TEXT MINING (2005)

TEXT MINING

Bioinformatics and Computational Biology

Summer School – University Complutense of Madrid

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The Biomedical literature

Introduction to Natural Language Processing

Information Retrieval in Biology

Functional annotations: Gene Ontology

Information Extraction in Biology

Evaluation of Text mining tools

Conclusions and outlook

Useful links, reviews and articles

LECTURE OVERVIEW

1- Experiments

Planning and

carrying out

experiments

(lab work)

2- Results

Processing and

interpretation of

obtained results

3- Scientific articles

'Relevant' results are

published in scientific

journals

FROM EXPERIMENTS TO ARTICLES

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DATA IN SCIENTIFIC ARTICLES

Scientific

Journals

Format

Paper structure

Article type

FREE TEXT

Title

Abstracts

Keywords

Text body

References

TABLES

FIGURES

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BIOMEDICAL LITERATURE CHARACTERISTICS

Heavy use of domain specific terminology (12% biochemistry

related technical terms).

Polysemic words (word sense disambiguation), e.g. Drosophila

genes like 'archipelago', 'capicua' or 'ebony'.

Most words with low frequency (data sparseness).

New names and terms created.

Typographical variants

Different writing styles (native languages)

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SCIENTIFIC ENGLISH ?

Most in English.

Different native

languages.

Different word

usage (preferences)

Netzel R, Perez-Iratxeta C, Bork P, Andrade MA

The way we write. EMBO Rep. 2003 May;4(5):446-51

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DIFFERENT COUNTRIES –

DIFFERENT WORD USAGE

Netzel R, Perez-Iratxeta C, Bork P, Andrade MA. The way we write. EMBO Rep. 2003 May;4(5):446-51

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PubMed DATABASE

Developed by the National Center for Biotechnology	Information (NCBI) at the National Library of Medicine NLM.

Devoted mainly life science literature.

Access through NCBI Entrez retrieval system:

http://www.ncbi.nlm.nih.gov/entrez/

Entrez: text-based search and retrieval system.

Publishers submit their citations electronically to PubMed.

Over 14 million citations from the 50th until today.

More than 48,000 journals

Some articles are indexed with MeSH terms publication types	and GenBank Accession nr.

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PubMed GROWTH

~ 450,000 new

abstracts/a

> 4,800 biomedical

journals

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PubMed web-interface

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PubMed retrieval

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PubMed retrieval

Title

Authors

Journal and publication date

Link to full text

Find similar entries

PubMed identifier (unique document ID)

Abstract

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Exercise 1.1 : PubMed

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1.1. Carry out a PubMed search for 'HIV' using the 'Limits' option.

How many articles did you retrieve? Now try to follow the research interest in HIV over time through the associated publications deposited in PubMed by constructing a 'Publication time period' vs 'number of retrieved publications' table. Start from 1980 and use time intervals of 5 years (e.g. 1980-1984, 1985-1990,...). Describe your results?

Comment: The aim of search exercise is to explore an easy way to monitor research interests related to a certain topic of research. For instance pharmaceutical companies are often interested in monitoring research interests of other companies to obtain competitive intelligence.

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Exercises 1.2. - 1.3. : PubMed

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1.2. Retrieve articles from PubMed for the Escherichia coli gene TRME_ECOLI. How many articles did your retrieve? Which problems did you encounter? Comment the obtained results. Notice that your worked with this gene before in the

1.3. Perform the same search for the Escherichia coli gene MRAZ_ECOLI Notice that you worked with that protein before, in the " Redes de Interaccion de Proteinas" session; and for the yeast gene RPE_YEAST (used in the Analisis de Secuencias session). What are the difficulties your encountered? How many documents did you retrieve?

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Natural language processing (NLP)

Logic

Informatics

Linguistics

AI

Psychology

Lexicography

Domain, e.g. Biomedicine/

Molecular Biology

Natural Language Processing

Techniques that analyse, understand

and generate language (free text, speech).

Linguistic tools, e.g. syntactic analyser and

semantic classification.

Multidisciplinary field.

Strongly language dependent.

Create computational models of language.

Learn statistical properties of language.

