hackseq: Catalyzing collaboration between biological and computational scientists via hackathon.
Ontology highlight
ABSTRACT:
SUBMITTER: hackseq Organizing Committee 2016
PROVIDER: S-EPMC5381621 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundTransdisciplinary collaboration is essential in addressing the translation gap between scientific discovery and delivery of evidence-based interventions to prevent and treat diabetes. We examined patterns of collaboration among scientists at the Washington University Center for Diabetes Translation Research.MethodsMembers (n = 56) of the Washington University Center for Diabetes Translation Research were surveyed about collaboration overall and on publications, presentations, and grants; 87.5% responded (n = 49). We used traditional and network descriptive statistics and visualization to examine the networks and exponential random graph modeling to identify predictors of collaboration.ResultsThe 56 network members represented nine disciplines. On average, network members had been affiliated with the center for 3.86 years (s.d. = 1.41). The director was by far the most central in all networks. The overall and publication networks were the densest, while the overall and grant networks were the most centralized. The grant network was the most transdisciplinary. The presentation network was the least dense, least centralized, and least transdisciplinary. For every year of center affiliation, network members were 10% more likely to collaborate (OR: 1.10; 95% CI: 1.00-1.21) and 13% more likely to write a paper together (OR: 1.13; 95% CI: 1.02-1.25). Network members in the same discipline were over twice as likely to collaborate in the overall network (OR: 2.10; 95% CI: 1.40-3.15); however, discipline was not associated with collaboration in the other networks. Rank was not associated with collaboration in any network.ConclusionsAs transdisciplinary centers become more common, it is important to identify structural features, such as a central leader and ongoing collaboration over time, associated with scholarly productivity and, ultimately, with advancing science and practice.
| S-EPMC4547723 | biostudies-literature
Project description:Background It often takes more than 10 years and costs more than 1 billion dollars to develop a new drug for a particular disease and bring it to the market. Drug repositioning can significantly reduce costs and time in drug development. Recently, computational drug repositioning attracted a considerable amount of attention among researchers, and a plethora of computational drug repositioning methods have been proposed. This methodology has widely been used in order to address various medical challenges, including cancer treatment. The most common cancers are lung and breast cancers. Thus, suggesting FDA-approved drugs via drug repositioning for breast cancer would help us to circumvent the approval process and subsequently save money as well as time. Methods In this study, we propose a novel network-based method, named RepCOOL, for drug repositioning. RepCOOL integrates various heterogeneous biological networks to suggest new drug candidates for a given disease. Results The proposed method showed a promising performance on benchmark datasets via rigorous cross-validation. The final drug repositioning model has been built based on a random forest classifier after examining various machine learning algorithms. Finally, in a case study, four FDA approved drugs were suggested for breast cancer stage II. Conclusion Results show the potency of the proposed method in detecting true drug-disease relationships. RepCOOL suggested four new drugs for breast cancer stage II namely Doxorubicin, Paclitaxel, Trastuzumab, and Tamoxifen.
| S-EPMC7532104 | biostudies-literature
Project description:A partnership between scientists, high school teachers, and their students provides authentic research experiences to help students understand the nature and processes of science. The Partnership for Research and Education in Plants (PREP) engages students in a large-scale genomics research project using classroom-tested protocols that can help to find the function of a disabled gene in the widely studied plant Arabidopsis thaliana. Here, we describe the framework of PREP in the classroom within the context of the National Science Education Standards.
| S-EPMC3856900 | biostudies-literature
Project description:Despite persistent efforts in understanding the creativity of scientists over different career stages, little is known about the underlying dynamics of research topic switching that drives innovation. Here, we analyze the publication records of individual scientists, aiming to quantify their topic switching dynamics and its influence. We find that the co-citing network of papers of a scientist exhibits a clear community structure where each major community represents a research topic. Our analysis suggests that scientists have a narrow distribution of number of topics. However, researchers nowadays switch more frequently between topics than those in the early days. We also find that high switching probability in early career is associated with low overall productivity, yet with high overall productivity in latter career. Interestingly, the average citation per paper, however, is in all career stages negatively correlated with the switching probability. We propose a model that can explain the main observed features.
| S-EPMC6668429 | biostudies-literature
Project description:In genomics, bioinformatics and other areas of data science, gaps exist between extant public datasets and the open-source software tools built by the community to analyze similar data types. The purpose of biological data science hackathons is to assemble groups of genomics or bioinformatics professionals and software developers to rapidly prototype software to address these gaps. The only two rules for the NCBI-assisted hackathons run so far are that 1) data either must be housed in public data repositories or be deposited to such repositories shortly after the hackathon's conclusion, and 2) all software comprising the final pipeline must be open-source or open-use. Proposed topics, as well as suggested tools and approaches, are distributed to participants at the beginning of each hackathon and refined during the event. Software, scripts, and pipelines are developed and published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development. The code resulting from each hackathon is published at https://github.com/NCBI-Hackathons/ with separate directories or repositories for each team.
