CS276B Text Information Retrieval, Mining, and Exploitation Lecture 16 Bioinformatics II March 13,...

CS276BText Information Retrieval, Mining, and

Exploitation

Lecture 16Bioinformatics IIMarch 13, 2003

(includes slides borrowed from J. Chang, R. Altman, L. Hirschman, A. Yeh, S. Raychaudhuri)

Bioinformatics Topics

Last week Basic biology Why text about biology is special Text mining case studies

Microarray analysis, Abbreviation mining

Today Combined text mining and data mining I

Text-enhanced homology search Text mining in biological databases KDD cup: Information extraction for bio-

journals Combining text mining and data mining II

Text-Enhanced Homology Search(Chang, Raychaudhuri, Altman)

Sequence Homology Detection

Obtaining sequence information is easy; characterizing sequences is hard.

Organisms share a common basis of genes and pathways.

Information can be predicted for a novel sequence based on sequence similarity:

Function Cellular role Structure

PSI-BLAST Used to detect protein sequence

homology. (Iterated version of universally used BLAST program.)

Searches a database for sequences with high sequence similarity to a query sequence.

Creates a profile from similar sequences and iterates the search to improve sensitivity.

PSI-BLAST Problem: Profile Drift

At each iteration, could find non-homologous (false positive) proteins.

False positives create a poor profile, leading to more false positives.

Addressing Profile Drift

PROBLEM: Sequence similarity is only one indicator of homology.

More clues, e.g. protein functional role, exists in the literature.

SOLUTION: we incorporate MEDLINE text into PSI-BLAST.

Modification to PSI-BLAST

Before including a sequence, measure similarity of literature. Throw away sequences with least similar literatures to avoid drift.

Literature is obtained from SWISS-PROT gene annotations to MEDLINE (text, keywords).

Define domain-specific “stop” words (< 3 sequences or >85,000 sequences) = 80,479 out of 147,639.

Use similarity metric between literatures (for genes) based on word vector cosine.

Evaluation

Created families of homologous proteins based on SCOP (gold standard site for homologous proteins--http://scop.berkeley.edu/ )

Select one sequence per protein family: Families must have >= five members Associated with at least four references Select sequence with worst performance

on a non-iterated BLAST search

Evaluation

Compared homology search results from original and our modified PSI-BLAST.

Dropped lowest 5%, 10% and 20% of literature-similar genes during PSI-BLAST iterations

Results

46/54 families had identical performance 2 families suffered from PSI-BLAST drift,

avoided with text-PSI-BLAST. 3 families did not converge for PSI-BLAST,

but converged well with text-PSI-BLAST 2 families converged for both, with slightly

better performance by regular PSI-BLAST.

Discussion

Profile drift is rare in this test set and can sometimes be alleviated when it occurs.

Overall PSI-BLAST precision can be increased using text information.

Mining Text inBiological Databases

Where is the Information?What is the Data?

GenBank – genetic sequences Swiss-prot – protein sequences DNA chips / microarrays Metabolic pathways Signaling pathways / regulatory

networks Medline – biomedical literature Taxonomies / Ontologies

Genetic Information in GenBank

100.00

1000.00

10000.00

100000.00

1000000.00

10000000.00

100000000.00

1000000000.00

10000000000.00

100000000000.00

1983 1988 1993 1998

Base Pairs

Sequences

•Numbers are for all species.

•Biology is fundamentally an information science.

Species represented in GENBANK

Entries Bases Species 4323294 7028540140 Homo sapiens 2595599 1385749133 Mus musculus 166778 488340565 Drosophila melanogaster 182124 247830592 Arabidopsis thaliana 114669 203787073 Caenorhabditis elegans 189000 165542107 Tetraodon nigroviridis 159412 136005048 Oryza sativa 219183 107771966 Rattus norvegicus 166688 75404535 Bos taurus 155647 68679866 Glycine max 109941 56390403 Lycopersicon esculentum 70448 51527034 Hordeum vulgare 104773 51202716 Medicago truncatula 91352 50512383 Trypanosoma brucei 56416 49410018 Giardia intestinalis 77536 47598841 Strongylocentrotus purpuratus 49939 44524589 Entamoeba histolytica 86706 42479448 Danio rerio 79696 37899117 Zea mays 71318 37381894 Xenopus laevis