Methods: statistical analysis, machine learning,

rule-based, pattern-matching, AI, etc...

Domain dependent (biomedical) vs

generic NLP.

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MAIN NLP TOPICS

Domain, e.g. Biomedicine/

Molecular Biology

Automatic summarisation.

Machine translation.

Text proofing.

Speech recognition.

Optical character recognition (OCR).

Information Retrieval (IR).

Information extraction/Text mining (IE).

Question Answering (QA).

Natural Language Generation (NLG).

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INFORMATION RETRIEVAL (IR)

Domain, e.g. Biomedicine/

Molecular Biology

IR: process of recovery of those documents from a collection of documents

which satisfy a given information demand.

Information demand often posed in form of a search query.

Example: retrieval of web-pages using search engines, e.g. Google.

First step: indexing document collection:

Tokenization

Case folding

Stemming

Stop word removal

Efficient indexing to reduce vocabulary of terms and query formulations.

Example: 'Glycogenin AND binding' and 'glycogenin AND bind'.

Query types: Boolean query and Vector Space Model based query.

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BOOLEAN QUERY

Domain, e.g. Biomedicine/

Molecular Biology

Based on combination of terms using Boolean operators.

Basic Boolean operators: AND, OR and NOT.

Queries matched against the terms in the inverted index file.

Entrez – Boolean search in PubMed.

Fast, easy to implement.

Search engines: often stop word removal and case folding.

Stop word removal : space saving speed improvement.

Return a unranked list.

Return large list of documents, many not relevant.

Terms have different information content ->

better weighted query.

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GOOGLE SCHOLAR

Domain, e.g. Biomedicine/

Molecular Biology

Search engine for scholarly literature.

URL: http://scholar.google.com/

Include:

peer-reviewed papers

theses

books

preprints

abstracts

technical reports,...

Return a ranked list according to relevance to user query.

Ranking uses: full text, authors, publication type/journal, nr of citations in

scholarly literature.

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EXERCISE 2: GOOGLE SCHOLAR

Domain, e.g. Biomedicine/

Molecular Biology

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Google developed Google Scholar, in order to provide a search engine specifically for academic and research users. Try out the search queries proposed in exercises 1.3 and 1.4. using the advanced Scholar Search. Compare the results with the results of PubMed. What are the advantages and disadvantages when using Google Scholar?

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SELECTIVE DISSEMINATION OF INFORMATION

SERVICES (SDI)

Domain, e.g. Biomedicine/

Molecular Biology

Service provided by a library or data repository institution which periodically

alerts users of new publications.

New publications can be associated to certain subjects or information demands

Often based on automated iterative/periodical IR queries.

Advantages: new publications are automatically anounced (e.g. using e-mail alerts)

Disadvantages: implicit to IR based on Boolean queries, often unrelevant articles.

Free SDI services based on PubMed / Biomedical literature:

Cubby (NCBI)

PubCrawler

BioMail

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EXERCISE 3: SDI

Domain, e.g. Biomedicine/

Molecular Biology

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1.2. Set up your own selective dissemination of information service (SDI) query using the My NCBI Cubby service for a query topic of your own interest or of one of the genes discussed before.

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ZIPF'S LAW

Domain, e.g. Biomedicine/

Molecular Biology

From: Rebholz-Schuhmann D, Kirsch H, Couto F (2005) Facts from Text—Is Text Mining Ready to Deliver? PLoS Biol 3(2): e65

A small number of words occur very often

Those high frequency words are often function

words (e.g. prepositions)

Most words with low frequency .

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STOP WORD FILTERING

Domain, e.g. Biomedicine/

Molecular Biology

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VECTOR SPACE MODEL (VSM)

Domain, e.g. Biomedicine/

Molecular Biology

Measure similarity between query and documents.

(1) Document indexing , (2) Term weighting,

(3) similarity coefficient

Query: a list of terms or even whole documents.

Query as vectors of terms.

Term weighting (w) according to their frequency:

within the document (i)

within the document collection (d)

Widespread term weigthing: tf x idf.

Calculate similarity between those vectors.

Cosine similarity often used.

Return a ranked list.

