Literature Mining BMI 730

Literature Mining BMI 730

Kun HuangDepartment of Biomedical Informatics

Ohio State University

Announcement

• HW #3 is cancelled. The grades will be adjusted accordingly.

Jensen et al. Nature Reviews Genetics 7, 119–129 (February 2006) | doi:10.1038/nrg1768

Acknowledgement

Acknowledgement

• Dr. Hongyu Peng (Brandies Univ.)• Dr. Hagit Shatkay (

http://www.shatkay.org)

provided part of the slides.

http://www.shatkay.org/

Connecting the dots

• Story of Thalidomide (from sedative to birth defects to anti-cancer drug)

Information Retrieval (IR)• Finding the papers• IR systems aim to identify the text

segments (be it full articles, abstracts, paragraphs or sentences) that pertain to a certain topic (e.g., yeast cell cycle).

• E.g., PubMed, Google Scholar• Ad hoc IR• Text categorization (pre-defined set of

papers)• Advanced – integrate Entity Recognition

Ad Hoc IR• User provide query • Boolean model• Index based (e.g. “Gene and CD”)

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DB: Database of documents.

Vocabulary: {t1,…,tM } (Terms in DB, produced by the tokenization stage)

Index Structure: A term all the documents containing it.

Boolean Queries

acquired immunodeficiencyasthmabloodblood pressure

IndexDatabase

Ad Hoc IR• User provide query • Boolean model• Challenges

CD

Chagas' disease

cytosine deaminase

Crohn‘s disease

capillary density

Cortical dysplasia

(54,745 Pubmed entries)

compact disk...

Synonymy (AGP1, aka, Amino Acid Permease1)

Polysemy

• Similarity query, e.g., Vector based. Semantic search

TIME (Sept 5, 2005): Search engines are good at matching words … The next step is semantic search – looking for meaning, not just matching key words. … Nervana, which analyzes language by linking word patterns contextually to answer questions in defined subject areas, such as medical-research literature.

Ad Hoc IR• User provide query • Vector-based model

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DB: Database of documents.

Vocabulary: {v1,…,vM } {Terms in DB}

Document dDB: Vector, <w1d,…,wM

d>, of weights.

The Vector Model

Weighting Principles

• Document frequency: Terms occurring in a few documents are more useful than terms occurring in many.

• Local term frequency: Terms occurring frequently within a document are likely to be significant for the document.

• Document length: A term occurring the same # of times in a long document and in a short one has less significance in the long one.

• Relevance: Terms occurring in documents judged as relevant to a query, are likely to be significant (WRT the query).

[Sparck Jones et al. 98]

Some Weighting Schemes:

Binary

TF Wid = fi

d = # of times ti occurs in d.

Wid=

fid

fi

(fi= # of docs containing ti)

TF X IDF(one version...)

Wid =

1 if ti d

0 otherwise

Consider Local term frequency

Consider Local term frequency and (Inverse) Document frequency

Document d= <w1d,…,wM

d>DB

Query q = < w1q,…,wM

q> (q could itself be a document in DB...)

Vector-Based similarity

Sim(q, d) = cosine (q, d ) =

q • d

|q| |d|

d

q

[Salton89, Witten et al99] Introductory IR.

[Sparck Jones et al. 98, Sahami98, Ponte&Croft 98, Hoffman 99]

Probabilistic Models

Query q ; Document d

Log[Log[PP(relevant | (relevant | dd, , qq))

PP(Irrelevant | (Irrelevant | dd, , qq)) ]]Maximize log-odds:Maximize log-odds:

• Goal:Goal: Find all Find all dd’s such that ’s such that PP(relevant | (relevant | dd, , qq) is high) is high

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Latent Semantics Analysis [Dumais, Deerwester et al,1988,1990]

Motivation: Overcoming synonymy and polysemy.Reducing dimensionality.

Idea: Project from “explicit term” space to a lower dimension, “abstract concept” space.

Methodology: PCA applied to the document-term matrix. Highest singular values are used as the features for representing documents.

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Information Retrieval- Details(cont.)

Text Categorization (semantic)

Automatically place documents in right categories so as to make them easy-to-find.

......

Cancer

Apoptosis Elongation

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Information Retrieval-Details(cont.)

Rule-Based Text ClassificationA knowledge-engineering approach. Boolean rules (DNF), based on the presence/absence of specific terms within the document, decide its membership in the class. (e.g. the CONSTRUE system [Hayes et al. 90,92] )

Example: If ( (<GENE_Name> ⋀ transcript) ⋁ ((<GENE_Name> Western Blot) ⋀ ⋁ ((<GENE_Name> Northern Blot))⋀ Then GeneExpressionDoc Else Gene⌝ ExpressionDoc

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Information Retrieval-Details(cont.)

Machine Learning for Text Classification (supervised)

• Take a training set of pre-classified documents• Build a model for the classes from the training examples• Assign each new document to the class that best fits it

(e.g. closest or most-probable class.)

