Text Mining for Bioscience Applications: The State of the Art

1
(No Transcript)
2
Text Mining for Bioscience ApplicationsThe
State of the Art

• Marti Hearst
• University of California, Berkeley

3
Outline

• Search vs. Discovery
• Why is text analysis difficult?
• Some current approaches
• Future directions

4
My Background

• Computer Scientist by training
• NOT a biologist
• Professor in an interdisciplinary program
• School of Information Management Systems (SIMS)
• Affiliated with the UCSF Bioinformatics Grad
  Group
• Research fields are
• Computational Linguistics
• Search (Information Retrieval)
• User Interfaces and Information Visualization
• Have focused for a while on bioscience text
• Have received research support from Genentech

5
Search vs. Discovery
Search Finding hay in a haystack
Discovery Creating a new kind of hay
6
Search Goals

• More accurate results
• More comprehensive results
• Thesaurus expansion
• Intelligent summaries of results
• Organize results along biologically relevant
  lines
• Better user interfaces

7
Knowledge Discovery from Text

• How to discover new information
• As opposed to looking up whats already known.
• Method
• Create hypotheses
• Use large text collections to gather evidence to
  refute or support hypotheses
• Do lab tests to verify promising results

8
Discovery Goals

• Genomics
• Automatically build gene networks
• Discover gene functions
• Pharmacology
• Help determine which drugs can help cure a
  disease
• Help determine which genetic traits will lead to
  a reaction to a drug
• Etiology
• Discover underlying causes of disease

9
Why is Automated Text Analysis Difficult?
10
Why is automated text analysis difficult?

• Avastin, developed by South San Francisco-based
  Genentech (DNA), was approved for advanced
  colorectal cancer and for patients who haven't
  received other chemotherapy, according to the
  Food and Drug Administration.
• What is approved doing in this sentence?
• John was approved for advancement -gt gets a
  promotion.
• Avastin was approved for cancer -gt to fight
  cancer.
• Avastin was approved for patients -gt to consume
  to fight cancer.
• What kind of patients approved for?
• Ambiguous. Could be for anyone who hasnt
  received chemotherapy, or only those patients
  with advanced colorectal cancer who havent
  received chemotherapy.

11
Why is automated text analysis difficult?

• This could easily be a multibillion-dollar
  drug," McCamant says.
• Refers to concepts mentioned in earlier
  sentences.

12
Why is automated text analysis difficult?

• "Avastin opens up this new gateway for cancer
  care," says William Li, president of the
  Angiogenesis Foundation in Massachusetts. "It's
  the first in a fleet of other drugs.
• Is Avastin a vehicle? It opens gateways and
  travels in a fleet!

13
Why is automated text analysis difficult?

• There are many indirect ways to say things
• A two-dose combined hepatitis A and B vaccine
  would facilitate immunization programs.
• The vaccine helps prevent hep B.
• These results suggest that con A-induced
  hepatitis was ameliorated by pretreatment with
  TJ-135.
• The treatment TJ-135 helps cure hep.
• Effect of interferon on hepatitis B.
• There is an unspecified effect of interferon on
  hep B.

14
What do we do?

• Solve sub-problems
• Extract certain types of entities
• Gene/protein names
• Abbreviation definitions
• Classify the noun phrases using ontologies
• MeSH, LocusLink, GO, etc.
• Define relationship types try to recognize them.
• Many other subproblems are actively being worked
  on
• Word sense disambiguation
• Co-reference resolution

15
Two Main Approaches
Hand-built Rules
Machine Learning
16
Two Main Approaches

• Hand-built rules
• Can be very accurate
• Are also very brittle
• Dont scale
• Machine learning
• Usually requires labeled training data
• Unsupervised methods under development
• Can be made to scale
• Is the way of the future

17
Abbreviation Definition Recognition

• A Simple Algorithm for Identifying Abbreviation
  Definitions in Biomedical Text, Ariel Schwartz
  and Marti Hearst, PSB 2003 Kauai, Jan 2003
• Fast, simple algorithm for recognizing
  abbreviation definitions.
• Simpler and faster than the rest
• Other approaches are cubic or quadratic in time
• 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)
• In future
• Use redundancy across abstracts to figure out
  abbreviation meaning even when definition is not
  present.