Example: related article search in PubMed

w: term weight

tf: term frequency

idf: inverted document frequency

sim(Q,D): similarity between query

and document

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PubMed related article search

Title

Authors

Journal and publication date

PubMed identifier

Find similar entries

Link to full text

Abstract

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eTBlast system

http://invention.swmed.edu/etblast/index.shtml

Query input:

Article, Abstract,

reports, etc...

e-mailed results

option

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eTBlast submission

http://invention.swmed.edu/etblast/index.shtml

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eTBlast results

http://invention.swmed.edu/etblast/index.shtml

Similarity ranked

document list

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eTBlast results

http://invention.swmed.edu/etblast/index.shtml

Terms with high weight

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EXERCISE 4. eTBlast

http://invention.swmed.edu/etblast/index.shtml

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http://www.pdg.cnb.uam.es/martink/LINKS/tm_sc_ucm2005.htm

While writing a scientific article, report or a grant application, people often want to retrieve a set of documents which are related/relevant to this given work. What could/should you do in such situations? A PubMed search using alternative Boolean queries? Typically people use Boolean queries against PubMed to obtain their set of references.

You can use eTBlast instead and upload or past your free text to obtain similar articles. You can even iterate the search by selecting a subset of relevant documents retrieved in the first eTBlast round.

In case you have your own input document or are interested in certain PubMed article you can use it as your query text (or else try some of the following files: etblast_sample1.txt, etblast_sample1_trmE.txt).

Notice that eTBlast is relatively slow. Use the advance search mode, you can try out different metrics for calculating the document similarity. You can try out uploading your own stop word file: stop_word_list.txt to filter those for when calculating the document similarity.

Explain the output (ranked list). Compare the list of similar documents for a given abstract in PubMed (related article search) with the results of eTBlast. What are the advantages of using eTBlast and what are the disadvantages. Are the highlighted word (with high weight) according to your opinion relevant and discriminative?

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IR performance

Precision: fraction of relevant documents retrieved

divided by the total returned documents

Recall: proportion of relevant documents returned

divided by the total number of relevant documents

F-score: the harmonic mean of precision and recall

Precision-recall curves

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Information extraction and text mining

Identification of semantic structures within free text.

Use of syntactic and Part of Speech (POS) information.

Integration of domain specific knowledge (e.g. ontologies).

Identification of textual patterns.

Extraction of predefined entities (NER), relations, facts.

Entities like: companies, places or proteins, drugs.

Relations like: protein interactions

Methods: heuristics, rule-based systems, machine

learning and statistical techniques, regular expressions,..

TEXT MINING (2005)

Stemming

Process of removing affixes of words transforming them

to their corresponding morphological base form or root.

http://maya.cs.depaul.edu/~classes/ds575/porter.html

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POS tagging

Providing each word given a sentence with

its corresponding part of speech label , e.g.

whether it is a noun, verb, preposition,

article, etc.

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Question Answering (QA)

Humans formulate questions using natural language.

Example: What are the molecular functions of Glycogenin?.

QA: automatic generation of answers to queries in form

NL expressions from document collections.

Most systems limited to generic literature or newswire.

QA difficult: heterogeneous, poorly formalised domain,

new scientific terms

Ad hoc retrieval task of the TREC Genomics Track 2005.

Galitsky system (semantic skeletons (SSK), logical

programming).

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Natural Language Generation

NLG: constructing automatically natural language texts.

Display the content of databases: reports, error messages.

Based on semantic input, providing computer-internal

representation of the information.

Different degrees of complexity.

Biology: modelling the domain language difficult.

Simpathica/XSSYS trace analysis tool.

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Annotation of gene products: Gene Ontology

Ontology deacyclic graph structure.

Controlled vocabulary of concepts.

Three main categories:

Molecular Function

Cellular Component

Biological Process

Describe relevant biological aspects of gene products

Synonyms, links to external keywords.

Currently most important source annotation terms.

http://www.geneontology.org/

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Gene Ontology Annotation

http://www.ebi.ac.uk/GOA/ 04/22/05

Ev.C.	Annot	Perc.

