Some Data Mining Challenges Learned
From Bioinformatics & Actions Taken
Limsoon Wong
National University of Singapore
Bertinoro, Nov 2005
Plan
• Bioinformatics Examples
– Treatment prognosis of DLBC lymphoma
– Prediction of translation initiation site
– Prediction of protein function from PPI data
• What have we learned from these projects?
• What have I been looking at recently?
– Statistical measures beyond frequent items
– Small changes that have large impact
– Evolution of pattern spaces
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Example #1:
Treatment
Prognosis for
DLBC Lymphoma
Ref: H. Liu et al, “Selection of
patient samples and genes for
outcome prediction”, Proc.
CSB2004, pages 382--392
Bertinoro, Nov 2005
Image credit: Rosenwald et al, 2002
Diffuse Large B-Cell Lymphoma
• DLBC lymphoma is the
most common type of
lymphoma in adults
• Can be cured by
anthracycline-based
chemotherapy in 35 to 40
percent of patients
 DLBC lymphoma
comprises several
diseases that differ in
responsiveness to
chemotherapy
Bertinoro, Nov 2005
• Intl Prognostic Index (IPI)
– age, “Eastern Cooperative
Oncology Group” Performance
status, tumor stage, lactate
dehydrogenase level, sites of
extranodal disease, ...
• Not very good for
stratifying DLBC
lymphoma patients for
therapeutic trials
 Use gene-expression
profiles to predict outcome
of chemotherapy?
Copyright 2005 © Limsoon Wong
Knowledge Discovery from Gene
Expression of “Extreme” Samples
240
samples
“extreme”
sample
selection:
< 1 yr vs > 8 yrs
knowledge
discovery
from gene
expression
47 shortterm survivors
26 longterm survivors
7399
genes
80
samples
84
genes
T is long-term if S(T) < 0.3
T is short-term if S(T) > 0.7
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Kaplan-Meier Plot for 80 Test Cases
Low risk
High risk
p-value of log-rank test: < 0.0001
Risk score thresholds: 0.7, 0.3
Bertinoro, Nov 2005
No clear difference on
the overall survival of the
80 samples in the
validation group of DLBCL
study, if no training
sample selection
conducted
Copyright 2005 © Limsoon Wong
Example #2: Protein Translation
Initiation Site Recognition
Ref: L. Wong et al., “Using
feature generation and feature
selection for accurate
prediction of translation
initiation sites”, GIW 13:192-200, 2002
Bertinoro, Nov 2005
A Sample cDNA
• What makes the second ATG the TIS?
299 HSU27655.1 CAT U27655 Homo sapiens
CGTGTGTGCAGCAGCCTGCAGCTGCCCCAAGCCATGGCTGAACACTGACTCCCAGCTGTG
CCCAGGGCTTCAAAGACTTCTCAGCTTCGAGCATGGCTTTTGGCTGTCAGGGCAGCTGTA
GGAGGCAGATGAGAAGAGGGAGATGGCCTTGGAGGAAGGGAAGGGGCCTGGTGCCGAGGA
CCTCTCCTGGCCAGGAGCTTCCTCCAGGACAAGACCTTCCACCCAACAAGGACTCCCCT
80
160
240
• Approach
– Training data gathering
– Signal generation
• k-grams, distance, domain know-how, ...
– Signal selection
• Entropy, 2, CFS, t-test, domain know-how...
– Signal integration
• SVM, ANN, PCL, CART, C4.5, kNN, ...
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Too Many Signals Feature Selection
• For each value of k, there
are 4k * 3 * 2 k-grams
• If we use k = 1, 2, 3, 4, 5,
we have 24 + 96 + 384 +
1536 + 6144 = 8184
features!
