76,203 research outputs found
Structural Alignment of RNAs Using Profile-csHMMs and Its Application to RNA Homology Search: Overview and New Results
Systematic research on noncoding RNAs (ncRNAs) has revealed that many ncRNAs are actively involved in various biological networks. Therefore, in order to fully understand the mechanisms of these networks, it is crucial to understand the roles of ncRNAs. Unfortunately, the annotation of ncRNA genes that give rise to functional RNA molecules has begun only recently, and it is far from being complete. Considering the huge amount of genome sequence data, we need efficient computational methods for finding ncRNA genes. One effective way of finding ncRNA genes is to look for regions that are similar to known ncRNA genes. As many ncRNAs have well-conserved secondary structures, we need statistical models that can represent such structures for this purpose. In this paper, we propose a new method for representing RNA sequence profiles and finding structural alignment of RNAs based on profile context-sensitive hidden Markov models (profile-csHMMs). Unlike existing models, the proposed approach can handle any kind of RNA secondary structures, including pseudoknots. We show that profile-csHMMs can provide an effective framework for the computational analysis of RNAs and the identification of ncRNA genes
How Advanced Change Patterns Impact the Process of Process Modeling
Process model quality has been an area of considerable research efforts. In
this context, correctness-by-construction as enabled by change patterns
provides promising perspectives. While the process of process modeling (PPM)
based on change primitives has been thoroughly investigated, only little is
known about the PPM based on change patterns. In particular, it is unclear what
set of change patterns should be provided and how the available change pattern
set impacts the PPM. To obtain a better understanding of the latter as well as
the (subjective) perceptions of process modelers, the arising challenges, and
the pros and cons of different change pattern sets we conduct a controlled
experiment. Our results indicate that process modelers face similar challenges
irrespective of the used change pattern set (core pattern set versus extended
pattern set, which adds two advanced change patterns to the core patterns set).
An extended change pattern set, however, is perceived as more difficult to use,
yielding a higher mental effort. Moreover, our results indicate that more
advanced patterns were only used to a limited extent and frequently applied
incorrectly, thus, lowering the potential benefits of an extended pattern set
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
SensorCloud: Towards the Interdisciplinary Development of a Trustworthy Platform for Globally Interconnected Sensors and Actuators
Although Cloud Computing promises to lower IT costs and increase users'
productivity in everyday life, the unattractive aspect of this new technology
is that the user no longer owns all the devices which process personal data. To
lower scepticism, the project SensorCloud investigates techniques to understand
and compensate these adoption barriers in a scenario consisting of cloud
applications that utilize sensors and actuators placed in private places. This
work provides an interdisciplinary overview of the social and technical core
research challenges for the trustworthy integration of sensor and actuator
devices with the Cloud Computing paradigm. Most importantly, these challenges
include i) ease of development, ii) security and privacy, and iii) social
dimensions of a cloud-based system which integrates into private life. When
these challenges are tackled in the development of future cloud systems, the
attractiveness of new use cases in a sensor-enabled world will considerably be
increased for users who currently do not trust the Cloud.Comment: 14 pages, 3 figures, published as technical report of the Department
of Computer Science of RWTH Aachen Universit
Handling non-ignorable dropouts in longitudinal data: A conditional model based on a latent Markov heterogeneity structure
We illustrate a class of conditional models for the analysis of longitudinal
data suffering attrition in random effects models framework, where the
subject-specific random effects are assumed to be discrete and to follow a
time-dependent latent process. The latent process accounts for unobserved
heterogeneity and correlation between individuals in a dynamic fashion, and for
dependence between the observed process and the missing data mechanism. Of
particular interest is the case where the missing mechanism is non-ignorable.
To deal with the topic we introduce a conditional to dropout model. A shape
change in the random effects distribution is considered by directly modeling
the effect of the missing data process on the evolution of the latent
structure. To estimate the resulting model, we rely on the conditional maximum
likelihood approach and for this aim we outline an EM algorithm. The proposal
is illustrated via simulations and then applied on a dataset concerning skin
cancers. Comparisons with other well-established methods are provided as well
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
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