IEA	6421817	0.97529

ISS	19576	0.00297

NR	2191	0.00033

ND	4433	0.00067

IPI	7130	0.00108

IGI	3014	0.00046

IMP	19072	0.00290

IDA	38862	0.00590

IEP	1495	0.00023

IC	831	0.00013

TAS	49630	0.00754

NAS	16456	0.00250

Electronic/

sequence-

based

annotation

Experimental

evidence

Curator

knowledge

TAS: Traceable Author Statement; IDA: Inferred by direct assay; IC: Inferred by curator ; ND:No data; IMP:Inferred from mutant phenotype; IGI: Inferred from genetic interaction; 3.8) IPI :Inferred from physical interaction; ISS: Inferred from sequence similarity; IEP: Inferred from expression pattern; NAS: Non traceable author statement; IEA: Inferred by electronic annotation; NR: Not recorded;

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Gene Ontology Growth

MF:Molecular Function

CC: Cellular	Component

BP: Biological Process

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Exercise 5: Gene Ontology

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Gene Ontology (GO) aims to provide standardized concepts or terms to describe relevant biological aspects. Try to use GO retrieve the ontology sub-structure for a set of terms: apoptosis, caspase, glycogenin, transcription factor (or in case you are interested in some particular function/process/compartment use your own query instead). What did you retrieve. Browse through the results and visualize the corresponding ontology graphs. What kind of relationships between terms did you find? What are the advantages of using such an ontology?

Try to explore annotation for a set of proteins, namely:

1) CASP9_HUMAN (P55211) (formerly known as ICE9_HUMAN),

2) Y1333_MYCTU (P64811) formerly known as YD33_MYCTU

3) RPE_YEAST (P46969)

by Searching the Gene Ontology Annotation database GOA. Those proteins have been used in the practical part of the Patrones, perfiles y dominios session. What are one of the weak points when using GO annotations for bioinformatics annotations? (Hint: think about domains).

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2000-2004

1995-2000

1990-1995

Before 1990

Shallow Parsing

NLP

POS - Tagging

Stemming

UMLS

Protein/Gene NER

tagging

TREC I

AI / MACHINE LEARNING

Pathology

reports

Biology

databases

Neighboring

relationships

Gene

Ontology

MEDLINE

KDD cup

JNLPBA

Shared task

TREC II

GENIA

corpus

BioCreative I

corpus

Microarrays

analysis

Protein sequence

analysis

Data resources

Assessments

Applications

Methods

Protein interactions

Automatic

Annotations

Cellular

localization

Function

prediction

New generation of

Visualization and

Browsing systems

PubMed

BioCreative 1

Neural Networks

HMMs

Bayesian Classifier

SVMs

CRFs

MEMMs

LLL05

NLP in Molecular Biology - timeline

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Text Mining applications in Biology

NER: tagging biological entities.

Automatic annotation: associating proteins to

functional descriptions.

Protein interactions: extracting interactions of

proteins, genes and drugs.

Microarray analysis: providing biological context

through literature mining

Protein localisation

Improving sequence-based homology detection.

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Text Mining applications in Biology

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Tagging biological names

Aim: Identify biological entities in articles and to link

them to entries in biological databases.

Generic NER: corporate names and places (0.9 f-score).

Biology NER: more complex (synonyms, disambiguation,

typographical variants, official symbols not used,..).

Bioinformatics vs NLP approach.

Performance organism dependent.

Methods: POS tagging, rule-based, flexible matching,

statistics, ML (naïve Bayes, ME, SVM, CRF, HMM).

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GAPSCORE

Scores words based

on a statistical model of

gene names

Quantifies:

Appearance

Morphology

Context.

Online.

http://bionlp.stanford.edu/gapscore/

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GAPSCORE

Scores words based

on a statistical model of

gene names

Quantifies:

Appearance

Morphology

Context.

Online.

http://bionlp.stanford.edu/gapscore/

Chang JT, Schütze H, and Altman RB.

GAPSCORE: Finding Gene and Protein

Names One Word at a Time.

Bioinformatics. 2004 Jan 22;20(2):216-25.

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NLProt

Online (e-mail alert).

Downloadable.

SVM-based

Pre-processing dictionary

Rule-based filtering step

PubMed words.