• This is too many for most
machine learning
algorithms
Bertinoro, Nov 2005
• Choose a signal w/ low
intra-class distance
• Choose a signal w/ high
inter-class distance
• E.g.,
Copyright 2005 © Limsoon Wong
Sample k-grams Selected by CFS
Kozak consensus
Leaky scanning
• Position –3
• in-frame upstream ATG
• in-frame downstream
– TAA, TAG, TGA,
– CTG, GAC, GAG, and GCC
Stop codon
Codon bias?
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Validation Results (on Chr X and Chr 21)
Our
method
ATGpr
• Using top 100 features selected by entropy and
trained on Pedersen & Nielsen’s
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Level-1 neighbour
Example #3: Protein
Function Prediction
from Protein Interactions
Bertinoro, Nov 2005
Level-2 neighbour
An illustrative Case of
Indirect Functional Association?
SH3 Proteins
SH3-Binding
Proteins
• Is indirect functional association plausible?
• Is it found often in real interaction data?
• Can it be used to improve protein function
prediction from protein interaction data?
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Freq of Indirect Functional Association
• 59.2% proteins in dataset
share some function with
level-1 neighbours
YAL012W
|1.1.6.5
|1.1.9
YJR091C
YMR300C
YPL149W
YBR055C
YMR101C
|1.3.16.1
|16.3.3
|1.3.1
|14.4
|20.9.13
|42.25
|14.7.11
|11.4.3.1
|42.1
YPL088W
YBR293W
|2.16
|1.1.9
|16.19.3
|42.25
|1.1.3
|1.1.9
YDR158W
|1.1.6.5
|1.1.9
YBL072C
|12.1.1
YBR023C
YLR330W
YBL061C
|10.3.3
|32.1.3
|34.11.3.7
|42.1
|43.1.3.5
|43.1.3.9
|1.5.1.3.2
|1.5.4
|34.11.3.7
|41.1.1
|43.1.3.5
|43.1.3.9
|1.5.4
|10.3.3
|18.2.1.1
|32.1.3
|42.1
|43.1.3.5
|1.5.1.3.2
YLR140W
• 27.9% share some function
with level-2 neighbours
but share no function with
level-1 neighbours
YMR047C
|11.4.2
|14.4
|16.7
|20.1.10
|20.1.21
|20.9.1
YKL006W
YOR312C
|12.1.1
|16.3.3
|12.1.1
Bertinoro, Nov 2005
YPL193W
YDL081C
YDR091C
YPL013C
|12.1.1
|12.1.1
|1.4.1
|12.1.1
|12.4.1
|16.19.3
|12.1.1
|42.16
Copyright 2005 © Limsoon Wong
Over-Rep of Functions in
L1 & L2 Neighbours
Fraction of Neighbours w ith Functional
Sim ilarity
Sensitivity vs Precision
1
0.6
0.5
Fraction
0.4
0.3
0.2
L1 - L2
0.9
L2 - L1
0.8
L1 ∩ L2
0.7
Sensitivity
L1 - L2
L2 - L1
L1 ∩ L2
L3 - (L1 U L2)
All Proteins
0.6
0.5
0.4
0.3
0.2
0.1
0.1
0
≥0.1 ≥0.2 ≥0.3 ≥0.4 ≥0.5 ≥0.6 ≥0.7 ≥0.8 ≥0.9
Similarity
Bertinoro, Nov 2005
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Precision
Copyright 2005 © Limsoon Wong
Performance Evaluation
• Prediction performance improves after
incorporation of L1, L2, & interaction reliability info
Informative FCs
1
NC
Chi²
PRODISTIN
Weighted Avg
Weighted Avg R
0.9
0.8
Sensitivity
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
0
Bertinoro, Nov 2005
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9
Precision
1
Copyright 2005 © Limsoon Wong
What Have We
Learned?
