Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph

Background Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. Methods In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. Results The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. Conclusion With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.

Predicting miRNA–disease associations using improved random walk with restart and integrating multiple similarities

Predicting beneficial and valuable miRNA–disease associations (MDAs) by doing biological laboratory experiments is costly and time-consuming. Proposing a forceful and meaningful computational method for predicting MDAs is essential and captivated many computer scientists in recent years. In this paper, we proposed a new computational method to predict miRNA–disease associations using improved random walk with restart and integrating multiple similarities (RWRMMDA). We used a WKNKN algorithm as a pre-processing step to solve the problem of sparsity and incompletion of data to reduce the negative impact of a large number of missing associations. Two heterogeneous networks in disease and miRNA spaces were built by integrating multiple similarity networks, respectively, and different walk probabilities could be designated to each linked neighbor node of the disease or miRNA node in line with its degree in respective networks. Finally, an improve extended random walk with restart algorithm based on miRNA similarity-based and disease similarity-based heterogeneous networks was used to calculate miRNA–disease association prediction probabilities. The experiments showed that our proposed method achieved a momentous performance with Global LOOCV AUC (Area Under Roc Curve) and AUPR (Area Under Precision-Recall Curve) values of 0.9882 and 0.9066, respectively. And the best AUC and AUPR values under fivefold cross-validation of 0.9855 and 0.8642 which are proven by statistical tests, respectively. In comparison with other previous related methods, it outperformed than NTSHMDA, PMFMDA, IMCMDA and MCLPMDA methods in both AUC and AUPR values. In case studies of Breast Neoplasms, Carcinoma Hepatocellular and Stomach Neoplasms diseases, it inferred 1, 12 and 7 new associations out of top 40 predicted associated miRNAs for each disease, respectively. All of these new inferred associations have been confirmed in different databases or literatures.

Ancient and modern geochemical signatures in the 13,500-year sedimentary record of Lake Cadagno

Although lake sediments are globally important organic carbon sinks and therefore important habitats for deep microbial life, the deep lacustrine biosphere has thus far been little studied compared to its marine counterpart. To investigate the impact of the underexplored deep lacustrine biosphere on the sediment geochemical environment and vice versa, we performed a comprehensive microbiological and geochemical characterization of a sedimentary sequence from Lake Cadagno covering its entire environmental history since formation following glacial retreat. We found that both geochemical gradients and microbial community shifts across the ~13.5 kyr subsurface sedimentary record reflect redox changes in the lake, going from oxic to anoxic and sulfidic. Most microbial activity occurs within the top 40 cm of sediment, where millimolar sulfate concentrations diffusing in from the bottom water are completely consumed. In deeper sediment layers, organic carbon remineralization is much slower but microorganisms nonetheless subsist on fermentation, sulfur cycling, metal reduction, and methanogenesis. The most surprising finding was the presence of a deep, oxidizing groundwater source. This water source generates an inverse redox gradient at the bottom of the sedimentary sequence and could contribute to the remineralization of organic matter sequestered in the energy-limited deep subsurface.

Measuring Quality of Public Hospitals in Croatia Using a Multi-Criteria Approach

Quality of public hospital services presents one of the most important aspects of public health in general. A significant number of health services are delivered due to public hospitals. Under the World Bank program “Improving Quality and Efficiency of Health Services: Program for Results”, the competent bodies in Croatia aimed to identify the top 40% best-performing public acute hospitals in Croatia, based on a clinical audit in the preceding 12 months. This paper presents how this goal was achieved, using a multi-criteria decision-making (MCDM) approach. A MCDM approach was selected due to the multidimensionality and complexity of healthcare performance and service quality. We aimed to develop a methodology for ranking top-performing hospitals at the national level. We chose the composite indicator methodology, combined with the analytic hierarchy process (AHP) as a tool for determining weights for aggregation of individual indicators. The study looked at three clinical entities: acute myocardial infarction, cerebrovascular insult, and antimicrobial prophylaxis in colorectal surgery. Indicators for each entity were evidence-based, following the national guidelines, but limited by availability of data. The clinical audit and databases of competent administrative bodies were used as sources of data. The problem investigated in this paper has a significant impact at the strategic (national) level. Even though the AHP has already been applied in the public health domain, to the best of our knowledge, this is the first application of the AHP in combination with composite indicators for hospital ranking at a national level. The AHP enabled participation of experts from the audited hospitals in the assessment of indicator weights. Results show that composite indicators can be successfully implemented for acute hospital evaluation using the AHP methodology: (1) the AHP supported a flexible structuring of the problem; (2) the resulting complexity of pairwise comparisons was appropriate for the experts (consistency ratios were under 0.1); (3) using the AHP approach enabled a successful aggregation of different opinions into group priorities; (4) the developed methodology was robust and enabled identifying the top 40% ranking best-performing public acute hospitals in Croatia combining 20 criteria within three entities, based on input from 36 clinical experts. The proposed methodology can be useful to other researchers for assessment of healthcare quality at the strategic level.