Types of class assignment:

Hard: Each document belongs to exactly one class

Soft: Each document is assigned a “degree of membership” in several classes

Methods

Nearest neighbor

Summarizing document vectors

SVM, Bayesian, boosting

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Evaluating Extraction and Retrieval

To say how good a system is we need:1. Performance metrics (numerical measures)2. Benchmarks, on which performance is

measured (the gold-standard).

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Evaluating Extraction and Retrieval(cont.)

Performance Metrics

N items (e.g. documents, terms or sentences) in the collection

REL: Relevant items (documents, terms or sentences) in the collection.These SHOULD be extracted or retrieved.

RETR: Retrieved items (e.g. documents, terms or sentences) are actually extracted/retrieved

Some correctly (A = |REL ⋀ RETR|),Some incorrectly (B = |RETR – REL| )|RETR| = A+B

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Precision: P = A/(A+B)

How many of the retrieved/extracted items are correct

Recall: R = A/(A+D)

How many of the items that should be retrieved are recovered

Accuracy: (A+C)/N (Ratio of Correctly classified items)

F-score: 2PR / (P+R)

Harmonic mean, in the range [0,1]

Combination Scores:

Fβ-score: (1+β2)PR / (β2·P + R)β >1 Prefer recall, β <1 Prefer precision

E-measure: 1 – F(β)-scoreInversely proportional to performance (Error measure).

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Precision-Recall Curves

4 relevant documents in the collection.

7 retrieved and ranked.

1

7

6

5

4

3

225% Recall

50%

75%

100%

6675

66

100

0102030405060708090

100

0 25 50 75 100

Recall

Pre

cisi

on

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Average ScoresAverage Precision: Average the precision over all the ranks in which a relevant document is retrieved.

Mean Average Precision: Mean of the Average Precision over all the queries.Micro-Average: Average over individual items across queriesMacro-Average: Average over queries

For a given rank n, Pn: Precision at rank n (P@n)

R-Precision: PR where R is the number of relevant documents

Accounting for Ranks

Entity Recognition (ER)• Identifying the substance(s)• Rule and contextual based approach

(manual) – e.g., ‘-ase’ for enzyme• Rule and contextual based approach

(machine learning)• Dictionary-based approach

• How the names are written - CDC28, cdc28, cdc28p, cdc-28

• Curation of the dictionary

Entity Recognition (ER)• Major Challenge

Lack of standardization of names• ‘cdc2’ refers to two completely unrelated

genes in budding and fission yeast• ‘SDS’ - serine dehydratase gene vs. Sodium

Dodecyl Sulfate vs. Shwachman-Diamond syndrome

Synonymy (AGP1, aka, Amino Acid Permease1) Polysemy

http://en.wikipedia.org/wiki/Shwachman-Diamond_syndrome

Entity Recognition (ER)• Simpler version – if this symbol is for

gene or its product• iHOP (Information hyperlinked over

proteins) http://www.pdg.cnb.uam.es/UniPub/iHOP

Vocabulary• Many, many• SNOWMED, ICD, …• ICD (

International Statistical Classification of Diseases and Related Health Problems)

http://en.wikipedia.org/wiki/ICD

http://en.wikipedia.org/wiki/ICD

Vocabulary• ICD

573.3 Hepatitis, unspecifiedToxic (noninfectious) hepatitisUse additional E code to identify cause

571.4 Chronic hepatitisExcludes:

viral hepatitis (acute) (chronic) (070.0-070.9)

571.49 OtherChronic hepatitis:

activeaggressive

Recurrent hepatitis

070 Viral hepatitisIncludes:

viral hepatitis (acute) (chronic)Excludes:

cytomegalic inclusion virus hepatitis (078.5)

Unified Medical Language system (UMLS)

Information Extraction (IE)• Extract pre-defined types of fact — in

particular, relationships between biological entities.

• Co-occurrence based method• Natural language processing (NLP) based

method

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Information Extraction

• Identify the relevant sentences• Parse to extract specific information • Assume “well-behaved” fact sentences• Using co-occurrence relationships alone

does not require parsing or good fact-structure

Usually it requires

Text Mining (TM)• The discovery by computer of new,

previously unknown information, by automatically extracting information from different written records.

Text Mining

Fish Oil

Blood Viscosity

Platelet aggregability

Vascular Reactivity Reduces(and co-occurs)

Raynaud’s Syndrome

Increased(and co-occurs)

Fish OilRaynaud’s Syndrome

•Based on transitivity of relationships in co-occurrence graph.•This idea can be used to discover new facts by co-occurrence•Web Tool : Arrowsmith

[Swanson 86,Swanson87,Swanson90, Swanson and Smalheiser99, Weeber et al. 2001, Stapley & Benoit 2000, Srinivasan 2003, Srivinasan 2004]

Can Reduce

Integration: combining text and biological data


Literature Mining BMI 730

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