[Corpora-List] Special issue of Journal of Biomedical Informatics on "Mining the Pharmacogenomics Literature": Call For Papers

Sat Jan 8 17:16:48 UTC 2011

Note to CORPORA readers: this is in a somewhat specialized domain, but
the CFP specifically calls for papers on corpus construction, so I
thought that some of you would be interested.

Journal of Biomedical Informatics
Call for Papers: Mining the Pharmacogenomics Literature

Guest editors:  K. Bretonnel Cohen, Russ Altman, Yael Garten, Udo
Hahn, Nigam Shah

We are inviting submissions for a special issue of the Journal of
Biomedical Informatics on the automatic or semi-automatic extraction
of relationships between biomedical entities relevant to
pharmacogenomics from the research literature. Accepted papers will
focus particularly on methods for the extraction of
genotype-phenotype, genotype-drug, and phenotype-drug relationships
and the novel use of these relationships for advancing pharmacogenomic
research. Efforts aimed at creating benchmark corpora as well as
comparative evaluation of existing relationship extraction methods are
of special interest.

Pharmacogenomics is the study of how human genetic variation affects
an individual’s response to drugs.  It includes a spectrum of
discovery and application, ranging from the discovery of individual
gene-drug interactions, the uncovering of pathways of drug response,
the use of human and model system genetic variation to understand the
mechanism of drug action, and the understanding of both the
anticipated and unanticipated effects of drugs.  The field includes
the understanding of the role of gene-drug interactions in clinical
medicine, including the identification of study cohorts, the
definition of target populations, the evaluation of benefit, and the
analysis of research and clinical databases to extract information
about the larger context of drug interactions in different disease
contexts.  As such, pharmacogenomics is a timely and important field.
The promise that it holds for individualized medicine is central as
technical advances such as SNP microarrays, whole genome sequencing
and other high-throughput measurement technologies allow us to predict
beneficial, non-beneficial, and deleterious drugs for specific
individuals based on aspects of both the individual and the drug.

However, information management in this field relies on fairly
traditional means, especially curated databases (PharmGKB, DrugBank,
dbGAP, PubMed and others), which do not scale to (1) the rapid
expansion of the pharmacogenomics literature in recent years and (2)
the increasingly available volume of full text publications, which
contain more specific and (potentially) informative facts than Medline
abstracts.   Hence, although there is a large demand and significant
utility of text analytics for the study of pharmacogenomics, the
potential of such methods is not fully realized; in part because the
work to date has failed to bridge the two distinct worlds—that of
(bench) molecular biology and that of (clinically oriented)
pharmacology—and because the developers of text analytics, both in
computer science and biomedical informatics, are not fully aware of
this challenging subfield.

The steady stream of work on extracting interactions from text, the
increasing attention in the Semantic Web to capturing facts as
"nano-publications" (individual assertions that are attributable to
authors and traceable in their publications), and efforts to represent
scientific discourse in a structured manner, all indicate that the
time is ripe for research that goes even beyond the mere extraction of
explicitly stated knowledge in documents to linking text-mined and
database elements through formal reasoning to uncover implicit and in
some sense "new" knowledge.   There have recently been scientific
workshops and sessions at conferences devoted to text mining in the
context of pharmacogenomics, including the 2010 and 2011 workshops of
the Pacific Symposium on Biocomputing (http://psb.stanford.edu/),
which have demonstrated the emerging critical mass of investigators in
this subspecialty.

In order to advance this agenda, it is also essential that existing
relationship extraction methods be compared to one another and that a
community-wide sharable benchmark corpus emerges against which such
efforts can be compared. We welcome submissions to this special issue
that utilize information available at PharmGKB to compare different
relationship extraction methods and the corresponding "new" knowledge
discovery they might drive.  These include curated relationships and
annotations of genetic variants available at
http://www.pharmgkb.org/resources/downloads_and_web_services.jsp.

The planned special issue aims to address the gap in coverage of text
mining for pharmacogenomics, as an important initial application area
of genomics in clinical medicine, and thus an important translational
medicine activity. The technical area of the issue is intended to
focus particularly on genotype-phenotype-drug relationships.  It will
include broad categories of work that have been well-studied in the
past, specifically text mining and reasoning, but will restrict
submissions to applications of that work to the constrained area of
pharmacogenomics, and particularly genotype-phenotype-drug
relationships.  For example, topics that are solicited include:

•	Relation extraction between genotypes, phenotypes, and drugs, and
other semantic classes relevant to pharmacogenomics
•	Corpus development for pharmacogenomics text mining
•	Associating gene variants (mutations, alleles, rs/ss numbers) to the
associated gene name
•	Using text mining to extract information about the association of
drugs with clinical phenotpyes
•	The use of biological networks in combination with text mining to
facilitate discovery
•	Work on the corpus of documents linked to by PharmGKB
•	Reasoning systems applied over the PharmGKB knowledge base
•	The creation of ontologies to help relate molecular action of drugs
to their clinical effects.

Work on named entity recognition (e.g., gene taggers) alone will not
be considered responsive to this call.  The key feature we seek in
submissions is the use of language technologies to understand the
molecular basis of drug response, its variability, and its impact on
phenotypes at the molecular, cellular, organ and whole organisms
level. Approaches that combine text-mining and knowledge-based systems
are of special interest.

Peer review process

All submitted papers will go through a rigorous peer-review process
that will include both programmatic relevance as well as scientific
quality.  All submissions should follow the guidelines for authors
available at the Journal of Biomedical Informatics web site
(http://www.elsevier.com/locate/yjbin).  JBI’s editorial policy is
also outlined on that page and will be strictly followed by special
issue reviewers.  Note that JBI generally publishes at least one
methodological review paper in each issue of the journal, and we would
welcome a review of the state of the art in text mining for
pharmacogenomics for this special issue.

Authors may contact the special issue lead editors
(kevin.cohen at gmail.com or russ.altman at stanford.edu) with a proposed
abstract before submission to get a sense for fit between the proposed
manuscript and the goals of the special issue.

Submission process

Authors must submit their paper via the online Elsevier Editorial
System (EES) at http://ees.elsevier.com/jbi.  Authors can register and
upload their text, tables, and figures, as well as subsequent
revisions, through this website.  Potential authors may contact the
Publishing Services Coordinator in the journal’s editorial office
(jbi at elsevier.com) for questions regarding this process.  When asked
for the category of their submission, they should indicate that it is
for the special issue on Mining the pharmacogenomics literature (which
will appear as “Pharmacogenomics” on the pull-down menu).

Submission Deadline:  March 15, 2011.
Targeted special section:  Late 2011

-- 
Kevin Bretonnel Cohen, PhD
Biomedical Text Mining Group Lead, Center for Computational
Pharmacology, U. Colorado School of Medicine
and
Lead Artificial Intelligence Engineer, The MITRE Corporation, Human
Language Technology Division
303-916-2417 (cell) 303-377-9194 (home)
http://compbio.ucdenver.edu/Hunter_lab/Cohen

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