=Paper=
{{Paper
|id=Vol-435/paper-10
|storemode=property
|title=Structuring Mined Knowledge for the Support of Hypothesis Generation in Molecular Biology
|pdfUrl=https://ceur-ws.org/Vol-435/paper10.pdf
|volume=Vol-435
|authors=Marco Roos,M. Scott Marshall,Andrew Gibson,Pieter Adriaans
|dblpUrl=https://dblp.org/rec/conf/swat4ls/RoosMGA08
}}
==Structuring Mined Knowledge for the Support of Hypothesis Generation in Molecular Biology==
https://ceur-ws.org/Vol-435/paper10.pdf
Structuring mined knowledge for the support of
hypothesis generation in molecular biology
Marco Roos1, M. Scott Marshall1, Andrew P. Gibson2, Pieter W. Adriaans1
1
Informatics Institute, University of Amsterdam
Kruislaan 403, 1098 SJ Amsterdam, The Netherlands
2
Swammerdam Institute for Life Sciences, University of Amsterdam
Amsterdam, The Netherlands
{roos, marshall, adriaans}@science.uva.nl, a.p.gibson@uva.nl
Abstract. Hypothesis generation in the life sciences is an empirical process in
which obtaining and structuring knowledge from literature plays a significant
role. Text mining and Information Extraction techniques are seen as key for
programmatically accessing the knowledge captured in the form of free text.
We describe progress towards an application that supports the task of
generating a hypothesis about biomolecular mechanisms using Semantic Web
technologies and a workflow to carry out text mining in a service-oriented
architecture. The output is a semantic model with putative biological
relationships that have been extracted from literature, with each relationship
linked to the corresponding evidence. We present preliminary data that extends
a model for chromatin (de)condensation. The methodology can be used to
bootstrap the process of human-guided construction of semantically rich
biological models using the results of knowledge extraction processes.
Keywords: Knowledge extraction, Hypothesis support, Molecular biology,
Chromatin, Web service, Workflow, Semantic Web, OWL
1 Introduction
Conceiving or improving a hypothesis about a biomolecular mechanism usually
implies integration of various types of information and distillation into a
comprehensible model. This includes information from literature, our own
knowledge, and interpretations of experimental data. Many Web resources such as
Entrez PubMed1 provide such information. However, the difficulty of information
retrieval from literature reveals the scale of today’s information overload: over 17
million biomedical documents are now available from PubMed. Support for
extracting information from these resources is therefore a general requirement, with
many scientists finding it increasingly challenging to ensure that all potentially
1 http://www.ncbi.nlm.nih.gov/pubmed/
relevant facts are considered whilst forming a hypothesis. Developments in the area of
information extraction promise to deliver applications that will more directly support
the task of hypothesis generation. The general approach requires retrieving relevant
documents, recognizing named entities (e.g. proteins) and their relationships, and
storing results for later inspection [6, 10].
In this study, we address the question of how the results of a knowledge extraction
procedure should be stored to best support hypothesis conception for experimental
biology. In particular, we focus on epigenetics and chromatin research, where typical
examples are qualitative hypothetical models that attempt to explain the role of
various proteins in changing the level of condensation of DNA as a means to regulate
transcription (see for instance [12]). To support the linking of a knowledge extraction
process to this type of modelling, we present an approach that extracts information
from text and populates an OWL-based knowledge base with the extraction results.
2 Methods and tools for knowledge extraction
Knowledge extraction was performed by web services from the Adaptive Information
Discovery Application (AIDA) toolbox, a set of web services and infrastructure being
developed for knowledge extraction and knowledge management in a virtual
laboratory for e-science1. It contains services for document retrieval based on Lucene2
[7], entity and relation recognition applying conditional random fields [5], and access
to Sesame [1], a RDF repository that serves as our knowledge base. Ontologies were
created in Protégé and conform to the OWL1.1 specification.
The general steps of the knowledge extraction process [6, 10] were implemented as
a workflow in Taverna [3]. We added steps to provide a likelihood score, cross
references to biological databases, and tabular results (Fig. 1). The likelihood of
finding a document with query (q) and discovery (d) was calculated by:
QDexp
, QDexp / N , in which Q,
Query Q
Add query to
log
semantic model QD D
Retrieve documents
from Medline D, and QD are the frequencies of documents
containing q, d, and q and d; QDexp is the
Add documents (IDs)
to semantic model
Extract proteins expected frequency of documents containing
(Homo sapiens)
q and d assuming independence of Q and D;
Add proteins to
semantic model N is the total number of documents in
Calculate
ranking scores
MedLine. The workflow further contains a
Add scores to web service for adding protein name
Create biological
semantic model synonyms to the original query and
cross references providing UniProt identifiers for human, rat,
Add cross references and mouse that we also used to filter false
to semantic model
Convert to positives. This service, kindly provided by
table (html)
Martijn Schuemie, wraps components from
Fig. 1 - Workflow to extract proteins from the text analysis tool Anni2.0 [4]. At each
literature and store them in a knowledge base.