Precision of 75%

Recall of 76%

Chang JT, Schutze H, Altman RB. GAPSCORE: finding gene and protein names one word at a time. Bioinformatics. 2004 Jan 22;20(2):216-25.

http://cubic.bioc.columbia.edu/services/nlprot/

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ABNER

Burr Settles. "ABNER: A Biomedical Named Entity Recognizer."

http://www.cs.wisc.edu/~bsettles/abner/. 2004.

A Biomedical Named

Entity Recogniser

Downloadable.

CRF-based

Trained on BioCreative

and GENIA

orthographic and

contextual features

Can be trained on

new corpora

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iHOP

Hoffmann R, Valencia A. A gene network for navigating the literature Nat Genet. 2004 Jul;36(7):664.

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iHOP

Hoffmann R, Valencia A. A gene network for navigating the literature Nat Genet. 2004 Jul;36(7):664.

Protein centric: nucleates the literature around protein name.

For a range of model organisms (e.g. Human, yeast,..)

Hyperlinks proteins through co-occurrence

Highlight direct associations between proteins and functional

terms.

Online, fast, easy to use.

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iHOP

Hoffmann R, Valencia A. A gene network for navigating the literature Nat Genet. 2004 Jul;36(7):664.

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iHOP

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iHOP

iHOP:

Visualization

of protein

interactions

using network

graphs

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EXERCISE 6: BIO-NER

Retrieve a given abstract from PubMed searching for genes of your own research interest or alternatively for some of the following genes gene names: Caspase-9 (CASP-9, APAF-3), RPE1 (EPI1, POS18), Orc-1, Bcl-2, glycogenin, p53. Then try to tag gene and protein names from some those abstracts using different gene/protein NER tools and compare the results. If your need GenBank ids (e.g. gi:20986531) or SwissProt accession numbers (Q07817 / BCLX_HUMAN ) use: NCBI or UniProt Use some of the online applications NLProt, GAPSCORE, Yapex or BioNE recognizer (you can also download ABNER).

How do they perform? What are the common error? Which differences do you encounter? What are the main difficulties ? Which taggers do you think are useful in practice?

Explore for some of the gene symbols previously used (e.g. CASP-9, RPE1, Orc-1, glycogenin, p53 ) or for genes of your own research interest iHOP. This tool was developed at out group (PDG) at the CNB. Create a gene model for your query gene, check the results carefully, and surf through the virtual gene network of iHOP. What kind of results are obtained by iHOP? What are the advantages/disadvatages when using iHOP instead of other bio-NER tools or the PubMed retireval search?

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EXERCISE 7: From sequence to abstracts

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From sequence to abstracts

You have been using protein sequences for a range of analysis purposes in previous lectures of this course. Traditionally in case you want to obtain information related to a query sequence , after doing a sequence search (e.g. Blast against NCBI), retrieving the query genes, extracting their gene names or symbols and searching with those names PubMed you obtained the associated literature. This is a lot of work, with a lot of corresponding working steps. Those steps are integrated in the MedBlast tool.

Lets try to obtain the corresponding literature for some of the protein sequences used in other lectures (or your own query sequence of interest) for this exercise.:

Use MedBlast, a NLP based retrieval system to return relevant articles for your sequence. Notice that this system is low and sensitive to server overload! Describe the obtained results. What are the main difficulties when linking a query sequence to scientific articles?

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Extracting functional Annotations

Manual annotation extraction by database curators.

Scientific literature analysis.

Time-consuming & labour-intensive.

Example: Gene Ontology annotation (GOA).

Text mining to assist annotation extraction:

Identification of annotation relevant sentences.

Identification of protein-term associations.

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Textpresso

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KEYWORD ANNOTATION TOOL (KAT)

Perez AJ, Perez-Iratxeta C, Bork P, Thode G, Andrade MA.Gene annotation from scientific literature using mappings between keyword systems. Bioinformatics. 2004 Sep 1;20(13):2084-91. Epub 2004 Apr 1.

