The document discusses a novel approach for splice junction prediction using a boosted categorical restricted Boltzmann machine to enhance performance in class-imbalanced datasets, particularly relevant for deep neural networks. It presents new training methods, including boosted contrastive divergence and a regularization term for sparsity in DNA sequences, showing significant improvements in prediction accuracy. Additionally, the study compares its methodology to existing machine learning and sequence alignment-based approaches, demonstrating robustness in handling high-dimensional data.

1. Advanced Computing Laboratory
Electrical and Computer Engineering
Seoul National University
Taehoon Lee SungrohYoon
BoostedCategorical Restricted Boltzmann Machine
forComputational Prediction of Splice Junctions

2. • Motivation
• Preliminary
• Boosted contrastive divergence
• Categorical restricted Boltzmann machine
• Experiment results
• Conclusion
Outline
2/25

3. • Deep Neural Networks (DNN) show human level performance on many
recognition tasks.
• We focus on class-imbalanced prediction.
• Insufficient samples to represent the true distribution of a class.
Motivation
3/25

4. • Deep Neural Networks (DNN) show human level performance on many
recognition tasks.
• We focus on class-imbalanced prediction.
• Insufficient samples to represent the true distribution of a class.
Motivation
negative positive
3/25

5. • Deep Neural Networks (DNN) show human level performance on many
recognition tasks.
• We focus on class-imbalanced prediction.
• Insufficient samples to represent the true distribution of a class.
Motivation
negative positive
easy to
misclassify
query images
3/25

6. • Deep Neural Networks (DNN) show human level performance on many
recognition tasks.
• We focus on class-imbalanced prediction.
• Insufficient samples to represent the true distribution of a class.
• Q. How can we learn minor but important features using neural networks?
Motivation
negative positive
easy to
misclassify
query images
3/25

7. • Deep Neural Networks (DNN) show human level performance on many
recognition tasks.
• We focus on class-imbalanced prediction.
• Insufficient samples to represent the true distribution of a class.
• Q. How can we learn minor but important features using neural networks?
• We propose a new RBM training method called boosted CD.
Motivation
negative positive
easy to
misclassify
query images
3/25

8. • Deep Neural Networks (DNN) show human level performance on many
recognition tasks.
• We focus on class-imbalanced prediction.
• Insufficient samples to represent the true distribution of a class.
• Q. How can we learn minor but important features using neural networks?
• We propose a new RBM training method called boosted CD.
• We also devise a regularization term for sparsity of DNA sequences.
Motivation
negative positive
easy to
misclassify
query images
3/25

9. • Genetic information flows through the gene expression process.
• DNA: a sequence of four types of nucleotides (A,G,T,C).
• Gene: a segment of DNA (the basic unit of heredity).
(Splice) Junction Prediction: ExtremelyClass-Imbalanced Problem
4/25

10. • Genetic information flows through the gene expression process.
• DNA: a sequence of four types of nucleotides (A,G,T,C).
• Gene: a segment of DNA (the basic unit of heredity).
(Splice) Junction Prediction: ExtremelyClass-Imbalanced Problem
DNA
RNA
protein
4/25

11. • Genetic information flows through the gene expression process.
• DNA: a sequence of four types of nucleotides (A,G,T,C).
• Gene: a segment of DNA (the basic unit of heredity).
(Splice) Junction Prediction: ExtremelyClass-Imbalanced Problem
DNA
RNA
protein
gene expression
4/25

12. • Genetic information flows through the gene expression process.
• DNA: a sequence of four types of nucleotides (A,G,T,C).
• Gene: a segment of DNA (the basic unit of heredity).
(Splice) Junction Prediction: ExtremelyClass-Imbalanced Problem
DNA
RNA
protein
gene expression
exon
4/25

13. • Genetic information flows through the gene expression process.
• DNA: a sequence of four types of nucleotides (A,G,T,C).
• Gene: a segment of DNA (the basic unit of heredity).
(Splice) Junction Prediction: ExtremelyClass-Imbalanced Problem
DNA
RNA
protein
gene expression
exon
intron
4/25

