The BioText Project

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The BioText Project

• SIMS Affiliates Meeting
• Nov 14, 2003
• Marti Hearst
• Associate Professor
• SIMS, UC Berkeley
• Projected sponsored by NSF DBI-0317510,
• ARDA AQUAINT, and a gift from Genentech

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BioText Project Goals

• Provide fast, flexible, intelligent access to
  information for use in biosciences applications.
• Better search results
• Text mining
• Focus on
• Textual Information
• Tightly integrated with other resources
• Ontologies
• Record-based databases

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People

• Project Leaders
• PI Marti Hearst Co-PI Adam Arkin
• Computational Linguistics
• Barbara Rosario
• Presley Nakov
• Database Research
• Ariel Schwartz
• Gaurav Bhalotia (graduated)
• User Interface / Information Retrieval
• Kevin Li
• Dr. Emilia Stoica
• Bioscience
• Dr. TingTing Zhang

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Outline

• Main Goals
• Text Mining Examples
• System Architecture
• Apoptosis problem statement
• Recent results in
• Abbreviation definition recognition
• Semantic relation recognition (from text)
• Search User Interfaces
• Hierarchical grouping of journals

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Text Mining Example 1

• How to discover new information
• As opposed to discovering which statistical
  patterns characterize occurrence of known
  information.
• Method
• Use large text collections to gather evidence to
  support (or refute) hypotheses
• Make Connections
• Gather Evidence

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Etiology Example

• Don Swanson example, 1991
• Goal find cause of disease
• Magnesium-migraine connection
• Given
• medical titles and abstracts
• a problem (incurable rare disease)
• some medical expertise
• find causal links among titles
• symptoms
• drugs
• results

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Gathering Evidence
stress
CCB
migraine
magnesium
magnesium
PA
SCD
magnesium
magnesium
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Gathering Evidence
migraine
magnesium
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Swansons Linking Approach

• Two of his hypotheses have received some
  experimental verification.
• His technique
• Only partially automated
• Required medical expertise

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Text Mining Example 2

• How to find functions of genes?
• Have the genetic sequence
• Dont know what it does
• But
• Know which genes it coexpresses with
• Some of these have known function
• So infer function based on function of
  co-expressed genes
• This is problem suggested by Michael Walker and
  others at Incyte Pharmaceuticals

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Gene Co-expressionRole in the genetic pathway
Kall.
Kall.
g?
h?
PSA
PSA
PAP
PAP
g?
Other possibilities as well
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Make use of the literature

• Look up what is known about the other genes.
• Different articles in different collections
• Look for commonalities
• Similar topics indicated by Subject Descriptors
• Similar words in titles and abstracts
• adenocarcinoma, neoplasm, prostate, prostatic
  neoplasms, tumor markers, antibodies ...

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Formulate a Hypothesis

• Hypothesis mystery gene has to do with
  regulation of expression of genes leading to
  prostate cancer
• New tack do some lab tests
• See if mystery gene is similar in molecular
  structure to the others
• If so, it might do some of the same things they
  do

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Outline

• Main Goals
• Text Mining Examples
• System Architecture
• Apoptosis problem statement
• Recent results in
• Abbreviation definition recognition
• Semantic relation recognition (from text)
• Search User Interfaces
• Hierarchical grouping of journals

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BioText Architecture
Sophisticated Text Analysis
Annotations in Database
Improved Search Interface
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Recent Result (Schwartz Hearst 03)

• Fast, simple algorithm for recognizing
  abbreviation definitions.
• Simpler and faster than the rest
• Higher precision and recall
• Idea Work backwards from the end
• Examples
• In eukaryotes, the key to transcriptional
  regulation of the Heat Shock Response is the Heat
  Shock Transcription Factor (HSF).
• Gcn5-related N-acetyltransferase (GNAT)
• Idea use redundancy across abstracts to figure
  out abbreviation meaning even when definition is
  not present.

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BioText A Two-Sided Approach
Empirical Computational Linguistics Algorithms
Sophisticated Database Design Algorithms
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Apoptosis Network
Survival Factors Signaling
Genotoxic Stress
Lost of Attachment Cell Cycle stress, etc
ER Stress
Initiator Caspases (8, 10)
P53 pathway
BH3 only
Bcl-2 like
NFkB
Bax, Bak
Mitochondria Cytochrome c
Smac
Caspase 12
IAPs
Apaf 1
AIF
Caspase 9
Apoptosis
Slide courtesy TingTing Zhang
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The issues (courtesy TingTing Zhang)

• The network nodes are deduced from reading and
  processing of experimental knowledge by experts.
  Every month gt1000 apoptosis papers are published.
  
• The supporting experimental data are gathered in
  different organs, tissues, cells using various
  techniques.
• There are various levels of uncertainty
  associated with different techniques used to
  answer certain questions.
• Depending on the expression patterns for the
  players in the network, the observation may or
  may not be extended to other contexts.
• We need to keep track of ALL the information in
  order to understand the system better.

