Mining the Medical Literature

Chirag BhattOctober 14th, 2004

Why MINE data! Medical, genomics,

proteomics research Find causal links

between symptoms or diseases and drugs or chemicals

Gene comparison

An exampleProblem What is causing an

uncharacteristic behavior in protein production?

Solution Find which genes have a roll to

play in amino acid synthesis?How? Search through online literature

for genes that play a role in amino acid synthesis

Search vs. Discover

Search(goal oriented)

Discover(opportunistic)

Structured data (database)

Data retrieval

Data mining

Unstructured Data (text)

Information Retrieval

Text mining

Data Retrieval Company Database

e.g. Customer records, product inventory

Search entity (structured) records

Query (goal-driven) What is the address of our client? How many widgets are in stock?

SQL, Oracle, DB2, etc

Information Retrieval Google, A9, AltaVista Query (goal-driven) Search entity (unstructured)

documents variable format

html, pdf, etc

Data Mining Structured data set Generally a large amount of

(historical) data Find relations or patterns or trends

in database (opportunistic) Eg “beer and diaper”

Text Mining Unstructured data set

Documents, publications, abstracts, web pages

Discover useful and previously unknown “gems” of information in large text collections using patterns, trends and domain knowledge

Need for mining text Approximately 90% of the world’s data is

held in unstructured formats (source: Oracle

Corporation)

Why Text Mining in Medical Literature?

Many multi-functional genes Screen functionally interesting ones

Complexity of needs increasing Individual genes -> family of genes

Manual Text Mining ? Not really! Availability of published literature

online

Functionally Coherent Genes Group of genes that

exhibit similar experimental features Amino acid metabolism,

electron transport, stress response

Difficulties Difficulties faced in

finding functionally coherent genes Most genes express

multi-functionality Some genes studied

extensively and some only just discovered

Semantic neighbor

Two articles are semantic neighbors if they have similar word usage

Use statistical natural language processing to access and interpret online text

Methodology

Methodology Find semantic neighbors

in document set If any article about

common functionality contains atleast one in the group then the group is functionally coherant

Neighbor divergence

Scoring method Each articles

relevance to gene group is scored by: count of number

neighbors that have references to the group

Neighbor divergence scores

If score distribution is different from Poisson then gene group represents biological function

The log ratio for a Poisson distribution should be flat along

the horizontal axis

Need to filter results Generally well-studied

genes tend to have semantic neighbor that refer to same gene

Neighbor may not be relevant to group function, but increases score – false positive

So only articles that refer to different genes are considered

Evaluation Report percentile of a functional

group of genes Calculate precision and recall at

different cutoff levels (next slide) Remove legitimate genes with

irrelevant genes in group

Precision and Recall

Results Sample Space: 19 known yeast groups

and 1900 random groups

Results

Replacing functional genes

Limitations of neighbor divergence

Neighbor divergence helps group genes not tell us function

Work based on abstracts only Entire literature search may prove

challenging Break into smaller components

Another mining approach

Extracting synonymous gene and protein terms

Why find synonyms? Genes and proteins are often

associated with multiple names across articles and sub domains

More names keep getting added new functional or structural

information is discovered Improve search and analysis

Current work Biological databases such

as GenBank and SWISSPROT include synonymsNot up to dateDisagreement on some synonymsLaborious manual curation and reviewNeed for automation

Two-step problem Identifying gene and protein

names Done by state-of-the-art taggers

Determining whether these names are synonymous We’ll discuss more on this…

Current synonym approaches

Synonymous gene and protein names represent same biological substance Exhibit identical biological functions Same gene or amino acid sequences

Other approaches String matching Matching abbreviations to full-forms

Gene and Protein Tagging Identification step

Uses BLAST techniques and domain knowledge to pick out genes and protein terms

Heuristics Synonyms usually occur within

same sentence Synonyms mentioned in first

few pages of article

Synonym detection approaches Unsupervised - ‘Similarity’

based on contextual similarity Semi-supervised - ‘Snowball’

extracts structured relations using patterns

Supervised - Text Classification/SVM Hand-crafted extraction – GPE Combined system

Combined Approach Combine output of SnowBall, SVM,

and GPE Each system gives a confidence score

for each synonym pair

Where, s = <p1,p2> is a synonym pair and ConfE(s) is confidence assigned to s by individual extraction by the system E

Unsupervised - Similarity Context based

All words occurring within a ‘x’ word window

False positives are very common Run time – O(|lexicon|3)

Semi-supervised - Snowball

Manual feedback mechanism

Supervised – Text Classification

Input: known synonym pairs Automatically find contexts and

assign weights Train classifier to distinguish

between ‘positive’ and ‘negative’ contexts Eg ‘A also known as B’ and ‘A

regulates B’

Why Combined Approach? SnowBall and SVM, machine-learning

based captures synonyms that may be

missed by GPE GPE, knowledge-based

SnowBall and SVM have many false positives

Combine both advantages

Results

Summary Text mining Semantic neighbor Neighbor divergence Precision and Recall Synonym detection Approaches

Comments / Questions?