7,899 research outputs found
Mining Heterogeneous Multivariate Time-Series for Learning Meaningful Patterns: Application to Home Health Telecare
For the last years, time-series mining has become a challenging issue for
researchers. An important application lies in most monitoring purposes, which
require analyzing large sets of time-series for learning usual patterns. Any
deviation from this learned profile is then considered as an unexpected
situation. Moreover, complex applications may involve the temporal study of
several heterogeneous parameters. In that paper, we propose a method for mining
heterogeneous multivariate time-series for learning meaningful patterns. The
proposed approach allows for mixed time-series -- containing both pattern and
non-pattern data -- such as for imprecise matches, outliers, stretching and
global translating of patterns instances in time. We present the early results
of our approach in the context of monitoring the health status of a person at
home. The purpose is to build a behavioral profile of a person by analyzing the
time variations of several quantitative or qualitative parameters recorded
through a provision of sensors installed in the home
Statistical significance of variables driving systematic variation
There are a number of well-established methods such as principal components
analysis (PCA) for automatically capturing systematic variation due to latent
variables in large-scale genomic data. PCA and related methods may directly
provide a quantitative characterization of a complex biological variable that
is otherwise difficult to precisely define or model. An unsolved problem in
this context is how to systematically identify the genomic variables that are
drivers of systematic variation captured by PCA. Principal components (and
other estimates of systematic variation) are directly constructed from the
genomic variables themselves, making measures of statistical significance
artificially inflated when using conventional methods due to over-fitting. We
introduce a new approach called the jackstraw that allows one to accurately
identify genomic variables that are statistically significantly associated with
any subset or linear combination of principal components (PCs). The proposed
method can greatly simplify complex significance testing problems encountered
in genomics and can be utilized to identify the genomic variables significantly
associated with latent variables. Using simulation, we demonstrate that our
method attains accurate measures of statistical significance over a range of
relevant scenarios. We consider yeast cell-cycle gene expression data, and show
that the proposed method can be used to straightforwardly identify
statistically significant genes that are cell-cycle regulated. We also analyze
gene expression data from post-trauma patients, allowing the gene expression
data to provide a molecularly-driven phenotype. We find a greater enrichment
for inflammatory-related gene sets compared to using a clinically defined
phenotype. The proposed method provides a useful bridge between large-scale
quantifications of systematic variation and gene-level significance analyses.Comment: 35 pages, 1 table, 6 main figures, 7 supplementary figure
Data mining as a tool for environmental scientists
Over recent years a huge library of data mining algorithms has been developed to tackle a variety of problems in fields such as medical imaging and network traffic analysis. Many of these techniques are far more flexible than more classical modelling approaches and could be usefully applied to data-rich environmental problems. Certain techniques such as Artificial Neural Networks, Clustering, Case-Based Reasoning and more recently Bayesian Decision Networks have found application in environmental modelling while other methods, for example classification and association rule extraction, have not yet been taken up on any wide scale. We propose that these and other data mining techniques could be usefully applied to difficult problems in the field. This paper introduces several data mining concepts and briefly discusses their application to environmental modelling, where data may be sparse, incomplete, or heterogenous
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
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