Competitive Advantage and Risk: Impact on Indian Stock Market

The current study examines the performance of top 40 companies based on market capitalization for the period of 5 years (2015-2019). Competitive advantage is measured using asset turnover ratio and profit margin and risk is measured using financial leverage. Book to market ratio is used as a measure of market performance of the firms. The results indicate that profit margin has the most significant impact on the market performance in the Indian stock market.

Using Big Data to Assess Legitimacy of Plastic Surgery Information on Social Media

Background The proliferation of social media in Plastic Surgery has posed significant difficulties for the public in determining legitimacy of information. In this work, we propose a system based on social network analysis (SNA) to assess the legitimacy of contributors of information within a Plastic Surgery community using academic Plastic Surgery and one social media outlet as a model. Objectives The aim of this study was to quantify the centrality of individual or group accounts on Plastic Surgery social media. Methods To develop the model, we chose one high-fidelity, active, and legitimate source account in academic Plastic Surgery (@psrc1955, the Plastic Surgery Research Council) on one social media outlet (Instagram, Facebook, Menlo Park, CA, USA). We then recorded all follower-following relationships between accounts and used Gephi (https://gephi.org/) to compute five different centrality metrics for each contributor within the network. Results We identified 64,737 unique users and 116,439 unique follower-followed relationships within the academic Plastic Surgery community. Among the metrics assessed, the in-degree centrality metric is the gold standard for SNA, hence we designated this metric as the Centrality Factor (CF). Stratification of 1000 accounts by CF demonstrated that all of the top 40 accounts were affiliated with a Plastic Surgery residency program, a board-certified academic plastic surgeon, a professional society, or a peer-reviewed journal. None of the accounts in the top decile belonged to a non-plastic surgeon or non-physician, however, this increased significantly beyond the 50 th percentile. Conclusions This study took a data-driven approach to identifying and vetting a core group of interconnected accounts within one Plastic Surgery sub-community for the purposes of determining legitimate sources of information.

Nanyang Business School- Mô hình đào tạo quốc tế đáng nghiên cứu

Trường Kinh doanh Nanyang (Nanyang Business School- NBS) luôn thuộc top 40 các chương trình đào tạo Thạc sĩ Quản trị Kinh doanh(Master of Business Administration), trong khi Đại học Công nghệ Nanyang (Nanyang Technological University- NTU) cũng luôn được xếp trong top 20 trên thế giới. Mô hình đào tạo quốc tế này cần được phân tích và học hỏi.

Sentiment analysis of popular-music references to automobiles, 1950s to 2010s

In recent years, there has been a scholarly debate regarding the decrease in automobile-related mobility indicators (car ownership, driving license holding, VMT, etc.). Broadly speaking, two theories have been put forward to explain this trend: (1) economic factors whose impacts are well-understood in principle, but whose occurrence among young adults as a demographic sub-group had been overlooked, and (2) less well-understood shifts in cultural mores, values and sentiment towards the automobile. This second theory is devilishly difficult to study, due primarily to limitations in standard data resources such as the National Household Travel Survey and international peer datasets. In this study we first compiled a database of lyrics to popular music songs from 1956 to 2015 (defined by inclusion in the annual “top 40”), and subsequently identified references to automobiles within this corpus. We then evaluated whether there is support for theory #2 above within popular music, by looking at changes from the 1950s to the 2010s. We demonstrate that the frequency of references to automobility tended for many years to increase over time, however there has more recently been a decline after the late 2000s (decade). In terms of the sentiment of popular music lyrics that reference automobiles, our results are mixed as to whether the references are becoming increasingly positive or negative (machine analysis suggests increasing negativity, while human analysis did not find a significant association), however a consistent observation is that sentiment of automobile references have over time become more positive relative to sentiment of song lyrics overall. We also show that sentiment towards automobile references differs systematically by genre, e.g. automobile references within ‘Rock’ lyrics are in general more negative than similar references to cars in other music genres). The data generated on this project have been archived and made available open access for use by future researchers; details are in the full paper.