arise when time series have a natural group structure, and predictions at multiple levels of aggregation and disaggregation across the groups are needed. In such problems, it is often desired to satisfy the aggregation constraints in a given hierarchy, referred to as hierarchical coherence in the literature. Maintaining hierarchical coherence while producing accurate forecasts can be a challenging problem, especially in the case of probabilistic forecasting. We present a novel method capable of accurate and coherent probabilistic forecasts for hierarchical time series. We call it Deep Poisson Mixture Network (DPMN). It relies on the combination of neural networks and a statistical model for the joint distribution of the hierarchical multivariate time series structure. By construction, the model guarantees hierarchical coherence and provides simple rules for aggregation and disaggregation of the predictive distributions. We perform an extensive empirical evaluation comparing the DPMN to other state-of-the-art methods which produce hierarchically coherent probabilistic forecasts on multiple public datasets. Compared to existing coherent probabilistic models, we obtained a relative improvement in the overall Continuous Ranked Probability Score (CRPS) of 11.8% on Australian domestic tourism data, and 8.1% on the Favorita grocery sales dataset.Comment: Probabilistic Hierarchical Forecasting, Neural Networks, Poisson Mixtures, Preprint submitted to IJ

An evaluation of EQ-5D-3L health utility scores using five country-specific tariffs in a rural population aged 45-69 years in Hua county, Henan province, China.

BACKGROUND: This study aims to compare the performance of the recently developed Chinese (city) tariff of the EQ-5D-3L against the UK, US, Japanese and Korean tariffs in a general rural population in China. METHODS: From November 2015 to September 2016, 12,085 permanent residents aged 45-69 from 257 villages randomly selected from Hua County, Henan Province, China, were interviewed using EQ-5D-3L, and a one-on-one questionnaire investigation was used to collect data on factors associated with HRQOL. The health utility scores were calculated using the UK, US, Japanese, Korean and Chinese (city) tariffs. The agreement, known-groups validity and sensitivity of these five tariffs were evaluated. Transition scores for pairs of observed EQ-5D-3L health states were calculated and compared. RESULTS: The Korean tariff yielded the highest mean health utility score (0.963), followed by the Chinese (city) (0.948), US (0.943), UK (0.930) and Japanese (0.921) tariffs, but the differences in the scores of any two tariffs did not exceed the MCID. The Chinese (city) tariff showed higher ICC values (ICCs> 0.89, 95% CI:0.755-0.964) and narrower limits of agreement (0.099-0.167) than the Korean tariff [(ICCs> 0.71, 95% CI:0.451-0.955); (0.146-0.253)]. The Chinese (city) tariff had a higher relative efficiency and effect size statistics in 10 out of 11 variables as compared to the UK, US and Japanese tariffs. The Chinese (city) tariff (0.215) was associated with moderate mean absolute transition scores compared with the UK (0.342), US (0.230), Japanese (0.149) and Korean (0.189) tariffs for 1485 observed pairs of the EQ-5D-3L health states. CONCLUSIONS: Health utility scores derived from the five tariffs differed. The Chinese (city) tariff was the most suitable of these tariffs and was without obvious weakness. We recommend adopting the Chinese (city) tariff when applying EQ-5D-3L to assess quality of life among the elderly in China's agricultural region with socio-economic status similar to Hua County. Results of this study had provided a crucial basis for health surveys, health promotion projects, health intervention trials, and health economic evaluation taking HRQOL as a target in rural areas of China

WHEN SHOULD WE NOT TRANSFER FUNCTIONAL ANNOTATION BETWEEN SEQUENCE PARALOGS?

