Abstract:Unjustified killing of badgers in Kyushu An ecological crisis is unfolding on Kyushu Island, Japan. Thousands of Japanese badgers (Meles anakuma) are being culled illegally without scientific advice or strategic planning. We urge the government to intervene and work with ecologists to establish whether this increase in culling is warranted. Justified culls should then be planned, regulated, monitored for effectiveness and subjected to animal-welfare controls (see Nature 543, 18-19; 2017). Badgers are protected… Show more
“…Clinicians should be prepared to handle data collected either horizontally from a large number of individuals, or vertically from granular, high resolution, multi-parameters analyses of a single individual (or few). In both cases all the caveats and challenges of big data hold (Brazas et al, 2017) and are similar to those encountered in the Internet of Things (IoT) domain. IoT is described as a network of electronic devices equipped with software, sensors, and connectivity used to collect data for many purposes.…”
Section: From Personalized To High Definition Medicinementioning
confidence: 88%
“…Without necessarily transforming themselves into bioinformaticians, biomedical researchers have to make a cultural shift embracing a new domain of "infobiology" (Brazas et al, 2017). As a consequence, the many flavors of bioinformatics and computational biology skills are now a must-have in the technologically advanced research laboratories or R&D departments: companies and research institutions, as well as single laboratories, should also promote and organize computationally skilled personnel (Chang, 2015;Bartlett et al, 2017).…”
Section: How Data Are Shaping Life Science and Healthmentioning
Research and innovation are constant imperatives for the healthcare sector: medicine, biology and biotechnology support it, and more recently computational and data-driven disciplines gained relevance to handle the massive amount of data this sector is and will be generating. To be effective in translational and healthcare industrial research, big data in the life science domain need to be organized, well annotated, catalogued, correlated and integrated: the biggest the data silos at hand, the stronger the need for organization and tidiness. The degree of such organization marks the transition from data to knowledge for strategic decision making. Medicine is supported by observations and data and for certain aspects medicine is becoming a data science supported by clinicians. While medicine defines itself as personalized, quantified (precision med) or in high-definition, clinicians should be prepared to deal with a world in which Internet of People paraphrases the Internet of Things paradigm. Integrated use of electronic health records (EHRs) and quantitative data (both clinical and molecular) is a key process to develop precision medicine. Health records collection was originally designed for patient care and billing and/or insurance purposes. The digitization of health records facilitates and opens up new possibilities for science and research and they should be now collected and managed with this aim in mind. More data and the ability to efficiently handle them is a significant advantage not only for clinicians and life science researchers, but for drugs producers too. In an industrial sector spending increasing efforts on drug repurposing, attention to efficient methods to unwind the intricacies of the hugely complex reality of human physiology, such as network based methods and physical chemistry computational methods, became of paramount importance. Finally, the main pillars of industrial R&D processes for vaccines, include initial discovery, early-late pre clinics, pre-industrialization, clinical phases and finally registration-commercialization. The passage from one step to another is regulated by stringent pass/fail criteria. Bottlenecks of the R&D process are often represented by animal and human studies, which could be rationalized by surrogate in vitro assays as well as by predictive molecular and cellular signatures and models.
