Text mining,
pathways and disease
Andrey Rzhetsky
Scientists are like plants
biomass -> texts/studies
sunlight,water -> peaceful
time and resources for
work
Production of new texts (in
pages): new additions annually
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We estimated that the world
libraries host at least a
TRILLION
unique scholarly pages
(Evans and Rzhetsky, 2009; analysis of the Online Computer
Library Center’s WorldCat database of books and journals in
71,000 libraries across 121 countries)
A bit of context
PubMed search for cancer
Results: 2,358,785 papers (March 17,
2010)
GeneWays as an infogrinder
On-line Journals
GeneWays
Pathways
Graph: multi-type arcs
and nodes
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Context-free grammar:
Production rules
S  A activates B
B  binding of D by C
A  p53 C  bax
D  bad
Context-free grammar:
Non-terminals
S, A, B, C, D
Context-free grammar
Terminals:
p53
bax
bad
Context-free grammar
binding
A activates
B
p53
S
of bad
D by bax
C
Context-free grammar:
Parsing
binding
A activates
B
p53
S
of bad
D by bax
C
[ bind, [protein, bad], [protein,bax] ]
Context-free grammar:
Parsing
A activates
B
p53
S
[activate, [protein, p53], [action, B]]
Context-free grammar:
Parsing
“Our experiments show that p53 activates
binding of bax by bad.”
[activate, [protein, p53],
[bind, [protein, bad],
[protein, bax]
]
To be more concrete…
The phosphorylation of ATP-citrate lyase by NDPK
suggests that NDPK may have a role in the regulation
of membrane biosynthesis
The phosphorylation of ATP-citrate lyase by
NDPK suggests that NDPK may have a role in
the regulation of membrane biosynthesis
ndpk
phosphorylates
atp-citrate lyase
Typical arcs
1001,'bind'
1004,'suppress'
1011,'replace'
1018,'interact'
1020,'activate'
1022,'stimulate'
1023,'phosphorylate'
1027,'increase'
1028,'associate'
1034,'up-regulate'
1036,'inhibit'
1040,'promote'
1041,'down-regulate'
1043,'trigger'
1049,'block'
1054,'modify'
1057,'digest'
1058,'degrade'
1062,'link'
1071,'cleave'
1072,'release'
1074,'catalyze'
1083,'inactivate'
1106,'repress'
1110,'acetylate'
1117,'methylate'
Typical nodes
17767,'calcium channel antagonists'
20324,'hsp70 chaperone'
17467,'activator protein 1'
5104,'daunorubicin'
13194,'tyrosyl-phosphorylated' 9689,'paroxonase'
4190,'immunodeficiency'
4478,'iga2'
8552,'human fcgammarii'
4472,'iga1'
13151,'ikaros'
9820,'caveolin 1'
7277,'virus-triggered p-dcs'
4366,'complexes pr-3'
12290,'anti-alpha4 mabs'
2258,'gal4-mef2d'
14464,'polyneuropathy'
database ID
16044,'alk5'
10393,'mek-1 inhibitor'
13262,'pro-matrilysin'
Graph: multi-type arcs
and nodes
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Useful hairballs…
dandelions…
vermicelli…
dust bunnies…
rat’s nests…
Well, if reality is complicated,
so is the corresponding figure
Intel 8086: breadboard
Making case for utility
Looking at cerebellar malformations
through text-mined interactomes of
mice and humans
Ivan Iossifov, Raul Rodriguez-Esteban, Ilya
Mayzus, Kathleen J. Millen, and Andrey
Rzhetsky
PLoS Computational Biology, 2009
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Application to discovery of genes
related to heritable disorders
Molecular triangulation
We (again!) generated a
number of high-confidence
predictions about associations
of phenotypes with genes.
Testing experimentally: to be
continued…
Looking at cerebellar malformations through
text-mined interactomes of mice and humans
Ivan Iossifov, Raul Rodriguez-Esteban, Ilya Mayzus,
Kathleen J. Millen, and Andrey
Rzhetsky
PLoS Computational Biology, 2009, to appear
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An alternative title:
about useful hairballs…
dandelions…
ridiculograms…
dust bunnies…
rat’s nests…
Intel 8086: breadboard
Using the position
in networks to
describe function
(from Mark Gerstein)
[NY Times, 2-Oct-05, 9-Dec-08]
Star witness: D-SPOP
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D-SPOP: phenotype
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Wild eye
Egr-triggered
Cell death
One copy of D-SPOP
deleted
D-SPOP as marker
In humans, SPOP was highly
expressed in 99% of clear
cell renal cell carcinomas,
the most prevalent form of
kidney cancer.
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A case for (marginal)
usefulness of our efforts
Next: A scary story…
The invisible plague
Steady rise
of prevalence
of neurodevelopmental
disorders during
the last 250
years
Autism
Bipolar disorder
Schizophrenia
Genetic-linkage Mapping
of Complex Hereditary
Disorders to a Wholegenome Molecularinteraction Network
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AR
Tian Zheng
Miron Baron
T. Conrad Gilliam
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One-chromosome example
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Sick
Healthy
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Sick
Healthy
Hypothetical pedigree
Model
Now, we have 23
chromosomes in two copies,
~25,000 genes
Combinations of genes:
108 -- 2 genes
12
10
-- 3 genes
1016 -- 4 genes
1037 -- 10 genes
It doesn’t make sense
(statistically) to test all
possible combinations of
genes for association with
disease
Possible way out: test only
functionally plausible
combinations of genes
Graph: multi-type arcs
and nodes
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Graph: only physical
interactions (such as bind,
phosphorylate, etc)
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Then we realized
that the clusters
should be
allowed to have
arbitrary
topology
Genetic heterogeneity model
Pedigrees, phenotypes,
Marker states
parameters
cluster probability for gene 1
cluster probability for gene c
disease
We did this exercise for all
three disorders (autism,
bipolar disorder,
schizophrenia).
We then combined the most
significant predictions.
“Our” predictions indeed form overlapping networks!