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• Simple cases
• Mouse Bim proteins (isoforms EL, L, S) binds to
  human Bcl-2 (bacteriophoage screening using cDNA
  expression library from T-Lymphoma cell line
  KO52DA20).
• Human BimEL protein is 89 identical to mouse
  BimEL, Human BimL is 85 identical to mouse BimL
  (Hybridization of mouse bim cDNA to human fetal
  spleen and peripheral blood cDNA library).
• Bim mRNA is detected in B and T lyphoid cells
  (Northern blot analysis of mouse KO52DA20, WEHI
  703, WEHI 707, WEHI7.1, CH1, WEHI231 WEHI415,
  B6.23.16BW2 cell extracts).
• BimL protein interact with Bcl-2 OR Bcl-XL, or
  Bcl-w proteins (Immuno-precipitation (anti-Bcl-2
  OR Bcl-XL OR Bcl-w)) followed by Western blot
  (anti-EEtag) using extracts human 293T cells
  co-transfected with EE-tagged BimL AND (bcl-2 OR
  bcl-XL OR bcl-w) plasmids)
• BimL deleted of the BH3 domain does not bind to
  Bcl-2 OR Bcl-XL, or Bcl-w proteins (under
  experimental conditions mentioned above)

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Computational Language Goals

• Recognizing and annotating entities within
  textual documents
• Identifying semantic relations among entities
• To (eventually) be used in tandem with
  semi-automated reasoning systems.

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Main Ideas for NLP Approach

• Assign Semantics using
• Statistics
• Hierarchical Lexical Ontologies to generalize
• Redundancy in the data
• Build up Layers of Representation
• Syntactic and Semantic
• Use these in a feedback loop

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Computational Linguistics Goals

• Mark up text with semantic relations

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Recent ResultDescent of Hierarchy

• Idea
• Use the top levels of a lexical hierarchy to
  identify semantic relations
• Hypothesis
• A particular semantic relation holds between all
  2-word Noun Compounds that can be categorized by
  a MeSH pair.

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Definition

• NC Any sequence of nouns that itself functions
  as a noun
• asthma hospitalizations
• health care personnel hand wash
• Technical text is rich with NCs
• Open-labeled long-term study of the
  subcutaneous sumatriptan efficacy and
  tolerability in acute migraine treatment.

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NCs Three tasks

• Identification
• Syntactic analysis (attachments)
• Baseline headache frequency
• Tension headache patient
• Our Goal Semantic analysis
• Headache treatment ? treatment for
  headache
• Corticosteroid treatment ? treatment that
  uses
  corticosteroid

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Main Idea

• Top-level MESH categories can be used to indicate
  which relations hold between noun compounds
• headache recurrence
• C23.888.592.612.441 C23.550.291.937
• headache pain
• C23.888.592.612.441 G11.561.796.444
• breast cancer cells
• A01.236 C04 A11

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Linguistic Motivation

• Can cast NC into head-modifier relation, and
  assume head noun has an argument and qualia
  structure.
• (used-in) kitchen knife
• (made-of) steel knife
• (instrument-for) carving knife
• (used-on) putty knife
• (used-by) butchers knife

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Distribution of Frequent Category Pairs
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How Far to Descend?

• Anatomy 250 CPs
• 187 (75) remain first level
• 56 (22) descend one level
• 7 (3) descend two levels
• Natural Science (H01) 21 CPs
• 1 (4) remain first level
• 8 (39) descend one level
• 12 (57) descend two levels
• Neoplasm (C04) 3 CPs
• 3 (100) descend one level

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Evaluation

• Apply the rules to a test set
• Accuracy
• Anatomy 91 accurate
• Natural Science 79
• Diseases 100
• Total
• 89.6 via intra-category averaging
• 90.8 via extra-category averaging

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Summary of NC Work

• Lexical hierarchy useful for inferring semantic
  relations
• Works because semantics are constrained and word
  sense ambiguity is not too much of a problem
• Can it be extended to other types of relations?
• Preliminary results on one set of relations are
  promising.

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Database Research Issues

• Efficiently and effectively combining
• Relational databases Text
• Hierarchical Ontologies
• Layers of Annotations

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Interface Issues

• Create intuitive, appealing interfaces that are
  better than whats currently out there.
• Start with existing assigned metadata
• As text analysis improves, incorporate the
  results into the interface.

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Some Recent Work

• Organizing BioScience Journal Names
• Currently there are gt 3500

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Some Recent Work

• Organizing BioScience Journal Names
• Currently there are gt 3500
• Idea
• Group them into faceted hierarchies
  semi-automatically
• Using clustering of title terms, synonym
  similarity via WordNet, and other techniques

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Summary

• BioText aims to improve access to bioscience
  information via
• Sophisticated language analysis
• Integration of results into
• Annotated database
• Flexible user interface
• Eventual goal
• Semi-automated mining and discovery

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Theres lots to do!
For more information

• biotext.berkeley.edu