| S-EPMC4837979 | biostudies-literature
Project description:Although tracking research has achieved excellent performance in mathematical angles, it is still meaningful to analyze tracking problems from multiple perspectives. This motivation not only promotes the independence of tracking research but also increases the flexibility of practical applications. This paper presents a significant tracking framework based on the multi-dimensional state?action space reinforcement learning, termed as multi-angle analysis collaboration tracking (MACT). MACT is comprised of a basic tracking framework and a strategic framework which assists the former. Especially, the strategic framework is extensible and currently includes feature selection strategy (FSS) and movement trend strategy (MTS). These strategies are abstracted from the multi-angle analysis of tracking problems (observer's attention and object's motion). The content of the analysis corresponds to the specific actions in the multidimensional action space. Concretely, the tracker, regarded as an agent, is trained with Q-learning algorithm and ? -greedy exploration strategy, where we adopt a customized rewarding function to encourage robust object tracking. Numerous contrast experimental evaluations on the OTB50 benchmark demonstrate the effectiveness of the strategies and improvement in speed and accuracy of MACT tracker.
| S-EPMC6264108 | biostudies-other
Project description:The construction and application of biological network models is an approach that offers a holistic way to understand biological processes involved in disease. Chronic obstructive pulmonary disease (COPD) is a progressive inflammatory disease of the airways for which therapeutic options currently are limited after diagnosis, even in its earliest stage. COPD network models are important tools to better understand the biological components and processes underlying initial disease development. With the increasing amounts of literature that are now available, crowdsourcing approaches offer new forms of collaboration for researchers to review biological findings, which can be applied to the construction and verification of complex biological networks. We report the construction of 50 biological network models relevant to lung biology and early COPD using an integrative systems biology and collaborative crowd-verification approach. By combining traditional literature curation with a data-driven approach that predicts molecular activities from transcriptomics data, we constructed an initial COPD network model set based on a previously published non-diseased lung-relevant model set. The crowd was given the opportunity to enhance and refine the networks on a website ( https://bionet.sbvimprover.com/) and to add mechanistic detail, as well as critically review existing evidence and evidence added by other users, so as to enhance the accuracy of the biological representation of the processes captured in the networks. Finally, scientists and experts in the field discussed and refined the networks during an in-person jamboree meeting. Here, we describe examples of the changes made to three of these networks: Neutrophil Signaling, Macrophage Signaling, and Th1-Th2 Signaling. We describe an innovative approach to biological network construction that combines literature and data mining and a crowdsourcing approach to generate a comprehensive set of COPD-relevant models that can be used to help understand the mechanisms related to lung pathobiology. Registered users of the website can freely browse and download the networks.
| S-EPMC4350443 | biostudies-literature
Project description:Reinforcement learning is a computational framework for an active agent to learn behaviors on the basis of a scalar reward signal. The agent can be an animal, a human, or an artificial system such as a robot or a computer program. The reward can be food, water, money, or whatever measure of the performance of the agent. The theory of reinforcement learning, which was developed in an artificial intelligence community with intuitions from animal learning theory, is now giving a coherent account on the function of the basal ganglia. It now serves as the "common language" in which biologists, engineers, and social scientists can exchange their problems and findings. This article reviews the basic theoretical framework of reinforcement learning and discusses its recent and future contributions toward the understanding of animal behaviors and human decision making.
| S-EPMC2645553 | biostudies-other
Project description:The present article presents an up-to-date account of the current media relations of scientists, based on a comprehensive analysis of relevant surveys. The evidence suggests that most scientists consider visibility in the media important and responding to journalists a professional duty--an attitude that is reinforced by universities and other science organizations. Scientific communities continue to regulate media contacts with their members by certain norms that compete with the motivating and regulating influences of public information departments. Most scientists assume a two-arena model with a gap between the arenas of internal scientific and public communication. They want to meet the public in the public arena, not in the arena of internal scientific communication. Despite obvious changes in science and in the media system, the orientations of scientists toward the media, as well as the patterns of interaction with journalists, have their roots in the early 1980s. Although there is more influence on public communication from the science organizations and more emphasis on strategic considerations today, the available data do not indicate abrupt changes in communication practices or in the relevant beliefs and attitudes of scientists in the past 30 y. Changes in the science-media interface may be expected from the ongoing structural transformation of the public communication system. However, as yet, there is little evidence of an erosion of the dominant orientation toward the public and public communication within the younger generation of scientists.
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Project description:Failure to understand evolutionary dynamics has been hypothesized as limiting our ability to control biological systems. An increasing awareness of similarities between macroscopic ecosystems and cellular tissues has inspired optimism that game theory will provide insights into the progression and control of cancer. To realize this potential, the ability to compare game theoretic models and experimental measurements of population dynamics should be broadly disseminated. In this tutorial, we present an analysis method that can be used to train parameters in game theoretic dynamics equations, used to validate the resulting equations, and used to make predictions to challenge these equations and to design treatment strategies. The data analysis techniques in this tutorial are adapted from the analysis of reaction kinetics using the method of initial rates taught in undergraduate general chemistry courses. Reliance on computer programming is avoided to encourage the adoption of these methods as routine bench activities.
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