1 http://adaptivedisclosure.org
2 http://lucene.apache.org/
step in the workflow, the results are converted into OWL instance statements in RDF
format in order to populate the ontologies pre-loaded in our knowledge base.
References to our scientific research objects (ontologies, workflows, AIDA
services) are stored as a pack on myExperiment.org that is available for download
upon request (http://www.myexperiment.org/packs/27).
3 Model Representation in OWL
3.1 Different types of knowledge
In order to represent our biological hypothesis, we would like an OWL ontology of
the relevant biological domain entities and their biological relationships. The purpose
of our knowledge extraction procedure is to populate this model with instances. We
would also like to model the evidence that has led to these instances. This leads to a
clash between our intention of enriching a biological model, and representing the
artifacts of a text mining procedure such as ‘term’, ‘interaction assertion’, or ‘term
collocation’. For these, we have concrete instance but that have no direct meaning in
the biological domain. Within our OWL representation, we purposefully kept five
distinct OWL models in order to avoid the conflation of knowledge from the different
stages of our knowledge extraction process. Our models represent:
Biological knowledge for our hypothesis (Protein, Association)
Documents (Terms, PubMed Identifiers)
Knowledge extraction process (Workflows, Processes)
Mined results (Extracted terms, extracted relationships)
Mapping model to integrate the above through references.
Decondensed chromatin
HDAC HAT Histone methylation at H3K9
Histone
acetylation DNA methylation
Condensed chromatin
Fig. 2 – Example biological model: cartoon representation of a hypothesis for a chromatin
(de)condensation mechanism. HDAC and HAT refer to enzymes with histone deacetylase
activity and histone acetylase activity, respectively. For more details see figure 3 in [12] on
which this figure is based.
BiologicalModel
Protein
Association
Protein hasParticipant some
Chromatin
condensation PCAF HDAC1-PCAF
HDAC1 interaction
hypothesis
hasModelComponent hasParticipant
hasParticipant
hasModelComponent
hasModelComponent
Fig. 3 - Biological domain model for hypothesis support with example instances. HDAC11 and
PCAF2 are examples of proteins implied in chromatin (de)condensation and known to interact.
In this and following figures, diamonds represent instances, dashed arrows connected from
diamonds instance-of relationships. The other dashed arrows represent properties between
classes or instances. For clarity inverse relationships are not shown.
3.1.1 Biological model
In the context of our example hypothesis (Fig. 2) we start with a minimal set of
classes for a biological model with proteins and protein-protein associations (Fig. 3).
We cannot directly inspect concrete instances of proteins or their interactions. We
regard instances in the biological model as interpretations of certain observations, in
our case, of text mining results. We also do not consider such instances as biological
facts; they are restricted to a hypothetical model. The evidence for the interpretation is
important, but it is not within the scope of this model. In the case of text mining,
evidence is modeled by the document and text mining models.
3.1.2 Document model
A model of the structure of documents and statements therein is less ambiguous than
the biological model, because we can directly inspect concrete instances such as
(references to) documents or pieces of text (Fig. 4). We can be sure of the scope of
the model and we can be clear about the distinction between classes and instances
because we computationally process the documents. For our knowledge extraction
experiment, we have created classes for documents, protein or gene terms, and
mentions of associations between proteins or genes. Unfortunately, we cannot make a
distinction between proteins and genes at this stage due to the limits of biological text
mining.
1 http://www.uniprot.org/uniprot/Q13547
2 http://www.uniprot.org/uniprot/Q92831
Protein or gene
Document association
assertion
Protein
Association
or gene
term
term
“p68 and p72
PMID: associate with
“HDAC1” “p68” histone
15298701
“associate” deacetylase 1
(HDAC1)”
isComponentOf
relatesBy
isComponentOf relates
relates
isComponentOf
Fig. 4 – Basic ontological model that represents the relationship between documents and terms
and statements used in the text.
3.1.3 Text mining model
Next, we want to structure what we know of the knowledge extraction process that
may serve as evidence for the population of our biological model (Fig. 5). The aim of
this step is to create assertions about instances of text mining processes, which
Retrieved
Discovery document
score Discovered
protein or
gene term
Discovered
Text mining association
process term
Document
search
Discovered
query
association
assertion
PMID:
15298701 “p68”
AIDA based
“HDAC1 AND extraction “interacts”
chromatin” process “p68 and p72
discoveredBy associate with
searchesWith
histone
discoveredBy deacetylase 1
(HDAC1)”
discoveredBy
4.78 discoveredBy
hasDiscoveryScore
Fig. 5 – The knowledge extraction model with defined classes that classify instances from the
text model as text mining discoveries. For clarity, property restrictions between classes and
model components of the text mining process are not shown.
process instances of documents that contain instances of terms. In addition, in this
model we represent information about the likelihood of terms and relationships being
found in the literature. We also gain valuable knowledge provenance that can be used
to track down any conflicting statements later on. This allows us to qualify the
uncertainty of the text mining procedure. For more complete knowledge provenance,
we have also created a semantic model representing the implementation of the text
mining process as a workflow of (AIDA) Web Services (not shown).