18
Gene name co-occurence

• A literature network of human genes for
  high-throughput analysis of gene expression.
  Jenssen TK, Laegreid A, Komorowski J, Hovig E.
  Nat Genet. 2001 May28(1)21-8.
• PubGene Assumption
• If two genes are co-mentioned in a MEDLINE
  record, there is an underlying biological
  relationship.

Example Genes highly upregulated at time point 6
h (6H) in the fibroblast serum response.
Green upregulation Red downregulation
19
Gene name co-occurence

• A literature network of human genes for
  high-throughput analysis of gene expression.
  Jenssen TK, Laegreid A, Komorowski J, Hovig E.
  Nat Genet. 2001 May28(1)21-8.
• Evaluation
• 29-40 of the pairs were incorrect
• 45 of OMIM pairs found
• 51 of DIP pairs found (DB of Interacting
  Proteins)

20
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

21
Gene Co-expressionRole in the genetic pathway
Kall.
Kall.
g?
h?
PSA
PSA
PAP
PAP
g?
Other possibilities as well
22
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 ...

23
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

24
Etiology Example

• Complementary structures in disjoint science
  literatures. Don R. Swanson. In Proceedings of
  SIGIR 91
• 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

25
Gathering Evidence
migraine
26
Gathering Evidence
migraine
magnesium
27
Swansons Linking Approach

• Two of his hypotheses have received some
  experimental verification.
• His technique
• Only partially automated
• Required medical expertise
• Recently others have made progress automating it.

28
Automating Swanson-style Discovery

• Text Mining Generating Hypotheses from MEDLINE,
  Padmini Srinivasan. To appear in JASIST.
• UMLS defines Semantic Types
• Every MeSH term is assigned one or more Semantic
  Types
• Interferon type II falls within both
• Immunologic Factor and
• Pharmacologic Substance
• Each PubMed article is assigned a set of MeSH
  terms
• The idea is to characterize a set of articles
  according to which semantic types their MeSH
  terms fall into.

29
Automating Swanson-style Discovery

• Text Mining Generating Hypotheses from MEDLINE,
  Padmini Srinivasan. To appear in JASIST.
• Approach
• User inputs topic T of interest
• User selects 2 sets from a small number of sets
  of UMLS semantic types
• System
• Searches PubMed for articles about T
• Selects out the important MeSH terms as
  determined by the user-chosen semantic type
  categories
• Searches PubMed for articles that contain these
  MeSH terms
• Combines the MeSH terms that result from these
  retrieved documents
• Call this result C
• If a PubMed search on words from T and c from C
  are empty, place c as a candidate in a final
  result set R
• Report those terms in R that fall into the second
  user-selected semantic type set.

30
Automating Swanson-style Discovery

• Text Mining Generating Hypotheses from MEDLINE,
  Padmini Srinivasan. To appear in JASIST.
• Results have successfully reproduced the 7
  examples they tried, with very little manual
  intervention
• Example input topic is Raynauds disease

31
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

32
Automated Relation Assignment

• Recall the problem
• A two-dose combined hepatitis A and B vaccine
  would facilitate immunization programs.
• The vaccine helps prevent hep B.
• Identified 7 relations that can hold between
  Treatments and Diseases
• Used Machine Learning to address this
• Graphical models
• Neural nets
• Marked up the text with syntactic and semantic
  information
• MeSH labels turn out to be very important

33
Automated Relation Assignment

• Use Machine Learning to address this
• Graphical models
• Neural nets
• Mark up the text with syntactic and semantic
  information
• MeSH labels turn out to be very important

34
Automated Relation Assignment

• Results

35
Future Directions

• In text analysis
• Move away from hand-built rules
• More focus on labeling with semantics
• In problems tackled
• There are so many possibilities!
• Help with automated curation

36
Thank you!

• Visit our site
• biotext.berkeley.edu