24,212 research outputs found
Machine Learning and Integrative Analysis of Biomedical Big Data.
Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues
Exploiting Social Annotation for Automatic Resource Discovery
Information integration applications, such as mediators or mashups, that
require access to information resources currently rely on users manually
discovering and integrating them in the application. Manual resource discovery
is a slow process, requiring the user to sift through results obtained via
keyword-based search. Although search methods have advanced to include evidence
from document contents, its metadata and the contents and link structure of the
referring pages, they still do not adequately cover information sources --
often called ``the hidden Web''-- that dynamically generate documents in
response to a query. The recently popular social bookmarking sites, which allow
users to annotate and share metadata about various information sources, provide
rich evidence for resource discovery. In this paper, we describe a
probabilistic model of the user annotation process in a social bookmarking
system del.icio.us. We then use the model to automatically find resources
relevant to a particular information domain. Our experimental results on data
obtained from \emph{del.icio.us} show this approach as a promising method for
helping automate the resource discovery task.Comment: 6 pages, submitted to AAAI07 workshop on Information Integration on
the We
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