Complete GenomesAquifex aeolicus Aquifex aeolicus Archaeoglobus fulgidus Archaeoglobus fulgidus Bacillus subtilis Bacillus subtilis Borrelia burgdorferi Borrelia burgdorferi Chlamydia trachomatis Chlamydia trachomatis Escherichia coli Escherichia coli Haemophilus influenzae Haemophilus influenzae Methanobacterium Methanobacterium thermoautotrophicum thermoautotrophicum

Caulobacter crescentusCaulobacter crescentus

Helicobacter pyloriHelicobacter pyloriMethanococcus jannaschii Methanococcus jannaschii Mycobacterium Mycobacterium tuberculosis tuberculosis Mycoplasma genitalium Mycoplasma genitalium Mycoplasma pneumoniae Mycoplasma pneumoniae Pyrococus horikoshii Pyrococus horikoshii Treponema pallidumTreponema pallidumSaccharomyces cerevisiaeSaccharomyces cerevisiae Drosophila melanogasterDrosophila melanogasterArabidopsis thalianaArabidopsis thalianaHomo sapiensHomo sapiens

Protein Sequences

Swiss-prot (as of 3/03) 122,564 sequences Almost 45,000,000 total amino

acids 103,486 references

http://www.expasy.ch/sprot/

Three-Dimensional Structures

Protein three-dimensional Structures Protein Data Bank (PDB), as of March

27, 2001 13,158 proteins 939 nucleic acids 616 protein/nucleic acid complex 18 carbohydrates

http://www.rcsb.org/pdb/

networks Medline – biomedical literature

Completeyeastgenome(6000 genes)on a chip.

Online access to DNA chip Data

http://genome-www4.stanford.edu/MicroArray/SMD/

O(10) data sets available from Stanford site 10,000 to 40,000 genes per chip Each set of experiments involves 3 to 40 “conditions” Each data set is therefore near 1 million data points.

People gearing up for these measurements everywhere…

A Reaction in EcoCYC

Signaling Pathways

Where’s the Information? Medical Literature on line. Online database of published literature

since 1966 = Medline = PubMED resource

4,000 journals 10,000,000+ articles (most with

abstracts) www.ncbi.nlm.nih.gov/PubMed/

PubMed

SwissProt103,000 references100s Mb of text100,000s unique words

Abstracts Referenced in SP37

Number of abstracts associated with sequences in Swiss Prot.

(# sequences truncated at 100)

(as of 2001)

MESH = Medical Entity Subject Headings

Controlled vocabulary for indexing biomedical articles.

19,000 “main headings” organized hierarchically

Browser at http://www.nlm.nih.gov/mesh/MBrowser.html

UMLS: Semantic Model of Biomedical Language

Representing more of semantics of words and more relationships.

UMLS = Unified Medical Language System

http://www.nlm.nih.gov/research/umls/

UMLS Elements Semantic concepts (475K) = specific terms

connected to semantic categories (e.g. Munchausen syndrome linked to Behavioral-Dysfunction)

Concept maps (1,000K) = mapping from a terminology to a semantic concept (e.g. ICD-9 Billing code to Munchausen syndrome)

Categorizations = relate semantic concepts Conceptual links (7K) = relate two semantic

concepts with a semantic relationship

Gene Ontology(http://www.geneontology.org/)

A controlled listing of three types of function:

Molecular Function Biological Process Cellular Component

Vision: universal language for molecular biology across species

Molecular Function

<molecular_function ; GO:0003674 %anti-toxin ; GO:0015643 %lipoprotein anti-toxin ; GO:0015644

%anticoagulant ; GO:0008435 %antifreeze ; GO:0016172 %ice nucleation inhibitor ; GO:0016173

%antioxidant ; GO:0016209 %glutathione reductase (NADPH) ; GO:0004362 ; EC:1.6.4.2 % flavin-

containing electron transporter ; GO:0015933 % oxidoreductase\, acting on NADH or NADPH\, disulfide as acceptor ; GO:0016654

%thioredoxin reductase (NADPH) ; GO:0004791 ; EC:1.6.4.5 % flavin-containing electron transporter ; GO:0015933 % oxidoreductase\, acting on NADH or NADPH\, disulfide as acceptor ; GO:0016654

Current Genome Annotationshttp://www.geneontology.org

KDD Cup 2002:Information Extraction for

Biological Text

Task Background: Flybase

Flybase project Curates biomedical publications on the fruitfly Uses GO (gene ontology) as ontology Fruitfly (Drosophila melanogaster) is one of the key “model

organisms” Flybase goals

Distillation of literature on the fruitfly Table of contents function Support search of literature

Current methodology: Manual curation Curators read the literature and manually update flybase

Goal of KDD Cup 2002: Can this be (partially) automated?

