26,950 research outputs found
Automation on the generation of genome scale metabolic models
Background: Nowadays, the reconstruction of genome scale metabolic models is
a non-automatized and interactive process based on decision taking. This
lengthy process usually requires a full year of one person's work in order to
satisfactory collect, analyze and validate the list of all metabolic reactions
present in a specific organism. In order to write this list, one manually has
to go through a huge amount of genomic, metabolomic and physiological
information. Currently, there is no optimal algorithm that allows one to
automatically go through all this information and generate the models taking
into account probabilistic criteria of unicity and completeness that a
biologist would consider. Results: This work presents the automation of a
methodology for the reconstruction of genome scale metabolic models for any
organism. The methodology that follows is the automatized version of the steps
implemented manually for the reconstruction of the genome scale metabolic model
of a photosynthetic organism, {\it Synechocystis sp. PCC6803}. The steps for
the reconstruction are implemented in a computational platform (COPABI) that
generates the models from the probabilistic algorithms that have been
developed. Conclusions: For validation of the developed algorithm robustness,
the metabolic models of several organisms generated by the platform have been
studied together with published models that have been manually curated. Network
properties of the models like connectivity and average shortest mean path of
the different models have been compared and analyzed.Comment: 24 pages, 2 figures, 2 table
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Evolutionary Subject Tagging in the Humanities; Supporting Discovery and Examination in Digital Cultural Landscapes
In this paper, the authors attempt to identify problematic issues for subject tagging in the humanities, particularly those associated with information objects in digital formats. In the third major section, the authors identify a number of assumptions that lie behind the current practice of subject classification that we think should be challenged. We move then to propose features of classification systems that could increase their effectiveness. These emerged as recurrent themes in many of the conversations with scholars, consultants, and colleagues. Finally, we suggest next steps that we believe will help scholars and librarians develop better subject classification systems to support research in the humanities.NEH Office of Digital Humanities: Digital Humanities Start-Up Grant (HD-51166-10
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