“…Clinicians should be prepared to handle data collected either horizontally from a large number of individuals, or vertically from granular, high resolution, multi-parameters analyses of a single individual (or few). In both cases all the caveats and challenges of big data hold (Brazas et al, 2017) and are similar to those encountered in the Internet of Things (IoT) domain. IoT is described as a network of electronic devices equipped with software, sensors, and connectivity used to collect data for many purposes.…”
Section: From Personalized To High Definition Medicinementioning
confidence: 88%
“…Without necessarily transforming themselves into bioinformaticians, biomedical researchers have to make a cultural shift embracing a new domain of "infobiology" (Brazas et al, 2017). As a consequence, the many flavors of bioinformatics and computational biology skills are now a must-have in the technologically advanced research laboratories or R&D departments: companies and research institutions, as well as single laboratories, should also promote and organize computationally skilled personnel (Chang, 2015;Bartlett et al, 2017).…”
Section: How Data Are Shaping Life Science and Healthmentioning
Research and innovation are constant imperatives for the healthcare sector: medicine, biology and biotechnology support it, and more recently computational and data-driven disciplines gained relevance to handle the massive amount of data this sector is and will be generating. To be effective in translational and healthcare industrial research, big data in the life science domain need to be organized, well annotated, catalogued, correlated and integrated: the biggest the data silos at hand, the stronger the need for organization and tidiness. The degree of such organization marks the transition from data to knowledge for strategic decision making. Medicine is supported by observations and data and for certain aspects medicine is becoming a data science supported by clinicians. While medicine defines itself as personalized, quantified (precision med) or in high-definition, clinicians should be prepared to deal with a world in which Internet of People paraphrases the Internet of Things paradigm. Integrated use of electronic health records (EHRs) and quantitative data (both clinical and molecular) is a key process to develop precision medicine. Health records collection was originally designed for patient care and billing and/or insurance purposes. The digitization of health records facilitates and opens up new possibilities for science and research and they should be now collected and managed with this aim in mind. More data and the ability to efficiently handle them is a significant advantage not only for clinicians and life science researchers, but for drugs producers too. In an industrial sector spending increasing efforts on drug repurposing, attention to efficient methods to unwind the intricacies of the hugely complex reality of human physiology, such as network based methods and physical chemistry computational methods, became of paramount importance. Finally, the main pillars of industrial R&D processes for vaccines, include initial discovery, early-late pre clinics, pre-industrialization, clinical phases and finally registration-commercialization. The passage from one step to another is regulated by stringent pass/fail criteria. Bottlenecks of the R&D process are often represented by animal and human studies, which could be rationalized by surrogate in vitro assays as well as by predictive molecular and cellular signatures and models.
“…There has been a sustained interest in applying semantic web technologies to model educational resources, as seen by early vision papers, such as Bourda and Doan (2003) which proposed a semantic web for learning resources using an RDF schema version of the IEEE Learning Object Metadata standard, journal special issues (Anderson and Whitelock 2004), survey papers (Aroyo and Dicheva 2004;Dietze et al 2013;Pereira et al 2018), and the recent ISWC 2015 LINKed EDucation workshop. Other educational resource collection efforts include ELIXIR Training e-Support System (tess.elixir-europe.org), GOBLET (Brazas, Blackford, and Attwood 2017), and the Open Educational Resources Commons (oercommons.org). Our project shares many of the same goals of these previous efforts.…”
The availability of massive datasets in genetics, neuroimaging, mobile health, and other subfields of biology and medicine promises new insights but also poses significant challenges. To realize the potential of big data in biomedicine, the National Institutes of Health launched the Big Data to Knowledge (BD2K) initiative, funding several centers of excellence in biomedical data analysis and a Training Coordinating Center (TCC) tasked with facilitating online and inperson training of biomedical researchers in data science. A major initiative of the BD2K TCC is to automatically identify, describe, and organize data science training resources available on the Web and provide personalized training paths for users. In this paper, we describe the construction of ERuDIte, the Educational Resource Discovery Index for Data Science, and its release as linked data. ERuDIte contains over 11,000 training resources including courses, video tutorials, conference talks, and other materials. The metadata for these resources is described uniformly using Schema.org. We use machine learning techniques to tag each resource with concepts from the Data Science Education Ontology, which we developed to further describe resource content. Finally, we map references to people and organizations in learning resources to entities in DBpedia, DBLP, and ORCID, embedding our collection in the web of linked data. We hope that ERuDIte will provide a framework to foster open linked educational resources on the Web.
“…There is an urgent need for early training in bioinformatic skills in order to empower plant researchers and breeders to make use of their own data (i.e., for analysis and interpretation) [150]. However, to identify those who are adept at both bioinformatics and plant breeding is difficult and not trivial.…”
Section: Bioinformatics and Data Mining: Next Generation Breeding Is mentioning
Climate change, associated with global warming, extreme weather events, and increasing incidence of weeds, pests and pathogens, is strongly influencing major cropping systems. In this challenging scenario, miscellaneous strategies are needed to expedite the rate of genetic gains with the purpose of developing novel varieties. Large plant breeding populations, efficient high-throughput technologies, big data management tools, and downstream biotechnology and molecular techniques are the pillars on which next generation breeding is based. In this review, we describe the toolbox the breeder has to face the challenges imposed by climate change, remark on the key role bioinformatics plays in the analysis and interpretation of big “omics” data, and acknowledge all the benefits that have been introduced into breeding strategies with the biotechnological and digital revolution.
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