Literature Mining
for the Biologists
Santhosh J. Eapen
sjeapen@spices.res.in
Present scenario
• Generation of large
scale literature data
• no longer possible for
a researcher to keep
up-to-date with all
the relevant
literature manually
What is Literature Mining?
• For an average biologist
– Keyword search in PubMed/CeRa/CAB Abstracts
– ‘maps of science’ that cluster papers together on
the basis of how often they cite one another, or by
similarities in the frequencies of certain keywords
Machine learning
The ability of a machine to learn from
experience or extract knowledge from
examples in a database. Artificial
neural networks and support-vector
machines are two commonly used
types of machine-learning method.
Literature Mining
• To identify relevant articles (Information Retrieval - IR)
• For recognizing biological entities mentioned
in these articles (Entity recognition - ER)
• To enable specific facts to be pulled
out from papers (Information
Extraction - IE)
Text mining or Data mining
• Integrate the literature with other large data
sets such as genome sequences, microarray
expression studies, or protein–protein
interaction screens
• Dig out the deeper meaning that leads to
biological discoveries
Current status of
biological literature
mining
IR – Information Retrieval
• to identify the text
segments (be it full
articles, abstracts,
paragraphs or sentences)
that pertain to a certain
topic
Tools for IR
Problem setting
• Given a set of documents,
compute a representation, called index
<1 0 0 1 0 1>
<1 1 0 0 0 1>
<0 0 0 1 1 0>

• to retrieve, summarize, classify or cluster them
Problem setting
• Given a set of genes (and their literature),
•
compute a representation, called gene index
<1 0 0 1 0 1>
<1 1 0 0 0 1>
<0 0 0 1 1 0>

• to retrieve, summarize, classify or cluster them
Vector
space
model
 Document processing
Remove punctuation & grammatical structure
Define a vocabulary
•
•
•
•
(`Bag of words’)
Identify Multi-word terms (e.g., tumor suppressor) (phrases)
Eliminate words low content (e.g., and, thus, gene, ...)
(stopwords)
Map words with same meaning
(synonyms)
Strip plurals, conjugations, ...
(stemming)
Define weighing scheme and/or transformations
 Compute index of textual resources:
gene
vocabulary
(tf-idf,svd,..)
Biomedical Text Mining: Methods
•
•
•
•
•
•
•
•
Databases
Natural Language Processing
Information Retrieval
Information Extraction
Ontologies
Clustering
Classification
Visualization
Gene Ontology
A
set
of
controlled
vocabularies that are used to
describe
the
molecular
functions of a gene product,
the biological processes in
which it participates and the
cellular components in which
it can be found.
MeSH terms
A controlled vocabulary that
is used for annotating
Medline abstracts. Several
classes of MeSH term exist,
the most relevant for
literature
mining
being
‘Chemicals and Drugs’ (MeSHD) and ‘Diseases’ (MeSH-C).