FlyBase: Example of Data Curation

Curators Cannot Keep Up with the Literature!

FlyBase References By Year

Task Rationale and Description FlyBase provided the

Data annotation (plus biological expertise) Input on the task formulation

What can be useful to the curators

Start fairly simple. Try to help automate part of what one group of FlyBase curators needs to do:

Determine which papers need to be curated for fruit fly gene expression information

Want to curate those papers containing experimental results on gene products (RNA transcripts and proteins)

Abstracts are not enough, need the full papers

E.g., for one paper on Appl proteins (PubMed ID #8764652), FlyBase lists 19 “when-where” pairs for Appl protein expression

A “when-where” pair indicates when in the life cycle and where in the body some transcript or protein is found

“When-where” pair example: adult-brain Only 2 of the 19 pairs (11%) are mentioned in the

abstract. The rest are only mentioned in the body of the full paper

So need full papers in electronic form

Some Data (Text) Preparation Challenges

Full papers are copyrighted by publishers For the contest, only use “free” papers

As a result of all these complications, out of the ~7100 papers in FlyBase that were of interest only ~1100 were used

Some Data (Text) Preparation Challenges

Plain text is not enough, also need things like superscripts, subscripts, italics, Greek letters (in English text)

E.g., represent alleles (variants of a gene) with superscripts

Some Appl gene alleles: Appl , Appl , Appl If lose the superscripts, these appear as:

Appld, Appls, Applsd This would make it harder to determine that

these refer to the same gene Need to know what suffixes to remove before

trying to match

Some Data (Text) Preparation Challenges (Continued)

d s sd

FlyBase has certain conventions to represent superscripts, etc. in ASCII

E.g., represent those alleles as Appl[d], Appl[s], Appl[sd]

In general, gene and protein names are already hard to match because they often have a complicated word structure (morphology)

One needs to know what morphological transformations (like prefix or suffix removal) to perform before attempting to match the names

Some Data (Text) Preparation Challenges (Continued)

Information Extraction Task

Given for each paper The full text of that paper A list of the genes mentioned in that paper

Determine for each paper For each gene mentioned in the paper,

does that paper have experimental results for

Transcript(s) of that gene (Yes/No)? Protein(s) of that gene (Yes/No)?

Task is Harder Than It First Appears

Interested in results applicable to “regular” (found in the wild) flies, not mutants

Genes have multiple names (synonyms) Given a list of the known synonyms But list may be incomplete

Some names can refer to multiple genes E.g., “Clk” is a symbol for one gene (Clock) and is

also a synonym for another gene (period, symbol is “per”)

Contestants given evidence of experimental results found in the training data,

But only in the form that is recorded in the FlyBase database

Training Data in Flybase

Database (DB) records what evidence is found in a training paper, but not where in that paper

The evidence is often recorded in a “normalized” form and domain knowledge is needed to find the corresponding text, e.g.,

DB: Assay mode: “immunolocalization”Text (PubMed ID#9006979): “Figure 12. …Whole-mount tissue staining using an affinity-purified anti-PHM antibody in the CNS … This view displays only a portion of the CNS”

Term “immunolocalization” is not in the text Instead, text describes the process of

performing an immunolocalization

Typical NLP Training Data:More Detailed

These systems assume every mention of an entity or relation of interest in the text is annotated

So anything not annotated is not a mention E.g., Annotations to train a “Northern blot”

detector:Paper #7540168: ... transcripts on Northern analyses, raising questions whether @norpA@ ... @Northern Blots@ ... Northern blots were carried out as described by Zhu @et al.@(1993) ... @Northern Analysis of Adult RNA@ ... Figure 3: Northern blot analysis of @norpA@ transcripts in adult ... IThis paper has a total of 19 mentions.