Extraction of mappings

between related terms

using a model of fuzzy

associations

Mesh terms/SwissProt

keywords/GO terms

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Suppl. EXERCISE 8: PROTEIN FUNCTION

The functional annotations contained in databases such as Gene

Ontology annotation (GOA) was directly or indirectly extracted from

the literature.

Several applications have been developed to associate proteins

with functional terms.

Try to use text mining applications and GOA annotations to

find functional information for your query proteins:

GOAnnotator

iHOP

GOA

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PROTEIN INTERACTIONS

Advances in experimental large scale protein

interaction analysis

Exp. Methods for protein interaction characterization:

protein arrays

mRNA expression microarrays

Yeast two-hybrid

Affinity purification with MS

X-ray, NMRFRET, chemical cross-linking,..

Bioinformatics methods for protein characterization:

Genome-based

Sequence-based

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PROTEIN INTERACTION DATABASES

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TEXT MINING & PROTEIN INTERACTIONS

Extract automatically those interactions from articles.

NL used to characterise the nature of the interaction

and its directionality.

Literature-derived interaction networks:

power law distribution

scale free topology

Visualised using network graphs.

Methods range from: simple occurrence, expert derived

word patterns (frames) to machine learning.

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PubGene

Use the co-occurrence of protein and gene names.

Assumption: co-occurrence imply biological relationship

Indexing PubMed abstracts and titles with human proteins.

Construction of interaction networks.

Build upon binary interactions between co-occurring proteins

http://www.pubgene.org/

Jenssen TK, Laegreid A, Komorowski J, Hovig E.A literature network of human genes for high-throughput analysis of gene expression.Nat Genet. 2001 May;28(1):21-8.

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iHOP

iHOP:

Visualization

of protein

interactions

using network

graphs

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SUISEKI

Relationship between the co-occurring proteins using frames

Frames: textual patterns used to express interactions

Initial set of 14 interaction words based on domain knowledge.

Examples: activate, bind, suppress

Analysed the order of protein names within sentences.

Take into account distance (off-set) between protein names.

System effective for simple interaction types.

Difficult cases: long sentences with complex

grammatical structures

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SUISEKI

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CHILIBOT

NLP-based text mining approach.

Content-rich relationship networks among biological

Concepts, genes, proteins or drugs.

Nature of the relationship: inhibitory, stimulative, neutral

and simple co-occurrence.

Internet-based application with graphical visualisation

Sentence as unit, POS tagging, shallow parsing and rules.

http://www.chilibot.net/

Chen H, Sharp BM.Content-rich biological network constructed by mining PubMed abstracts.BMC Bioinformatics. 2004 Oct 8;5(1):147.

TEXT MINING (2005)

CHILIBOT

http://www.chilibot.net/

Chen H, Sharp BM.

Content-rich biological network constructed by mining PubMed abstracts.

BMC Bioinformatics. 2004 Oct 8;5(1):147.

Need registration.

Hypothesis generation.

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PreBIND

.....

http://www.blueprint.org/products/prebind

Based on SVM.

Query protein or accession

number.

Assist the Biomolecular

Interaction Network

Database (BIND)

Donaldson I, Martin J, de Bruijn B, Wolting C, Lay V,

Tuekam B, Zhang S, Baskin B, Bader GD, Michalickova

K, Pawson T, Hogue CW.PreBIND and Textomy--mining

the biomedical literature for protein-protein interactions

using a support vector machine.BMC Bioinformatics. 2003 Mar 27;4(1):11.

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EXERCISE 6.4.: PROTEIN INTERACTIONS

Proteins instantiate their function through interactions with

other bio-molecules.

Use different text mining tools which try to extract protein interactions

for a given query protein/s (caspase, glycogenin, p53 etc...)

from texts: iHOP, PreBIND, Chilibot.

Compare your results with entries in interaction databases:

BIND, DIP , GRID , HPID, HPRD, IntAct, MINT and STRING.

What kind of output is produced by each tool?

Which differences do you encounter?

What are the difficulties encountered by those tools?

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Microarray data analysis

Co-ordinated expression of genes.

Functional co-regulation within biological processes.

Mine micro array data using the associated

biomedical literature.

Characterise groups of genes extracting functional keywords.

Score the coherence of gene clusters.