Example
Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1 and
this modification served as a priming step to
promote subsequent Cdc5-dependent Swe1
hyperphosphorylation and degradation
Ad hoc IR
• These systems are very useful since the user can
provide any query
– The query is typically Boolean (yeast AND cell cycle)
– A few systems instead allow the relative weight of
each search term to be specified by the user
• The art is to find the relevant papers even if they
do not actually match the query
– Ideally our example sentence should be extracted by
the query yeast cell cycle although none of these
words are mentioned
Automatic query expansion
• In a typical query, the user will not have provided
all relevant words and variants thereof
• By automatically expanding queries with
additional search terms, recall can be improved
– Stemming removes common endings (yeast / yeasts)
– Thesauri can be used to expand queries with
synonyms and/or abbreviations (yeast / S. cerevisiae)
– The next logical step is to use ontologies to make
complex inferences (yeast cell cycle / Cdc28 )
Document similarity
• The similarity of two documents can be defined
based on their word content
– Each document can be represented by a word vector
– Words should be weighted based on their frequency
and background frequency
– The most commonly used scheme is tf*idf weighting
• Document similarity can be used in ad hoc IR
– Rather than matching the query against each
document only, the N most similar documents are also
considered
Document clustering
• Unsupervised clustering algorithms can be
applied to a document similarity matrix
– All pairwise document similarities are calculated
– Clusters of “similar documents” can be constructed
using one of numerous standard clustering methods
• Practical uses of document clustering
– The “related documents” function in PubMed
– Logical organization of the documents found by IR
Entity recognition
• An important but boring problem
– The genes/proteins/drugs mentioned within a given
text must be identified
• Recognition vs. identification
– Recognition: find the words that are names of entities
– Identification: figure out which entities they refer to
– Recognition without identification is of limited use
Example
Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1 and
this modification served as a priming step to
promote subsequent Cdc5-dependent Swe1
hyperphosphorylation and degradation
Entities identified
– S. cerevisiae proteins: Clb2 (YPR119W), Cdc28
(YBR160W), Swe1 (YJL187C), and Cdc5 (YMR001C)
Co-occurrence extraction
• Relations are extracted for co-occurring entities
– Relations are always symmetric
– The type of relation is not given
• Scoring the relations
– More co-occurrences  more significant
– Ubiquitous entities  less significant
– Same sentence vs. same paragraph
• Simple, good recall, poor precision
Example
Mitotic cyclin (Clb2)-bound Cdc28 (Cdk1
homolog) directly phosphorylated Swe1 and this
modification served as a priming step to promote
subsequent Cdc5-dependent Swe1
hyperphosphorylation and degradation
Relations
– Correct: Clb2–Cdc28, Clb2–Swe1, Cdc28–Swe1, and
Cdc5–Swe1
– Wrong: Clb2–Cdc5 and Cdc28–Cdc5
Mining text for
nuggets
• New relations can be
inferred from published
ones
– This can lead to actual
discoveries if no person
knows all the facts required
for making the inference
– Combining facts from
disconnected literatures
• Swanson’s pioneering work
– Fish oil and Reynaud's
disease
– Magnesium and migraine
Integration
• Automatic annotation of high-throughput data
– Loads of fairly trivial methods
• Protein interaction networks
– Can unify many types of interactions
– Powerful as exploratory visualization tools
• More creative strategies
– Identification of candidate genes for genetic diseases
– Linking genes to traits based on species distributions
Tools for information retrieval
E-BioSci
http://www.e-biosci.org
EBIMed
http://www.ebi.ac.uk/Rebholz-srv/ebimed
Google Scholar
http://scholar.google.com
GoPubMed
http://www.gopubmed.org
MedMiner
http://discover.nci.nih.gov/textmining
PubMed
http://www.pubmed.org
PubFinder
http://www.glycosciences.de/tools/PubFinder
Textpresso
http://www.textpresso.org
XplorMed
http://www.ogic.ca/projects/xplormed
ER & IE Tools
Entity recognition
iHOP
http://www.pdg.cnb.uam.es/UniPub/iHOP
Information extraction
iProLINK
http://pir.georgetown.edu/iprolink
JournalMine.
http://textmine.cu-genome.org
PreBIND
http://prebind.bind.ca
PubGene
http://www.pubgene.org
Text mining & integration tools
Text mining
Arrowsmith
http://arrowsmith.psych.uic.edu
LitInspector
http://www.litinspector.org/
CoPub
http://services.nbic.nl/cgi-bin/copub/CoPub.pl
Genei
http://cbdm.mdc-berlin.de/tools/genie/
BeeSpace Navigator
Integration
BITOLA
www.beespace.illinois.edu
G2D
http://www.ogic.ca/projects/g2d_2
ProLinks
http://dip.doe-mbi.ucla.edu/pronav
STRING
http://string.embl.de
http://www.mf.uni-lj.si/bitola
Permission denied
• Open access
– Literature mining methods cannot retrieve, extract, or
correlate information from text unless it is accessible
– Restricted access is already now the primary problem
• Standard formats
– Getting the text out of a PDF file is not trivial
– Many journals now store papers in XML format
• Where do I get all the patent text?!
Thank You