1,721,078 research outputs found
Graph theoretic and stochastic block models integrated with matrix factorization for community detection
No full textIn this work we describe a novel method to integrate graph theoretic and stochastic block models by using matrix factorization for the purposes of data mining interesting patterns. Complex networks represent pairwise patterns of connectivity between nodes and can reveal much information terms of the relationships between entities. Further information on these relationships can be extracted through a careful analysis of the shared communities they coexist with. Here we use the strengths of stochastic block models which are widely used for community detection and are a natural extension of complex networks. However, numerous false positive community affiliations are often identified. We integrate the two types of network with a non negative matrix factorization function. We test and validate our methods against other competing systems on several data sets
Analyzing Social Media Data using Sentiment Mining and Bi-gram Analysis for the Recommendation of YouTube Videos.
Get PDFIn this work we combine sentiment analysis with graph theory to analyze user posts, likes/dislikes on a variety of social media to provide recommendations for YouTube videos. We focus on the topic of climate change/global warming which has caused much alarm and controversy over recent years. Our intention is to recommend informative YouTube videos to those seeking a balanced viewpoint of this area and the key arguments/issues. To this end we analyze Twitter data; Reddit comments and posts; user comments, view statistics and likes/dislikes of YouTube videos. The combination of sentiment analysis with raw statistics and linking users with their posts gives deeper insights into their needs and quest for quality information. Sentiment analysis provides the insights into user likes and dislikes, graph theory provides the linkage patterns and relationships between users, posts and sentiment
Data Mining Open Source Databases for Drug Repositioning using Graph Based Techniques
No full textThe analysis of ‘Big Data’ has great potential in drug discovery; however complications arise in integrating this data in a principled and coherent way. An important statistical tool to manage complexity is that of graph theory, which is as satisfying and attractive to the hard core data analyst as it is to the lay person. The statistician can marvel at the mathematics behind the theory while the lay-person can appreciate the highly visual and graphical information that shows links between objects and their relationships. In this paper we show how graphs and proteomic data are used to facilitate drug discovery
Hidden Markov Models for Surprising Pattern Detection in Discrete Symbol Sequence Data
Get PDFDetecting unusual or interesting patterns in discrete symbol sequences is of great importance. Many domains consist of discrete sequential time-series such as internet traffic, online transactions, cyber-attacks, financial transactions, biological transcription, intensive care data and social sciences data such as career trajectories or residential history. The sequences usually consist of discrete symbols that may form regular patterns or motifs. We use regular expressions to construct the longest repeating sequences and sub-sequences that compose them, we then define these as motifs (which may or may not represent novel patterns). The sequences are now composed of simpler motifs which are used to build Hidden Markov Models models which can capture complex relationships based on location, frequency of occurrence and position. New data that deviates from established motifs either in location of appearance, frequency of appearance, or motif composition may represent patterns that may be different in some way and hence interesting to the user
Perforated diverticulitis: To anastomose or not to anastomose? A systematic review and meta-analysis
Get PDFBackground: No consensus has been reached in the management of perforated diverticulitis. Many surgeons opt for a Hartmann's procedure to avoid the risk of an anastomotic leak. We hypothesise that resection with primary anastomosis is a safe alternative in selected patients. We aim to conduct a systematic review and meta-analysis on the available literature.Methods: Studies that compared emergency Hartmann's with primary anastomosis in perforated left sided colonic diverticulitis were systematically reviewed. The search strategy included all study types that compared primary anastomosis to Hartmann's in perforated diverticulitis and reported on morbidity and mortality. 5
databases (PubMed, MEDLINE via PubMed, OVID, EMBASE via OVID and The Cochrane Collaboration). The Cochrane's Bias Methods Group tool was used to assess the risk of bias and a meta-analysis of the relevant studies was conducted.