Task Details

Task has 3 sub-tasks, that contribute equally to the overall score

1. Ranked-list of papers (curatable before non-curatable)

2. Yes/No decisions on the papers being curatable (having any results of interest)

3. Yes/No decisions for having results for each type of product (transcript, protein) for each gene mentioned in a paper

Some Numbers

Training set: 862 articles Test set: 213 articles (non-public!) Time Allowed

Release training set, wait ~6 weeks Release test set, results due ~2 weeks

later 18 teams submitted 32 entries Entries from 7 “countries”:

Japan, Taiwan, Singapore, India, UK, Portugal, USA

About equal numbers of universities and companies

Evaluation measure: F measure

Winner: a team from ClearForest and Celera Used manually generated rules and

patterns to perform information extraction Also had the best score in each of the 3

sub-tasks Best MedianRanked-list: 84% 69% Yes/No curate paper: 78% 58%Yes/No gene products: 67% 35%

Results

Summary

Reliance on partial annotations is key. “Information retrieval” task easiest to

solve and immediately useful. Electronic availability of full-text is big

issue. Mundane format problems (subscripts etc)

are a big issue. Best results were 67% for information

extraction.

Curated Databases

Flybase is an example of a curated database.

A lot of biological research is organized around such databases (cf. building and publishing software packages in CS)

There are hundreds (thousands?) of curated databases.

13 important databases just for one area: nuclear receptors.

Maintaining curated databases is labor-intensive.

Curated Databases

Text mining can be used for: Cost savings Time savings Consistency Freshness

Curated Databases: Uses

Protein-protein interactions Which proteins interact with X?

Support information retrieval Find all transcription factors that are

involved in cell death Interpretation of data-intensive

experiments Microarray case study presented last week

In silico biology

E-Cell (http://e-cell.org/)

Curated Databases: Uses (cont.)

Summary/selection of what is known Support search Knowledge discovery

Contradictory findings Nobel Prize

He/She who points out a critical gene-disease link first, wins the Nobel Prize.

You better do a thorough literature search.

CombiningText Mining and Data Mining

Combining Text and Links

Recall: Classifying a web document based on The text they contain The categories of other pages pointing to it The categories of other pages it is pointing

to Also

Usage information (Pitkow et al.)

Clustering: Example(Eisen et al.)

Combining Gene Expression&Text

Clustering of genes in a microarray experiment

Last week Clustering based on text only, or: Clustering based on gene expression only

What about combining the two? There is a large number of “good

clusterings” for a particular problem Use literature to guide clustering

Comments

Yeast : genes were grouped by expression. Functional labels guided us to find key subgroups. Once key subgroups are identified, supervised approaches

can refine identification process.

Cancer : cell line were grouped by semantic category (hypoxia versus normoxia).

Used supervised approaches to refine identification process

Literature as a guide

Free text documentation is widely available

Patient records to describe pathological specimens

~20,000 documents describing specific yeast genes

May have the information to guide us in searching for similarities in genes and expression

Goal of algorithm

To identify subgroups of genes with commonalities in gene expression and in biological function.

Literature is the means by which we identify functional commonalities

Projections in Linear Discriminant Analysis

A normal distribution is estimated for the features of each population of the training set.

Each distribution is centered at the mean of the population

Linear discriminant analysis assumes a pooled covariance matrix.

Our approach

Look for projections that separate specific groups of genes

In a good projection, the separated genes have some functional commonalities

These commonalities should be evident in the gene literature

Challenges

C1 : Can we identify biologically meaningful concepts from simple text representations?

C2 : In a group of genes with some biological similarity, can we detect that similarity in the literature?

C3 : Can we then find projections in the expression data that group genes appropriately?

Resources

NLP sessions of PSB: psb.stanford.edu www.bionlp.com bioperl.org, biopython.org National Library of Medicine:

www.nlm.nih.gov http://www.ai.ucsd.edu/rik/annblast/ab-bm.

html (out of date, but still comprehensive)

Links to Today’s Topics

http://www.smi.stanford.edu/projects/helix/psb01/chang.pdf Pac Symp Biocomput. 2001;:374-83. PMID: 11262956

Blast: http://www.ncbi.nlm.nih.gov/BLAST/ http://www-smi.stanford.edu/projects/helix/psb03

Genome Res 2002 Oct;12(10):1582-90 Using text analysis to identify functionally coherent gene groups.Raychaudhuri S, Schutze H, Altman RB

www.biostat.wisc.edu/~craven/kddcup/ http://www.ncbi.nlm.nih.gov/Genbank/genbankstat

s.html http://www.ncbi.nlm.nih.gov:80/entrez/query.fcgi?d

b=Genome (complete genomes)

Links to Today’s Topics

http://www.nlm.nih.gov/mesh/meshhome.html

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