Group genes based on their associated literature and

functional descriptions.

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GEISHA

Text mining tool for microarray analysis.

Analyse the correlation between:

the increase of the level of expression patterns and

the significance of functional information derived

from the literature.

Extract functional information from the literature linked

to the microarray genes.

Calculates statistical significance of terms from

documents associated to genes of each cluster.

TEXT MINING (2005)

GEISHA

Text mining tool for microarray analysis.

Analyse the correlation between:

the increase of the level of expression patterns and

the significance of functional information derived

from the literature.

Extract functional information from the literature linked

to the microarray genes.

Calculates statistical significance of terms from

documents associated to genes of each cluster.

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PROTEIN LOCALIZATION

Protein activity -> specific cellular environments.

Localisation determination:

Experimental techniques.

Bioinformatics techniques (PSORT).

Text mining.

Nair and Rost: lexical information in annotation database

records.

Stapley et al: Use SVM to classify proteins according

to their subcellular localisation, extracted from

PubMed abstracts.

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NLP AND SEQUENCE ANALYSIS: MEDBLAST

Use NLP techniques to retrieve the related articles

for a given sequence (online).

Related articles:

those describing the query sequence (protein) or

Its redundant sequences and close homologues

Direct search with the sequence.

Indirect search with gene symbols.

Use Blast against GenBank.

Use Eutilities toolset to retrieve documents

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NLP AND SEQUENCE ANALYSIS: SAWTED

http://www.bmm.icnet.uk/~sawted/

Sequence similarity

the base for identifying

structure templates

for query sequence

Structure Assignment

With Text Description

Document comparison

algorithms

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NLP AND SEQUENCE ANALYSIS: SAWTED

Use information

contained in text

descriptions of

SwissProt

annotations

identification of

remote homologues

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COMMUNITY WIDE EVALUATIONS

CASP: Critical asessment of Protein Structure Prediction

CAMDA: Critical Assessment of Microarray Data Analysis

CAPRI: Critical Assessment of Prediction of Interactions

GASP: Genome Annotation Assessment Project

GAW: Genome Access Workshop

TREC Genomics track

CASP

CAMDA

CAPRI

GASP

GAW

PTC

JNLPBA shared task

BioCreative

KDD cup

MUC

TREC

BIOINFORMATICS

BIO-NLP

NLP/IR/IE

PTC: Predictive Toxicology Challenge

KDD: Knowledge Discovery and Data mining

JNLPBA: Joint workshop on Natural Language Processing in Biomedicine

TREC: Text Retrieval conference

MUC: Message Understanding conference

LLL05: Genic interaction extraction challenge

LLL05 challenge

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CONCLUSIONS AND OUTLOOK

BIO-NLP VERY RECENT DISCIPLINE (MAINLY 2003-TODAY).

GROWING INTEREST

NEW TECHNIQUES AND DATASETS

NEED OF USER FEEDBACK AND INTERACTIVE LEARINING

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SELECTED REVIEW REFERENCES

R. Hoffmann, M. Krallinger, E. Andres, J. Tamames, C. Blaschke and A. Valencia. Text Mining for Metabolic Pathways, Signaling Cascades, and Protein Networks. Science STKE 283, pe21 (2005).

M. Krallinger, R. Alonso-Allende Erhadt and A. Valencia. Text-mining approaches in molecular biology and biomedicine. Drug Discovery Today 10, 439-445 (2005).

M. Krallinger and A. Valencia. Applications of Text Mining in Molecular Biology, from name recognition to Protein interaction maps. In Data Analysis and Visualization in Genomics and Proteomics, chapter 4, Wiley.

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M. Krallinger and A. Valencia. text mining and information retrievalservices for Molecular Biology. Genome Biology, 6 (7), 224 (2005)

SELECTED LINKS

http://www.pdg.cnb.uam.es/martink/LINKS/bionlp_tools_links.htm

http://www.pdg.cnb.uam.es/martink/links.htm

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Acknowledgements

I would like to thank Alfonso Valencia for

his supervisions and suggestions, the Protein

Design Group at the National Biotechnology

Centre (CNB) for interesting discussions.

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2005-06-06T14:00:09