and [email protected] Current automated computational methods to assign functional labels to unstudied genes often involve transferring annotation from orthologous or paralogous genes, however such genes can evolve divergent functions, making such transfer inappropriate. We consider the problem of determining when it is correct to make such an assignment between paralogs. We construct a benchmark dataset of two types of similar paralogous pairs of genes in the well-studied model organism S. cerevisiae: one set of pairs where single deletion mutants have very similar phenotypes (implying similar functions), and another set of pairs where single deletion mutants have very divergent phenotypes (implying different functions). State of the art methods for this problem will determine the evolutionary history of the paralogs with references to multiple related species. Here, we ask a first and simpler question: we explore to what extent any computational method with access only to data from a single species can solve this problem. We consider divergence data (at both the amino acid and nucleotide levels), and network data (based on the yeast protein-protein interaction network, as captured in BioGRID), and ask if we can extract features from these data that can distinguish between these sets of paralogous gene pairs. We find that the best features come from measures of sequence divergence, however, simple network measures based on degree or centrality or shortest path or diffusion state distance (DSD), or shared neighborhood in the yeast protein-protein interaction (PPI) network also contain some signal. One should, in general, not transfer function if sequence divergence is too high. Further improvements in classification will need to come from more computationally expensive but much more powerful evolutionary methods that incorporate ancestral states and measure evolutionary divergence over multiple species based on evolutionary trees

Analysis of Public Transportation Competitiveness Based on Potential Passenger Travel Intentions: Case Study in Shanghai, China

The primary goal of this paper is to identify the influence factors of public transportation (PT) competitiveness as related to potential passengers through an analysis of travel intentions. The logistic regression model is utilized to analyze PT travel intentions of people who commute daily by car in Shanghai, China. Through comparing the service quality of PT and of car from five aspects (i.e., comfort, timeliness, reliability, economics, and safety), the significant influence factors are identified. The results reveal that the difference in service quality between PT travel and car travel is insignificant for safety and convenience, while comfort, reliability, and economics matter most to the willingness to travel via PT. In addition, increasing the cost of car travel and improving the service quality of PT are both helpful in attracting car users to switch travel mode and enhancing PT competitiveness. The findings and suggestions will provide support to decisions in PT development policy making

Decoding the role of long non-coding RNAs in periodontitis: A comprehensive review

Periodontitis is an inflammatory disease characterized by the pathological loss of alveolar bone and the adjacent periodontal ligament. It is considered a disease that imposes a substantial health burden, with an incidence rate of 20–50%. The etiology of periodontitis is multifactorial, with genetic factors accounting for approximately half of severe cases. Studies have revealed that long non-coding RNAs (lncRNAs) play a pivotal role in periodontitis pathogenesis. Accumulating evidence suggests that lncRNAs have distinct regulatory mechanisms, enabling them to control numerous vital processes in periodontal cells, including osteogenic differentiation, inflammation, proliferation, apoptosis, and autophagy. In this review, we summarize the diverse roles of lncRNAs in the pathogenesis of periodontitis, shedding light on the underlying mechanisms of disease development. By highlighting the potential of lncRNAs as biomarkers and therapeutic targets, this review offers a new perspective on the diagnosis and treatment of periodontitis, paving the way for further investigation into the field of lncRNA-based therapeutics

Ўсё бы пела ды гуляла

Ўсё бы пела ды гуляла, / Ўсё бы весялілася. / Ўсё б ляжала пад табой, / Ўсё бы шавялілася

Observing the Characteristics of Multi-Activity Trip Chain and Its Influencing Mechanism

Understanding mechanism of daily activity trip chain especially multi-activity trip chain is significant for travel demand management and urban redevelopment. This paper explores the multi-activity trip chain behavior from both individual attributes and spatial attributes. Multiactivity trip chain is identified from people’s full-day travel diaries, then categorized into multi-activity intermittent trip chain and continuous trip chain to distinguish the characteristics and influencing factors of different chains. Using resident travel survey data of Xiaoshan District of Hangzhou, China, multinomial logistic regression model, transition probability matrix and activity analysis methods are employed to make analyses. Findings include: 1) making multi-activity trip chain can achieve multiple purposes with less average travel time for each purpose, but public transit is less used. 2) The choices of single or multi-activity trip chain and multi-activity intermittent or continuous trip chain are mainly affected by different individual attributes such as occupation, education, household income, etc. Moreover, household registration, driver’s license, gender, and household car ownership are related to the choice of activity sequence. 3) For typical trip chain with purposes of work, shopping/dining, and home, activity sequence is also obviously influenced by spatial distance between origin and destination, especially for the chain of work-shopping/dining-home