14. • Genetic information flows through the gene expression process.
• DNA: a sequence of four types of nucleotides (A,G,T,C).
• Gene: a segment of DNA (the basic unit of heredity).
(Splice) Junction Prediction: ExtremelyClass-Imbalanced Problem
exon
GT: false boundary
GT: true boundary
ACGTCGACTGCTACGTAGCAGCGA
TACGTACCGATCATCACTATCATC
GAGGTACGATCGATCGATCGATCA
GTCGATCGTCGTTCAGTCAGTCGA
TATCAGTCATATGCACATCTCAGT
DNA
RNA
protein
gene expression
exon
intron
4/25

15. • Genetic information flows through the gene expression process.
• DNA: a sequence of four types of nucleotides (A,G,T,C).
• Gene: a segment of DNA (the basic unit of heredity).
(Splice) Junction Prediction: ExtremelyClass-Imbalanced Problem
exon
GT: false boundary
GT: true boundary
ACGTCGACTGCTACGTAGCAGCGA
TACGTACCGATCATCACTATCATC
GAGGTACGATCGATCGATCGATCA
GTCGATCGTCGTTCAGTCAGTCGA
TATCAGTCATATGCACATCTCAGT
DNA
RNA
protein
gene expression
GT (or AG)
16K
76M
true sites
exon
intron
160K
(=0.21% over 76M)
4/25

16. • Two approaches:
• Machine learning-based:
• ANN (Stormo et al., 1982; Noordewier et al., 1990; Brunak et al., 1991),
• SVM (Degroeve et al., 2005; Huang et al., 2006; Sonnenburg et al., 2007),
• HMM (Reese et al., 1997; Pertea et al., 2001; Baten et al., 2006).
• Sequence alignment-based:
• TopHat (Trapnell et al., 2010), MapSplice (Wang et al., 2010),
RUM (Grant et al., 2011).
PreviousWork on Junction Prediction
1
2
5/25

17. • Two approaches:
• Machine learning-based:
• ANN (Stormo et al., 1982; Noordewier et al., 1990; Brunak et al., 1991),
• SVM (Degroeve et al., 2005; Huang et al., 2006; Sonnenburg et al., 2007),
• HMM (Reese et al., 1997; Pertea et al., 2001; Baten et al., 2006).
• Sequence alignment-based:
• TopHat (Trapnell et al., 2010), MapSplice (Wang et al., 2010),
RUM (Grant et al., 2011).
PreviousWork on Junction Prediction
We want to construct a learning model which can boost prediction
performance in a complementary way to alignment-based method.
1
2
1
2
5/25

18. • Two approaches:
• Machine learning-based:
• ANN (Stormo et al., 1982; Noordewier et al., 1990; Brunak et al., 1991),
• SVM (Degroeve et al., 2005; Huang et al., 2006; Sonnenburg et al., 2007),
• HMM (Reese et al., 1997; Pertea et al., 2001; Baten et al., 2006).
• Sequence alignment-based:
• TopHat (Trapnell et al., 2010), MapSplice (Wang et al., 2010),
RUM (Grant et al., 2011).
PreviousWork on Junction Prediction
We want to construct a learning model which can boost prediction
performance in a complementary way to alignment-based method.
1
2
1
2
We propose a learning model based on (multilayer) RBMs
and its training scheme.
5/25

19. • Training methods of RBM
• RBM for categorical values
• Softmax input units (Salakhutdinov et al., ICML 2007).
• Class-imbalance problems
• Refer to a review byGalar et al. (IEEET SMC 2012).
Related Methodologies
Description
Training
cost
Noise
handling
Class-imbalance
handling
CD (Hinton,
Neural Comp. 2002)
Standard and
widely used
- - -
Persistent CD
(Tieleman, ICML 2008)
Use of a single
Markov chain
- -
Parallel tempering
(Cho et al., IJCNN 2010)
Simultaneous Markov
chains generation
6/25

20. Main Contributions
7/25

21. Main Contributions
New RBM training methods
called boosted CD
7/25

22. Main Contributions
New RBM training methods
called boosted CD
New penalty term to handle
sparsity of DNA sequences
7/25

23. Main Contributions
Significant boosts in splicing
prediction performance
New RBM training methods
called boosted CD
New penalty term to handle
sparsity of DNA sequences
7/25

24. Main Contributions
Significant boosts in splicing
prediction performance
Robustness to high-dimensional
class-imbalanced data
New RBM training methods
called boosted CD
New penalty term to handle
sparsity of DNA sequences
7/25

