ai, text mining and scientific research - jst text mining and scientific research junichi tsujii...

AI, Text Mining

and Scientific Research

Junichi Tsujii

Director

Artificial Intelligence Research Center, AIST

Plan of the talk

• Background

• Challenges in Biology

• Linking Text with Knowledge

• Conclusion

2

Plan of the talk

• Background



• Conclusion

3

AI which models HIAI approaching to HI

• ＩＢＭWatson：NLU,Text and Structured Knowledge, Fact Retrieval, QA

• Computer Chess (Japanese Shogi): Large Search Space, Machine Learning

• Robot for entrance exam of U‐Tokyo：NLU, Problem Solving, Inferences based on Knowledge

• Conversational Agent：Intelligence with bodies, NLU in specific situations, Grounding of language

• Deep Learning：Brain inspired computation, Changes of Computation principles, Autonomous Intelligence, New Paradigms of Machine Learning

• Brain Science： Science of HI

AI evolving from Big Data and Data ScienceAI which surpasses HI

5

Another Stream of AI

Machine Learning Large‐scale GraphGraph Mining

GPU ・HPC Optimization Deep Learning

Integration of two AIs

AI with high Affinity for HI

①Data Knowledge Integration AI； AI which can explain

• AI which thinks based on Data

• HI which thinks based on Knowledge

②Brain‐Inspired AI； Revolution of Computation Principles

6

Modeling HI Surpassing HI

AlphaGo（２０１６）Machine Learning and Simulation

A game of perfect information

DNN

Database of Games in the past

Complete Simulation

v(s)

p(a|s)Training Data

Computational Science and AISimulation and Machine Learning

A game of perfect information

DNN

Complete Simulation

v(s)

p(a|s)Training Data

Unknowns

Diverse databasesOf

Omics

Incomplete and PartialSimulation

Database of Games in the past

Cooperation of HI and AI

• Collective Intelligence• Linked Data, Common Ontology, Shared Knowledge, Technology as Commodities

• Machine Learning• Autonomous AI, Self‐driving cars, Speech/Vision Processing, etc.

• Communication among NI and AI

• Collectively Solving Challenges • Social, Technological and Scientific Challenges

Plan of the talk

• Background



• Conclusion

10

… ATTCGGATATTTAAGGC …

… ATTCGGGTATTTAAGCC … Healthy

Disease(e.g., Alzheimer, Cancer)

Genome‐Wide Association Studies (GWAS)

2000

2010

“Genetic diagnosis of diseases would beaccomplished in 10 years and that treatmentswould start to roll out perhaps five years afterthat.”

“A Decade Later, Genetic Maps Yield Few New Cures” New York Times, June 2010.

11

Francis Collins (NIH)

by Hoifung Poon (MSR, 2013)

Traditional Biology

12

Targeted Experiments Discovery

One hypothesis


Genomics

13

High‐Throughput ExperimentsDiscovery


… ATTCGGGTATTTAAGCC …





Too many hypotheses

……

Big Data


Genomics

14

High‐Throughput Experiments

Discovery







Many hypotheses

Big Data

……

Oda K, Matsuoka Y, Funahashi A, Kitano H: A comprehensive pathway map of epidermal growth factor

receptor signaling. Mol Syst Biol 2005, 1:2005 0010.

Nodes : 652

Links: 444

600 papers were read to

construct the pathway

Artificial Intelligence

NaturalIntelligence

DataKnowledge

Cooperation

Big Challenges

1,2-Diacyglycerol intracellular

AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)

MEKK4(MAP3K4)MLK3(MAP3K11)

N-Ras

NCK2 (Grb4)

NO intracellularNeurofibromin

PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SLC36A1

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk

Big Mechanism: Robot ScientistsDARPA Chicago Univ. Manchester Univ. AIRC

Reading AssemblyExplanation


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SLC36A1

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SLC36A1

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk

Very large conflicting(probabilistic) network

Smaller(relevant)groundedmodel

Computationalhypotheses/wet labExperimentscontrolling states of thenetwork

A.Rzhetsky(U.Chicago)

Big Mechanism

• Project supported by DARPA• Some of the systems that matter most to the Defense Department are

very complicated. Ecosystems, brains and economic and social systemshave many parts and processes, but they are studied piecewise, and their literatures and data are fragmented, distributed and inconsistent. It is difficult to build complete, explanatory models of complicated systems, and so effects in these systems that are brought about by many interacting factors are poorly understood.

• Big mechanisms are large, explanatory models of complicated systems in which interactions have important causal effects. The collection of big data is increasingly automated, but the creation of big mechanisms remains a human endeavor made increasingly difficult by the fragmentation and distribution of knowledge. To the extent that the construction of big mechanisms can be automated, it could change how science is done.