Bertinoro, Nov 2005
Some of those
“techniques”
frequently needed
in analysis of
biomedical data are
insufficiently
studied by current
data mining
researchers
Bertinoro, Nov 2005
• Recognizing what
samples are relevant
and what are not
• Recognizing what
features are relevant
and what are not &
handling missing or
incorrect values
• Recognizing trends,
changes, and their
causes
Copyright 2005 © Limsoon Wong
Action #1:
Going Beyond
Frequent Patterns to
Recognize What
Features Are
Relevant and What
Are Not
Bertinoro, Nov 2005
Going Beyond Frequent Patterns
• Statisticians use a battery
of “interestingness”
measures to decide if a
feature/factor is relevant
PD ,ed
• Odds ratio
PD ,  d
OR ( P, D ) 
PD ,e 
PD ,  
• Examples:
– Odds ratio
– Relative risk
– Gini index
– Yule’s Q & Y
– etc
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Challenge: Frequent Pattern Mining
Relies on Convexity for Efficiency, But …
• Proposition:
Let SkOR(ms,D) = { P 
F(ms,D) | OR(P,D)  k}.
Then SkOR(ms,D) is not
convex
• i.e., the space of odds ratio
patterns is not convex.
Ditto for many other types
of patterns
{A,B}:1
{A,B,C}:3
{A}:∞
OR search space
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Solution: Luckily They Become Convex
When Decomposed Into Plateaus
• Theorem:
Let Sn,kOR(ms,D) = { P 
F(ms,D) | PD,ed=n, OR(P,D)
 k}. Then Sn,kOR(ms,D) is
convex
 The space of odds ratio
patterns becomes convex
when stratified into
plateaus based on support
levels on positive (or
negative) dataset
Bertinoro, Nov 2005
• Proposition:
Let Q ∊[P]D, then
OR(Q,D)=OR(P,D)
 The plateau space can be
further divided into convex
equivalence classes on the
whole dataset
 The space of equivalence
classes can be concisely
represented by generators
and closed patterns
Copyright 2005 © Limsoon Wong
Performance
• Mining odds ratio and
relative patterns depends
on GC-growth
• GC-Growth is mining both
generators and closed
patterns
• It is comparable in speed
to the fastest algorithms
that mined only closed
patterns
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Action #2: Tipping
Factors---The Small
Changes With Large
Impact
Bertinoro, Nov 2005
Tipping Events
• Given a data set, such as
those related to human
health, it is interesting to
determine impt cohorts
and impt factors causing
transition betw cohorts
 Tipping events
 Tipping factors are “action
items” for causing
transitions
Bertinoro, Nov 2005
• “Tipping event” is two or
more population cohorts
that are significantly
different from each other
• “Tipping factors” (TF) are
small patterns whose
presence or absence
causes significant
difference in population
cohorts
• “Tipping base” (TB) is the
pattern shared by the
cohorts in a tipping event
• “Tipping point” (TP) is the
combination of TB and a
TF
Copyright 2005 © Limsoon Wong
Impact-To-Cost-Ratio of Tipping Points
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Some Simple Results Useful
For Constructing TPs
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Action #3: Evolution of
Pattern Spaces---How
Do They Change When
the Sample Space
Changes?
Bertinoro, Nov 2005
Impact of Adding New Transactions on
Key and Closed Patterns
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Impact of
Removing
Items From All
Transactions
Bertinoro, Nov 2005
Copyright 2005 © Limsoon Wong
Acknowledgements
• DLBC Lymphoma:
– Jinyan Li, Huiqing Liu
• Translation Initiation:
– Fanfan Zeng, Roland
Yap
– Huiqing Liu
• Protein Function
Prediction:
– Kenny Chua, Ken Sung
Bertinoro, Nov 2005
• Odds Ratio & Relative Risk
– Mengling Feng, Yap-Peng
Tan,
– Haiquan Li, Jinyan Li
• Tipping Points:
– Guimei Liu, Jinyan Li
– Guozhu Dong
• Pattern Space Evolution:
– Mengling Feng, Yap-Peng
Tan
– Guozhu Dong
– Jinyan Li
Copyright 2005 © Limsoon Wong