Drug Research Meets Network Science: Where Are We?
Ontology highlight
ABSTRACT:
SUBMITTER: Recanatini M
PROVIDER: S-EPMC8007104 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Obstructive sleep apnea syndrome (OSAS) is a common clinical condition. The way that OSAS risk factors associate and converge is not a random process. As such, defining OSAS phenotypes fosters personalized patient management and population screening. In this paper, we present a network-based observational, retrospective study on a cohort of 1,371 consecutive OSAS patients and 611 non-OSAS control patients in order to explore the risk factor associations and their correlation with OSAS comorbidities. To this end, we construct the Apnea Patients Network (APN) using patient compatibility relationships according to six objective parameters: age, gender, body mass index (BMI), blood pressure (BP), neck circumference (NC) and the Epworth sleepiness score (ESS). By running targeted network clustering algorithms, we identify eight patient phenotypes and corroborate them with the co-morbidity types. Also, by employing machine learning on the uncovered phenotypes, we derive a classification tree and introduce a computational framework which render the Sleep Apnea Syndrome Score (SASScore); our OSAS score is implemented as an easy-to-use, web-based computer program which requires less than one minute for processing one individual. Our evaluation, performed on a distinct validation database with 231 consecutive patients, reveals that OSAS prediction with SASScore has a significant specificity improvement (an increase of 234%) for only 8.2% sensitivity decrease in comparison with the state-of-the-art score STOP-BANG. The fact that SASScore has bigger specificity makes it appropriate for OSAS screening and risk prediction in big, general populations.
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Project description:BackgroundDrug repurposing is an approach that holds promise for identifying new therapeutic uses for existing drugs. Recently, knowledge graphs have emerged as significant tools for addressing the challenges of drug repurposing. However, there are still major issues with constructing and embedding knowledge graphs.ResultsThis study proposes a two-step method called DrugRep-HeSiaGraph to address these challenges. The method integrates the drug-disease knowledge graph with the application of a heterogeneous siamese neural network. In the first step, a drug-disease knowledge graph named DDKG-V1 is constructed by defining new relationship types, and then numerical vector representations for the nodes are created using the distributional learning method. In the second step, a heterogeneous siamese neural network called HeSiaNet is applied to enrich the embedding of drugs and diseases by bringing them closer in a new unified latent space. Then, it predicts potential drug candidates for diseases. DrugRep-HeSiaGraph achieves impressive performance metrics, including an AUC-ROC of 91.16%, an AUC-PR of 90.32%, an accuracy of 84.63%, a BS of 0.119, and an MCC of 69.31%.ConclusionWe demonstrate the effectiveness of the proposed method in identifying potential drugs for COVID-19 as a case study. In addition, this study shows the role of dipeptidyl peptidase 4 (DPP-4) as a potential receptor for SARS-CoV-2 and the effectiveness of DPP-4 inhibitors in facing COVID-19. This highlights the practical application of the model in addressing real-world challenges in the field of drug repurposing. The code and data for DrugRep-HeSiaGraph are publicly available at https://github.com/CBRC-lab/DrugRep-HeSiaGraph .
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Project description:Biology research is becoming increasingly dependent on large-scale, "big data," networked research initiatives. At the same time, there has been a corresponding effort to expand undergraduate participation in research to benefit student learning and persistence in science. This essay examines the confluence of this trend through eight years of a collaboration within a successful biology research network that explicitly incorporates undergraduates into large-scale scientific research. We draw upon interviews with faculty in this network to consider the interplay of scientific and pedagogical objectives at the heart of this undergraduate-focused network research project. We identify ways that this network has expanded and diversified access to scientific knowledge production for faculty and students and examine a goal conflict that emerged around the dual objectives of mentoring emerging scientists while producing high-quality scientific data for the larger biology community. Based on lessons learned within this network, we provide three recommendations that can support institutions and faculty engaging in networked research projects with undergraduates: (1) establish rigorous protocols to ensure data and database quality, (2) protect personnel time to coordinate network and scientific processes, and (3) select appropriate partners and establish explicit expectations for specific collaborations.
| S-EPMC8693944 | biostudies-literature
Project description:According to a recent survey, ecologists and evolutionary biologists feel that theoretical and empirical research should coexist in a tight feedback loop but believe that the two domains actually interact very little. We evaluate this perception using a citation network analysis for two data sets, representing the literature on sexual selection and speciation. Overall, 54%-60% of citations come from a paper's own category, whereas 17%-23% are citations across categories. These cross-citations tend to focus on highly cited papers, and we observe a positive correlation between the numbers of citations a study receives within and across categories. We find evidence that reviews can function as integrators between the two literatures, argue that theoretical models are analogous to specific empirical study systems, and complement our analyses by studying a cocitation network. We conclude that theoretical and empirical research are more tightly connected than generally thought but that avenues exist to further increase this integration.
