In 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.
The notion of self-similarity has been shown to apply to wide-area and local-area network traffic. We show evidence that the subset of network traffic that is due to World Wide Web (WWW) transfers ...can show characteristics that are consistent with self-similarity, and we present a hypothesized explanation for that self-similarity. Using a set of traces of actual user executions of NCSA Mosaic, we examine the dependence structure of WWW traffic. First, we show evidence that WWW traffic exhibits behavior that is consistent with self-similar traffic models. Then we show that the self-similarity in such traffic can be explained based on the underlying distributions of WWW document sizes, the effects of caching and user preference in file transfer, the effect of user "think time", and the superimposition of many such transfers in a local-area network. To do this, we rely on empirically measured distributions both from client traces and from data independently collected at WWW servers.
Stereotyped signals can be a fast, effective means of communicating danger, but animals assessing predation risk must often use more variable incidental cues. Red eyed-treefrog,
Agalychnis ...callidryas,
embryos hatch prematurely to escape from egg predators, cued by vibrations in attacks, but benign rain generates vibrations with overlapping properties. Facing high false-alarm costs, embryos use multiple vibration properties to inform hatching, including temporal pattern elements such as pulse durations and inter-pulse intervals. However, measures of snake and rain vibration as simple pulse-interval patterns are a poor match to embryo behavior. We used vibration playbacks to assess if embryos use a second level of temporal pattern, long gaps within a rhythmic pattern, as indicators of risks. Long vibration-free periods are common during snake attacks but absent from hard rain. Long gaps after a few initial vibrations increase the hatching response to a subsequent vibration series. Moreover, vibration patterns as short as three pulses, separated by long periods of silence, can induce as much hatching as rhythmic pulse series with five times more vibration. Embryos can retain information that increases hatching over at least 45 s of silence. This work highlights that embryo behavior is contextually modulated in complex ways. Identical vibration pulses, pulse groups, and periods of silence can be treated as risk cues in some contexts and not in others. Embryos employ a multi-faceted decision-making process to effectively distinguish between risk cues and benign stimuli.
White adipose tissue plays an important role in physiological homeostasis and metabolic disease. Different fat depots have distinct metabolic and inflammatory profiles and are differentially ...associated with disease risk. It is unclear whether these differences are intrinsic to the pre-differentiated stage. Using single-cell RNA sequencing, a unique network methodology and a data integration technique, we predict metabolic phenotypes in differentiating cells. Single-cell RNA-seq profiles of human preadipocytes during adipogenesis in vitro identifies at least two distinct classes of subcutaneous white adipocytes. These differences in gene expression are separate from the process of browning and beiging. Using a systems biology approach, we identify a new network of zinc-finger proteins that are expressed in one class of preadipocytes and is potentially involved in regulating adipogenesis. Our findings gain a deeper understanding of both the heterogeneity of white adipocytes and their link to normal metabolism and disease.
Computer systems are increasingly driven by workloads that reflect large-scale social behavior, such as rapid changes in the popularity of media items like videos. Capacity planners and system ...designers must plan for rapid, massive changes in workloads when such social behavior is a factor. In this paper we make two contributions intended to assist in the design and provisioning of such systems.We analyze an extensive dataset consisting of the daily access counts of hundreds of thousands of YouTube videos. In this dataset, we find that there are two types of videos: those that show rapid changes in popularity, and those that are consistently popular over long time periods. We call these two types rarely-accessed and frequently-accessed videos, respectively. We observe that most of the videos in our data set clearly fall in one of these two types. In this work, we study the frequently-accessed videos by asking two questions: first, is there a relatively simple model that can describe its daily access patterns? And second, can we use this simple model to predict the number of accesses that a video will have in the near future, as a tool for capacity planning? To answer these questions we develop a framework for characterization and forecasting of access patterns. We show that for frequently-accessed videos, daily access patterns can be extracted via principal component analysis, and used efficiently for forecasting.
