16,351 research outputs found
Literature-based discovery of diabetes- and ROS-related targets
Abstract Background Reactive oxygen species (ROS) are known mediators of cellular damage in multiple diseases including diabetic complications. Despite its importance, no comprehensive database is currently available for the genes associated with ROS. Methods We present ROS- and diabetes-related targets (genes/proteins) collected from the biomedical literature through a text mining technology. A web-based literature mining tool, SciMiner, was applied to 1,154 biomedical papers indexed with diabetes and ROS by PubMed to identify relevant targets. Over-represented targets in the ROS-diabetes literature were obtained through comparisons against randomly selected literature. The expression levels of nine genes, selected from the top ranked ROS-diabetes set, were measured in the dorsal root ganglia (DRG) of diabetic and non-diabetic DBA/2J mice in order to evaluate the biological relevance of literature-derived targets in the pathogenesis of diabetic neuropathy. Results SciMiner identified 1,026 ROS- and diabetes-related targets from the 1,154 biomedical papers (http://jdrf.neurology.med.umich.edu/ROSDiabetes/). Fifty-three targets were significantly over-represented in the ROS-diabetes literature compared to randomly selected literature. These over-represented targets included well-known members of the oxidative stress response including catalase, the NADPH oxidase family, and the superoxide dismutase family of proteins. Eight of the nine selected genes exhibited significant differential expression between diabetic and non-diabetic mice. For six genes, the direction of expression change in diabetes paralleled enhanced oxidative stress in the DRG. Conclusions Literature mining compiled ROS-diabetes related targets from the biomedical literature and led us to evaluate the biological relevance of selected targets in the pathogenesis of diabetic neuropathy.http://deepblue.lib.umich.edu/bitstream/2027.42/78315/1/1755-8794-3-49.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/2/1755-8794-3-49-S7.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/3/1755-8794-3-49-S10.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/4/1755-8794-3-49-S8.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/5/1755-8794-3-49-S3.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/6/1755-8794-3-49-S1.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/7/1755-8794-3-49-S4.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/8/1755-8794-3-49-S2.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/9/1755-8794-3-49-S12.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/10/1755-8794-3-49-S11.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/11/1755-8794-3-49-S9.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/12/1755-8794-3-49-S5.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/13/1755-8794-3-49-S6.XLShttp://deepblue.lib.umich.edu/bitstream/2027.42/78315/14/1755-8794-3-49.pdfPeer Reviewe
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
Extracting Biomolecular Interactions Using Semantic Parsing of Biomedical Text
We advance the state of the art in biomolecular interaction extraction with
three contributions: (i) We show that deep, Abstract Meaning Representations
(AMR) significantly improve the accuracy of a biomolecular interaction
extraction system when compared to a baseline that relies solely on surface-
and syntax-based features; (ii) In contrast with previous approaches that infer
relations on a sentence-by-sentence basis, we expand our framework to enable
consistent predictions over sets of sentences (documents); (iii) We further
modify and expand a graph kernel learning framework to enable concurrent
exploitation of automatically induced AMR (semantic) and dependency structure
(syntactic) representations. Our experiments show that our approach yields
interaction extraction systems that are more robust in environments where there
is a significant mismatch between training and test conditions.Comment: Appearing in Proceedings of the Thirtieth AAAI Conference on
Artificial Intelligence (AAAI-16
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
The potential of text mining in data integration and network biology for plant research : a case study on Arabidopsis
Despite the availability of various data repositories for plant research, a wealth of information currently remains hidden within the biomolecular literature. Text mining provides the necessary means to retrieve these data through automated processing of texts. However, only recently has advanced text mining methodology been implemented with sufficient computational power to process texts at a large scale. In this study, we assess the potential of large-scale text mining for plant biology research in general and for network biology in particular using a state-of-the-art text mining system applied to all PubMed abstracts and PubMed Central full texts. We present extensive evaluation of the textual data for Arabidopsis thaliana, assessing the overall accuracy of this new resource for usage in plant network analyses. Furthermore, we combine text mining information with both protein-protein and regulatory interactions from experimental databases. Clusters of tightly connected genes are delineated from the resulting network, illustrating how such an integrative approach is essential to grasp the current knowledge available for Arabidopsis and to uncover gene information through guilt by association. All large-scale data sets, as well as the manually curated textual data, are made publicly available, hereby stimulating the application of text mining data in future plant biology studies
Lexical Adaptation of Link Grammar to the Biomedical Sublanguage: a Comparative Evaluation of Three Approaches
We study the adaptation of Link Grammar Parser to the biomedical sublanguage
with a focus on domain terms not found in a general parser lexicon. Using two
biomedical corpora, we implement and evaluate three approaches to addressing
unknown words: automatic lexicon expansion, the use of morphological clues, and
disambiguation using a part-of-speech tagger. We evaluate each approach
separately for its effect on parsing performance and consider combinations of
these approaches. In addition to a 45% increase in parsing efficiency, we find
that the best approach, incorporating information from a domain part-of-speech
tagger, offers a statistically signicant 10% relative decrease in error. The
adapted parser is available under an open-source license at
http://www.it.utu.fi/biolg
Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts
The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin
A Labeled Graph Kernel for Relationship Extraction
In this paper, we propose an approach for Relationship Extraction (RE) based
on labeled graph kernels. The kernel we propose is a particularization of a
random walk kernel that exploits two properties previously studied in the RE
literature: (i) the words between the candidate entities or connecting them in
a syntactic representation are particularly likely to carry information
regarding the relationship; and (ii) combining information from distinct
sources in a kernel may help the RE system make better decisions. We performed
experiments on a dataset of protein-protein interactions and the results show
that our approach obtains effectiveness values that are comparable with the
state-of-the art kernel methods. Moreover, our approach is able to outperform
the state-of-the-art kernels when combined with other kernel methods
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