28,999 research outputs found
Using Neural Networks for Relation Extraction from Biomedical Literature
Using different sources of information to support automated extracting of
relations between biomedical concepts contributes to the development of our
understanding of biological systems. The primary comprehensive source of these
relations is biomedical literature. Several relation extraction approaches have
been proposed to identify relations between concepts in biomedical literature,
namely, using neural networks algorithms. The use of multichannel architectures
composed of multiple data representations, as in deep neural networks, is
leading to state-of-the-art results. The right combination of data
representations can eventually lead us to even higher evaluation scores in
relation extraction tasks. Thus, biomedical ontologies play a fundamental role
by providing semantic and ancestry information about an entity. The
incorporation of biomedical ontologies has already been proved to enhance
previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
Advances in sequencing techniques have led to exponential growth in
biological data, demanding the development of large-scale bioinformatics
experiments. Because these experiments are computation- and data-intensive,
they require high-performance computing (HPC) techniques and can benefit from
specialized technologies such as Scientific Workflow Management Systems (SWfMS)
and databases. In this work, we present BioWorkbench, a framework for managing
and analyzing bioinformatics experiments. This framework automatically collects
provenance data, including both performance data from workflow execution and
data from the scientific domain of the workflow application. Provenance data
can be analyzed through a web application that abstracts a set of queries to
the provenance database, simplifying access to provenance information. We
evaluate BioWorkbench using three case studies: SwiftPhylo, a phylogenetic tree
assembly workflow; SwiftGECKO, a comparative genomics workflow; and RASflow, a
RASopathy analysis workflow. We analyze each workflow from both computational
and scientific domain perspectives, by using queries to a provenance and
annotation database. Some of these queries are available as a pre-built feature
of the BioWorkbench web application. Through the provenance data, we show that
the framework is scalable and achieves high-performance, reducing up to 98% of
the case studies execution time. We also show how the application of machine
learning techniques can enrich the analysis process
An Open Framework for Extensible Multi-Stage Bioinformatics Software
In research labs, there is often a need to customise software at every step
in a given bioinformatics workflow, but traditionally it has been difficult to
obtain both a high degree of customisability and good performance.
Performance-sensitive tools are often highly monolithic, which can make
research difficult. We present a novel set of software development principles
and a bioinformatics framework, Friedrich, which is currently in early
development. Friedrich applications support both early stage experimentation
and late stage batch processing, since they simultaneously allow for good
performance and a high degree of flexibility and customisability. These
benefits are obtained in large part by basing Friedrich on the multiparadigm
programming language Scala. We present a case study in the form of a basic
genome assembler and its extension with new functionality. Our architecture has
the potential to greatly increase the overall productivity of software
developers and researchers in bioinformatics.Comment: 12 pages, 1 figure, to appear in proceedings of PRIB 201
- …