3.1.4 Mapping model
At this point, we have a clear framework for the description of our biological domain
and the documents and the text mining results as instances in our document and
process ontologies. The next step is to relate the mined information to the biological
domain model. Our strategy is to initially keep the domain model simple at the class
and object property level, and to map sets of instances from our results to the domain
model. For this, we created an additional mapping model that defines reference
properties between the models (Fig. 6). We can now see that an interaction between
the proteins labeled ‘p68’ and ‘HDAC1’ in our hypothetical model is referred to by a
mention of an association between the terms ‘p68’ and ‘HDAC1’, with a likelihood
score for finding this combination in literature.
The difficulty of distinguishing between genes and proteins during text mining also
presents a problem for mapping to the biological model. When the number of proteins
is small enough we may choose to initially map the text mining results to proteins, or
we could create a perhaps more factual ‘gene or protein’ class in the biological model.
Document
BiologicalModel search
references
query
Discovered
Protein
protein
or gene references
term
Discovered
Association association
references
assertion
Chromatin HDAC1-p68
condensation association
hypothesis “HDAC1” “p68”
“p68 and p72
HDAC1
associate with
references histone
p68 deacetylase 1
(HDAC1)”
references
references
Fig. 6 – Mined knowledge mapping strategy. Instances from the results set (right) refer to
instances in the domain model (left).
4 Preliminary results
The final result of the knowledge extraction workflow is a knowledge base
extended with text mining results captured in OWL. We performed an example
experiment starting with the query ‘HDAC1 AND chromatin’. As a result we could
query our knowledge base to find an instance of our biological hypothesis model and
its partial representation by the input query and its expanded form (35 synonyms were
added for document retrieval). We could further find 257 proteins linked to this model
as putative components. We could also recover that these links were discovered
through 489 protein terms found in 276 documents, and by what process, Web
Service and workflow. The data is per individual: for each we stored its specific links
to other individuals within a domain (e.g. the biological) and between domains. For
instance, NF-KappaB is linked to our initial hypothesis and ‘HDAC1’ within the
biological model, and to its associated term which was found in 10 abstracts. As our
knowledge base grows with instances and different types of evidence we can perform
increasingly interesting queries in search of novel relations with respect to our nascent
hypothesis. A prototypical example is the protein referred to by the term ‘p68’ that
was found to be collocated with the query term ‘HDAC1’ and also in a direct mention
of this interaction in an abstract by Wilson et al. [13], suggesting p68 as a candidate
for investigating its role in relation to HDAC1 and chromatin.
5 Conclusion
We have demonstrated first steps towards automating support for the processes
involved in the formation of scientific hypotheses, particularly in studying
biomolecular mechanisms. Text mining supports a researcher by inspecting more
papers than an individual could and without human bias, while the use of an OWL-
based knowledge base supports exploration of semantic relationships of one or many
experiments. Our focus is on modeling information that is extracted during a
computational experiment, rather than on improving a particular text mining
procedure. The approach is not limited to the modeling of text mining results but
could be applied to the results of other computational experiments. Our method shares
some features with the general task of ontology learning from text [2, 9], and that of
populating a predefined ontology with instances obtained from text mining [14].
However, our aim is to provide a method for improving and reusing a biological
hypothesis. We do not aim to construct a comprehensive hierarchy for a domain, nor
are we specifically interested in recall as long as the text mining is reasonably
unbiased. Semantic Web standards and tools allow us to explicitly represent the
biological knowledge, share it as a resource online, and make it interoperable with
other knowledge resources. Models representing provenance add a layer of trust into
the results because the biological assertions are verifiable. It will be interesting to see
how much our approach can make use of the data provenance in future versions of
Taverna [8]. The rich potential of Semantic Web technologies will support the future
extension of the domain model to suit more complex knowledge; its exploration
hopefully supported by increasingly user friendly query tools and DL-reasoners [11].
Acknowledgements
We thank Edgar Meij, Sophia Katrenko, Willem van Hage, and Martijn Schuemie for providing
Web Services, and the myGrid team and OMII-UK for their support. This work was carried out
for the Virtual Laboratory for e-Science project (http://www.vl-e.nl) and BioRange, supported
by BSIK grants from the Dutch Ministry of Education, Culture and Science (OC&W). VL-e is
part of the ICT innovation program of the Ministry of Economic Affairs (EZ).
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