Automating Discovery from Biomedical Texts

Automating Discovery from Biomedical Texts

Marti Hearst & Barbara RosarioUC Berkeley

Agyinc VisitAugust 16, 2000

The LINDI ProjectLinking Information for New

Discoveries

UIs for building and reusing hypothesis seeking strategies.

Statistical language analysis techniques for extracting propositions

Two Main Thrusts:

Scenario: Explore Functions of a Gene

Objective– Determine the functions of a newly

sequenced Gene X. Known facts

– Gene X co-expresses (activated in the same cell) with Gene A, B, C

– The relationship of Gene A, B, C with certain types of diseases (from medical literature)

Question– What types of diseases are Gene X related

to?

Gene Co-expression:Role in the genetic pathway

g?

PSA

Kall.

PAP

h?

PSA

Kall.

PAP

g?

Other possibilities as well

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 abstractsadenocarcinoma, neoplasm, prostate, prostatic

neoplasms, tumor markers, antibodies ...

Developing Strategies

Different strategies seem needed for different situations– First: see what is known about

Kallikrein.– 7341 documents. Too many– AND the result with “disease” category

» If result is non-empty, this might be an interesting gene

– Now get 803 documents

Medical Literature

Explore Functions of New Gene X

Gene-A

Key

wo

rds

Slide adapted from K. Patel

Projection

Mapping

Query


Different strategies seem needed for different situations– First: see what is known about Kallikrein.– 7341 documents. Too many– AND the result with “disease” category

» If result is non-empty, this might be an interesting gene

– Now get 803 documents– AND the result with PSA

» Get 11 documents. Better!

Medical Literature


Gene-A

Key

wo

rds

Key

wo

rds

Gene-B Gene-C

Key

wo

rds

Projection

Keywords

Intersection

Query


Look for commalities among these documents– Manual scan through ~100 category

labels– Would have been better if

»Automatically organized» Intersections of “important” categories

scanned for first

Medical Literature


Gene-A

Key

wo

rds

Key

wo

rds

Gene-B

Keywords

Keywords


Slicing

Gene-C

Key

wo

rds

Projection

Keywords

Intersection

Mapping

Query

Try a new tack

Researcher uses knowledge of field to realize these are related to prostate cancer and diagnostic tests

New tack: intersect search on all three known genes– Hope they all talk about diagnostics

and prostate cancer– Fortunately, 7 documents returned– Bingo! A relation to regulation of this

cancer

Medical Literature


Possible FunctionFor Gene-X

Gene-A

Key

wo

rds

Key

wo

rds

Gene-B

Keywords

Keywords


Slicing

Gene-C

Key

wo

rds

Projection

Keywords

Intersection

Mapping

Query

Query

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

Strategies again

In hindsight, combining all three genes was a good strategy.– Store this for later

Might not have worked– Need a suite of strategies– Build them up via experience and a

good UI

The System Doing the same query with slightly different

values each time is time-consuming and tedious

Same goes for cutting and pasting results– IR systems don’t support varying queries

like this very well.– Each situation is a bit different

Some automatic processing is needed in the background to eliminate/suggest hypotheses

The User Interface A general search interface should

support– History– Context– Comparison– Operators: Intersection, Union, Slicing– Operator Reuse– Visualization (where appropriate)

We have an initial implementation It needs lots of work

Architecture of LINDI UI

Data Layer Annotation Layer User Interface Layer

Data Layer Purpose

– Hide different formats of text collections Components

– Data: Abstractions representing records of a text collection

– Operations: performed on the data Data

– A set of records– Each record is a set of tuples with types

Operations– union, intersection, projection, mapping

Annotation Layer

Purpose– Associate data set with operations

that produced them (history)– History is a first class object

Advantage– Streamline a sequence of operations– Reuse operations– Parameterize operations

User Interface

Direct manipulation of information objects and access operations– Query– Intersection– Union– Mapping– Slicing

Record and reuse of past operations Parameterization of operations Streamlining of operations

Initial Palette

Query Structure Determined by Collection Type

Query Operation Results

Projection Operation and Subsequent Results

Parameterized Query: Repeat operations with different values

GC

GB

GA

Intersection over Projected Attribute

Intersection over Projected Attribute

Example Interaction with UI Prototype

1 Query on Gene names2 Project out only mesh headings3 Intersect the results4 Map to create a ranking5 Slice out the top-ranked.

Future Work on UI As currently designed

– Better labeling– Better layout

» Intuitive» Scalable

– Connection to real backend– User Testing

» Does direct manipulation work?» What operator sequences help?» How to improve parameterization?

More advanced– Support for strategies– Incorporation of NLP

Language Analysis Component

Goals:– Extract Propositions from Text– Make Inferences

Language Analysis Component

Why Extract Propositions from Text?– Text is how knowledge at the

propositional level is communicated– Text is continually being created and

updated by the outside world

Example:Statistical Semantic

GrammarTo detect causal relationships between medical concepts– Title:

Magnesium deficiency implicated in increased stress levels.

– Interpretation: <nutrient><reduction> related-to

<increase><symptom>

– Inference:» Increase(stress, decrease(mg))

Statistical Semantic Grammars

Empirical NLP has made great strides– But mainly applied to syntactic structure

Semantic grammars are powerful, but– Brittle – Time-consuming to construct

Idea:– Use what we now know about statistical NLP

to build up a probabilistic grammar

LINDI: Target Components

1. Special UI for retrieving appropriate docs

2. Language analysis on docs to detect causal relationships between concepts

3. Probabilistic representation of concepts and relationships

4. UI + User: Hypothesis creation

Documents

Automating Discovery from Biomedical Texts