Plan of the talk

• Background



• Conclusion

18


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk

Big Mechanism: Reading‐Assembly‐Explanation

Reading Assembly Explanation


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SLC36A1

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SLC36A1

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk




By A. Rzhetsky(U. Chicago)

The Need for Text Mining

Types of documents

• Full papers

• Abstracts

• Reports, discharge summaries

• EMR

• Textbooks, monographs

• Grey content, online discussion forums

MEDLINE

• 2005: ~14M

• 2009: ~18M

• 2013: ~22M

• 2015: ~26M

20

Overwhelming information in textual, unstructured format

By S. Ananiadou(U. Manchester)

Event Extraction

Finding events ( trigger mentions , , andevent types typed arguments

including locations ) involving genes or gene products

… In this study we hypothesized that the phosphorylation of TRAF2 inhibitsbinding to the CD40 cytoplasmic domain. …

phosphorylation

TRAF2

binding

inhibits

TRAF2 CD40

Theme2ThemeTheme

Cause Theme

Negative_regulation

Phospholylation Binding

cytoplasmic domain

Site2

http://www.nactem.ac.uk/EventMine/

Finding Evidence ‐EuropePubMed Central

• Currently: runs on 2,550, 328 full texts

• 82,198,474 facts in 38,411,661 sentences

• Full parsing used a version of Enju (Mogura)

• Parsing pipeline run on 60 machines at EBI ~30 days

22

http://labs.europepmc.org/evf


Deep Reading: Reading with a Model

• Goal: evaluate how TM systems process text in relation to what is known about a pathway

• Performers asked to produce

– Relationship/proposed change to the model (new/corroborating/conflicting information)

– A model fragment describing the change

– The source text supporting the change

By L.Hirschman(MITRE)

Reading against a Model (1)

“monoubiquitination of Rasenhances association with the downstream effectors Raf and PI3‐Kinase”

CORROBORATING: We know that Ras binds Raf



“monoubiquitination of Rasenhances association with the downstream effectors Raf and PI3‐Kinase”

NEW MECHANISM: Ras binds PI3‐Kinase.

BEL: complex(p(PFH:”Ras family”), p(“PI3K”))



“Moreover, the RAS‐ASPP interaction enhances the transcription function of p53”

NEW RELATIONSHIP: RAS‐ASPP complexincreases transcriptional activity of p53

BEL: complex(p(PFH:”Ras Family”),p(HGNC:ASPP2) ‐> act(p(HGNC:P53), ma(tscript))

By L. Hirschman(MITRE)

Epistemic knowledge

• Enriches event‐based search systems – Discovery of new knowledge

– Negation, uncertainty, speculative claims in literature

27

Miwa, Thompson, McNaught, Kell, Ananiadou (2012). Extracting semantically enrichedevents from biomedical literature. BMC Bioinformatics 13, 108


Uncertainty

Negation

Analysis

Source

Extracting epistemic knowledge

28


Event Extraction

Finding events ( trigger mentions , , andevent types typed arguments

including locations ) involving genes or gene products


phosphorylation

TRAF2

binding

inhibits

TRAF2 CD40

Theme2ThemeTheme

Cause Theme

Negative_regulation

Phospholylation Binding

cytoplasmic domain

Site2

http://www.nactem.ac.uk/EventMine/

Deep reading

custom components

existing components supplied with custom resources

existing components

By R. Batista(U. Manchester)

custom components

existing components supplied with custom resources

existing components

Reads passages from remote

folder

Reads passages from remote

folder

Performs tokenisation, POS, chunk tagging; recognisesproteins and cell lines

Performs tokenisation, POS, chunk tagging; recognisesproteins and cell lines

Makes distinction between genes/proteins and protein

families

Makes distinction between genes/proteins and protein

families

Uses model trained on overlapping corporaUses model trained on overlapping corpora

Reads in BioPAXmodel from a

SPARQL endpoint

Reads in BioPAXmodel from a

SPARQL endpoint

By R. Batista(U. Manchester)

32

Words

Terms

Entities

Relations

Events

Wordform co‐occurrence, pattern matching, …

Term recognition and normalisation

Named entity recognition

Relation extraction

Event extraction

Associations

epistemicextraction

Data mining, Clustering

What is known aboutthis disease, protein, person?

What is linked with X?

{Who, what} Xed {whom, what} where, when and how?

What if…?

Keywordsearch

Is X possible, certain, probable, suggested, past, to come?

What is thispaper about?

Increased sophistication? Increased customisation!


Plan of the talk

• Background



• Conclusion

33


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk

Big Mechanism: Reading‐Assembly‐Explanation

Reading Assembly Explanation


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SLC36A1

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk


AKT(PKB)

ALK

Androgen receptor

B-Raf

BETA-PIX

C/EBPbeta

C3G

CDC42

CDK2

CREB1

Ca('2+) cytosol

Cyclic AMP intrac

Cyclic GMP intrace

EGR1

ERK1/2

ESR1 (nuclear)

Elk-1

FMO3

FRS2

GAB1

GRB2

Galectin-1

H-Ras

HDBP1

HGF receptor (Met)

HIF1A

HSP27

IRS-1

IRS-2

JNK(MAPK8-10)

K-RAS

Lyn

MAP2

MEK1/2

MEK4(MAP2K4)

MEK6(MAP2K6)

MEKK1(MAP3K1)


N-Ras

NCK2 (Grb4)


PAK1

PDGF receptor

PDLIM3

PDZ-GEF1

PI3K cat class IA

PIP5KI

PKC

PR (nuclear)

Protein kinase G1

Pyk2(FAK2)

R-Ras

RASGRF2

RIPK4

Rac1

SHP-2

SLC36A1

SOS

SP1

Shc

Slc39a14 (Zip14)

Tiam1

VEGFR-1

a-6/beta-4 integrin

c-Fos

c-Jun

c-Kit

c-Myc

c-Raf-1

cPLA2

p90Rsk




By A.Rzhetsky(U. Chicago)

Artificial Intelligence

NaturalIntelligence

DataKnowledge

Cooperation

Big Challenges

ai, text mining and scientific research - jst text mining and scientific research junichi tsujii...

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