Results: The review retrieved 1933 abstracts of which 14 studies (2 RCTs, 4 prospective non-randomised and 8 retrospective non-randomised) with 765 patients in
total, 482 in the Hartmann's group and 283 in the primary anastomosis group, met the inclusion criteria. This showed a significantly lower mortality with primary anastomosis (10.6%) compared to Hartmann's (20.7%) (p=0.0003). Morbidity was also significantly lower (41.8% vs. 51.2%) (p=0.0483). The RR for mortality was 0.92 in favour of primary anastomosis (p=0.0019). The average anastomotic leak rate was 5.9%. Conclusion: Resection and primary anastomosis should be considered as a feasible and safe operative strategy in selected patients with perforated diverticulitis. There is however a paucity of high level evidence and further research is needed
Doctor’s perceptions, expectations and experiences regarding the role of the Pharmacist in hospital settings of Pakistan
Get PDFBackground The inclusion of pharmacist in health care system is essential to ensure optimal patient care. However, with the passage of time, pharmacist’s role has transcended from dispensing, compounding and counting of pills, to more sophisticated clinical duties. Objective To evaluate doctors’ experience, perceptions and expectations regarding pharmacists’ role in Pakistani healthcare settings. Setting All tertiary care hospitals across 26 cities of Pakistan. Method A cross-sectional study using a self-administered questionnaire was carried out targeting doctors practising in Pakistan. The survey was conducted from January to April 2018. Chi square (χ2) test was used to analyse responses of doctors regarding pharmacist’s role in the healthcare system of Pakistan. The associations were considered significant at p value less than 0.05. The study was approved by concerned ethical committee. Main outcome measure Doctors’ experience, perceptions and expectations regarding pharmacists’ role. Results A total of 483 questionnaires were received and analysed (response rate; 87.9%). Most participants (67.5%) reported interaction with pharmacists at least once daily, and that was mostly related to drug availability inquiry (73.7%). 86.7% of doctors expected pharmacists to ensure safe and appropriate use of medicines to patients. 87.6% of doctors expected pharmacists to monitor patient’s response to drug therapy (p < 0.05) and 66.5% expected pharmacists to review patient’s medicines as well as discuss possible amendments to therapy (p < 0.05). Besides, most doctors (84.9%) disagreed with the notion of pharmacists prescribing medicine for patients (p < 0.05). Most participants (81.6%) did not want pharmacists to prescribe independently. Conclusion The study highlights that doctors considered pharmacists as drug information specialists, dispensers, educators and counsellors; however, their expectation of pharmacists performing the clinical role and being involved in direct patient care was limited. They negated the idea of prescription intervention and direct involvement of pharmacists in pharmacotherapy plan for patients. It is imparative to increase doctors’ awareness regarding the role pharmacists could play in Pakistan’s healthcare system. Currently, the clinical role of pharmacists in Pakistan’s healthcare system seems minimal and is seen with scepticism within the community of doctors
A method to explore the connectivity patterns of proteins and drugs for identifying disease communities
No full textDiseases are often caused by defective proteins, these proteins rarely operate in isolation and may have several roles in the cell. Thus over time a defective protein may be involved in several disorders, either directly or indirectly. The multiple roles leads to the concept of a disease module or cluster. This work describes how we generate overlapping clusters from complex networks to explore the dynamic nature of diseases, the genes implicated with them and the drugs used to treat them. Link clustering is vital for community detection as it enables the integration of disparate sources of data and provides a better understanding of community hierarchy and community dynamics than non-link methods. Furthermore, we view not just the genes directly shared between diseases but also indirectly connected genes in the network neighborhood. We use data and information from the STITCH protein and drug interaction databases, OMIM disease database, lists of diseases categorized by MeSH and the drugbank repository. The Gene Ontology, Disease Ontology and KEGG provide biological validity for the disease communities. We demonstrate how the detection of overlapping clusters enables the identification of biologically plausible communities consisting of cooperating proteins. We verify their role in disease with respect to targeting drugs more effectively with expert opinion. We have been able to identify various modules that make sense from a biological and medical perspective and validate drug repositioning candidates with clinicaltrials.gov
The development of a predictive model to identify potential HIV-1 attachment inhibitors
Get PDFDespite the significant progress in managing patients infected with HIV through the development of Highly Active Anti-Retroviral Therapy (HAART), major challenges and opportunities remain to be explored. Of particular interest, is the binding of glycoprotein 120 (gp120) to the primary cellular receptor Cluster of Differentiation 4 (CD4). In this work we describe our two phased computational process to identify useful compounds capable of binding to the gp120 protein for therapeutic purposes. We identified 187 compounds from the literature that conform to active binding sites on these proteins and use these as training/test sets. The data in the form of quantitative structure-activity relationships (QSAR) is downloaded from the ZINC database and transformed using principal components analysis. In the first phase we developed a Radial Basis Function neural network model that identifies potential inhibitors from a virtual screen of a subset of the ZINC database. In the second phase we modelled the top performing compounds using the Discovery Studio docking and screening software. By employing this approach, we identified that those compounds with a LogP value of approx 2-4 performed well in the binding simulations while the lower scoring compounds do not bind well
Influence of rGO/MWCNTs on antifouling properties of nanocomposite membrane: Statistical analysis
No full textThe research aimed to develop a statistical analysis of the antifouling properties of nanocomposite membrane using response surface methodology (RSM) for methyl orange (MO) dye wastewater treatment. Reduced graphene oxide (rGO) and multi-walled carbon nanotubes (MWCNTs) were blended directly into polyvinylidene difluoride (PVDF) membrane. 13 experiment runs were conducted using central composite design (CCD). Nanomaterials concentration and weight ratio of rGO:MWCNTs were manipulated, while the response was normalized flux. Next, the membranes were characterized by pore size, porosity, surface charge, and hydrophilicity. In general, the increase in membrane pore size was mainly due to the addition of nanomaterials. Lastly, the membrane performance was evaluated using dead-end filtration. The best antifouling was achieved by membrane containing 0.023 wt% nanomaterial concentration and 85.36 wt% weight ratio with the highest normalized flux of 0.9132. The antifouling properties of nanocomposite membrane were then modeled statistically using analysis of variance (ANOVA) and regression analysis to identify the data significance. Lastly, a quadratic model equation was rendered and it is found that normalized flux was significantly affected by nanomaterials concentration. The model was proven to be fitted with moderate accuracy based on the F-value (4.09), p-values (0.0468 <0.05), lack of fit F-value (2.81), R2 value (0.7449), and standard deviation (0.0946)
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