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Project description:Collaborative networks are of great value for science and technology (S&T) institutions as a way of sharing, generating and disseminating new knowledge that could ultimately lead to innovations. Driven by the need to assess the contribution and effectiveness of these networks in informing S&T management, we explored the evolution and dynamics of tuberculosis scientific networks involving the Oswaldo Cruz Foundation (Fiocruz), the major public health S&T Institution in Brazil. Social network analysis (SNA) was used to produce a 10-year (2005-2009, 2010-2014) retrospective longitudinal mapping of Brazilian tuberculosis research networks within the country and internationally, highlighting Fiocruz collaborations. Co-authorship analysis showed a significant expansion of collaboration in Brazil and the role of Fiocruz and other leading national institutions in maintaining connectivity, facilitating knowledge exchange and reducing network vulnerability. It also identified influential researchers that can act as information leaders and support strategic decisions. When we focused on networks inside the institution, the analysis showed a clear discontinuation between the clinical and the public health research areas, which needs specific internal policies to improve collaborations since outcomes in TB are expected to provide better diagnostic tools and more effective treatments. The approach provides evidence to support S&T management by pinpointing: key central institutions maintaining network connectivity; most influential researchers that can act as advisors/experts for investment and induction policies; key Fiocruz researchers that could improve information exchange, systems integration and innovation within the institution; opportunities for synergy between internal research groups working in complementary areas. In summary, we observed that SNA parameters proved to be a valuable tool that, along with other indicators, can strengthen knowledge platforms to support S&T management efforts.
| S-EPMC5549698 | biostudies-literature
Project description:Shift work is commonplace in modern societies, and shift workers are predisposed to the development of numerous chronic diseases. Disruptions to the circadian systems of shift workers are considered important contributors to the biological dysfunction these people frequently experience. Because of this, understanding how to alter shift work and zeitgeber (time cue) schedules to enhance circadian system function is likely to be key to improving the health of shift workers. While light exposure is the most important zeitgeber for the central clock in the circadian system, diet and exercise are plausible zeitgebers for circadian clocks in many tissues. We know little about how different zeitgebers interact and how to tailor zeitgeber schedules to the needs of individuals; however, in this review we share some guidelines to help shift workers adapt to their work schedules based on our current understanding of circadian biology. We focus in particular on the importance of diet timing and composition. Going forward, developments in phenotyping and "envirotyping" methods may be important to understanding how to optimise shift work. Non-invasive, multimodal, comprehensive phenotyping using multiple sources of time-stamped data may yield insights that are critical to the care of shift workers. Finally, the impact of these advances will be reduced without modifications to work environments to make it easier for shift workers to engage in behaviours conducive to their health. Integrating findings from behavioural science and ergonomics may help shift workers make healthier choices, thereby amplifying the beneficial effects of improved lifestyle prescriptions for these people.
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Project description:ObjectiveCurrent studies lack a comprehensive understanding of the environmental factors influencing type 2 diabetes, hindering an in-depth grasp of the overall etiology. To address this gap, we utilized network science tools to highlight research trends, knowledge structures, and intricate relationships among factors, offering a new perspective for a profound understanding of the etiology.MethodsThe Web of Science database was employed to retrieve documents relevant to environmental risk factors in type 2 diabetes from 2012 to 2024. Bibliometric analysis using Microsoft Excel and OriginPro provided a detailed scientific production profile, including articles, journals, countries, and authors. Co-occurrence analysis was employed to determine the collaboration state and knowledge structures, utilizing social network tools such as Gephi, Tableau, and R Studio. Additionally, theme evolutionary analysis was conducted using SciMAT to offer insights into research trends.ResultsThe publications and themes related to environmental factors in type 2 diabetes have consistently risen, shaping a well-established research domain. Lifestyle environmental factors, particularly diet and nutrition, stand out as the most represented and rapidly growing topics. Key focal hotspots include sedentary and digital behavior, PM2.5, ethnicity and socioeconomic status, traffic and greenspace, and depression. The theme evolutionary analysis revealed three distinct paths: (1) oxidative stress-air pollutants-PM2.5-air pollutants; (2) calcium-metabolic syndrome-cardiovascular disease; and (3) polychlorinated biphenyls (PCBs)-persistent organic pollutants (POPs)-obesity.ConclusionsDigital behavior signifies a novel approach for preventing and managing type 2 diabetes. The influence of PM2.5 and calcium on oxidative stress and abnormal vascular contraction is intricately linked to microvascular diabetes complications. The transition from PCBs and POPs to obesity underscores the disruption of endocrine function by chemicals, elevating the risk of diabetes. Future studies should explore the connections between environmental factors, microvascular complications, and long-term outcomes in diabetes.
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