Improving accuracy in genetic studies would greatly accelerate understanding the genetic basis of complex diseases. One approach to achieve such an improvement for risk variants identified by the ...genome wide association study (GWAS) approach is to incorporate previously known biology when screening variants across the genome. We developed a simple approach for improving the prioritization of candidate disease genes that incorporates a network diffusion of scores from known disease genes using a protein network and a novel integration with GWAS risk scores, and tested this approach on a large Alzheimer disease (AD) GWAS dataset. Using a statistical bootstrap approach, we cross-validated the method and for the first time showed that a network approach improves the expected replication rates in GWAS studies. Several novel AD genes were predicted including CR2, SHARPIN, and PTPN2. Our re-prioritized results are enriched for established known AD-associated biological pathways including inflammation, immune response, and metabolism, whereas standard non-prioritized results were not. Our findings support a strategy of considering network information when investigating genetic risk factors.
Identifying and understanding the functional role of genetic risk factors for Alzheimer disease (AD) has been complicated by the variability of genetic influences across brain regions and confounding ...with age-related neurodegeneration.
A gene co-expression network was constructed using data obtained from the Allen Brain Atlas for multiple brain regions (cerebral cortex, cerebellum, and brain stem) in six individuals. Gene network analyses were seeded with 52 reproducible (i.e., established) AD (RAD) genes. Genome-wide association study summary data were integrated with the gene co-expression results and phenotypic information (i.e., memory and aging-related outcomes) from gene knockout studies in Drosophila to generate rankings for other genes that may have a role in AD.
We found that co-expression of the RAD genes is strongest in the cortical regions where neurodegeneration due to AD is most severe. There was significant evidence for two novel AD-related genes including EPS8 (FDR p = 8.77 × 10
) and HSPA2 (FDR p = 0.245).
Our findings indicate that AD-related risk factors are potentially associated with brain region-specific effects on gene expression that can be detected using a gene network approach.
NETWORKING 2010 Crovella, Mark; Feeney, Laura Marie; Rubenstein, Dan ...
2010, 2010-04-26, Letnik:
6091
eBook, Conference Proceeding, Book
Recenzirano
This book constitutes the refereed proceedings of the 9th IFIP-TC6 Networking Conference, Networking 2010. Papers were solicited in three broad topic areas: applications and services, network ...technologies, and internet design. All papers were considered on their merits by a uni?ed Technical ProgramCommittee (TPC); there was no attempt to enforce a quota among topic areas. We believe the resulting program is an excellentrepresentationofthebreadthofrecentadvancesinnetworkingresearch. This year, the conference received 101 full paper submissions from 23 co- trieson?vecontinents,re?ectingastrongdiversityinthenetworkingcommunity. Similarly, the 92 members of the TPC are from 21 countries and include a mix of academic, industry, and governmental a?liations. The TPC members, aided by some 50 external reviewers, provided a total of 470 reviews and follow-up discussions totaling more than 200 messages. The ?nal selections were made at a TPC meeting hosted by Columbia University in New York City, with both in-person and remote participation. In total, authors of accepted papers have academic and industry a?liations in 15 countries. We ?nally selected 24papers for presentationduring the conference technical sessions. A small number of papers were assigned a shepherd from the TPC to assist in paper revision. These statistics represent an acceptance rate of just under 24%, comparable to that of previous years. The TPC also identi?ed several papers that re?ect particularly promising early results; these papers were selected for presentation as work-in-progress papers and are identi?ed as such in the proceedings.
Prediction of protein-protein interactions (PPIs) commonly involves a significant computational component. Rapid recent advances in the power of computational methods for protein interaction ...prediction motivate a review of the state-of-the-art. We review the major approaches, organized according to the primary source of data utilized: protein sequence, protein structure, and protein co-abundance. The advent of deep learning (DL) has brought with it significant advances in interaction prediction, and we show how DL is used for each source data type. We review the literature taxonomically, present example case studies in each category, and conclude with observations about the strengths and weaknesses of machine learning methods in the context of the principal sources of data for protein interaction prediction.
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