25. Main Contributions
Significant boosts in splicing
prediction performance
Robustness to high-dimensional
class-imbalanced data
New RBM training methods
called boosted CD
New penalty term to handle
sparsity of DNA sequences
25/25
The ability to detect subtle
non-canonical splicing signals

26. • Motivation
• Preliminary
• Boosted contrastive divergence
• Categorical restricted Boltzmann machine
• Experiment results
• Conclusion
Outline
8/25

27. • RBM is a type of logistic belief network whose structure is a bipartite graph.
• Nodes:
• Input layer:
• Hidden layer:
• Probability of a configuration :
•
•
• Each node is a stochastic binary unit:
•
• can be used as a feature.
Restricted Boltzmann Machines
9/25

28. • Training weights to minimize negative log-likelihood of data.
• Run the MCMC chain 𝒗(0), 𝒗(1),… , 𝒗(𝑘) for 𝑘 steps.
• The CD-𝑘 updates after seeing example 𝒗:
Contrastive Divergence (CD) forTraining RBMs
𝒗(0)
= 𝒗
𝒉(0) 𝒉(1) 𝒉(𝑘)
𝒗(1)
𝒗(𝑘)
10/25

29. • Training weights to minimize negative log-likelihood of data.
• Run the MCMC chain 𝒗(0), 𝒗(1),… , 𝒗(𝑘) for 𝑘 steps.
• The CD-𝑘 updates after seeing example 𝒗:
Contrastive Divergence (CD) forTraining RBMs
approximated by
k-step Markov chain
𝒗(0)
= 𝒗
𝒉(0) 𝒉(1) 𝒉(𝑘)
𝒗(1)
𝒗(𝑘)
10/25

30. • Motivation
• Preliminary
• Boosted contrastive divergence
• Categorical restricted Boltzmann machine
• Experiment results
• Conclusion
Outline
11/25

31. Overview of Proposed Methodology
12/25

32. Overview of Proposed Methodology
12/25

33. Overview of Proposed Methodology
12/25

34. Overview of Proposed Methodology
12/25

35. Overview of Proposed Methodology
12/25

36. Overview of Proposed Methodology
12/25

37. • Boosting is a meta-algorithm which converts weak learners to strong ones.
• Most boosting algorithms consist of iteratively learning weak classifiers with
respect to a distribution and adding them to a final strong classifier.
• The main variation between many boosting algorithms:
• The method of weighting training data points and hypotheses.
• AdaBoost, LPBoost,TotalBoost, …
What Boosting Is
from lecture notes @ UCIrvine CS 271 Fall 2007
13/25

38. • Contrastive divergence training is looped over all mini-batches and known to
be stable.
BoostedContrastive Divergence (1/2)
14/25

39. • Contrastive divergence training is looped over all mini-batches and known to
be stable.
BoostedContrastive Divergence (1/2)
14/25
hardly
observed
regions

40. • Contrastive divergence training is looped over all mini-batches and known to
be stable.
• However, for a class-imbalance distribution, we need to assign higher weights
to rare samples in order to jump to unseen examples byGibbs chains.
BoostedContrastive Divergence (1/2)
14/25
hardly
observed
regions

41. • Contrastive divergence training is looped over all mini-batches and known to
be stable.
• However, for a class-imbalance distribution, we need to assign higher weights
to rare samples in order to jump to unseen examples byGibbs chains.
BoostedContrastive Divergence (1/2)
14/25
hardly
observed
regions

42. • Contrastive divergence training is looped over all mini-batches and known to
be stable.
• However, for a class-imbalance distribution, we need to assign higher weights
to rare samples in order to jump to unseen examples byGibbs chains.
BoostedContrastive Divergence (1/2)
assign higher
weights to
rare samples
14/25
hardly
observed
regions

43. • Contrastive divergence training is looped over all mini-batches and known to
be stable.
• However, for a class-imbalance distribution, we need to assign higher weights
to rare samples in order to jump to unseen examples byGibbs chains.
BoostedContrastive Divergence (1/2)
assign lower
weights to
ordinary samples
assign higher
weights to
rare samples
14/25
hardly
observed
regions

44. • If we assign the same weight to all the data, the performance ofGibbs
sampling would degrade in the regions that are hardly observed.
• Whenever sampling, we therefore re-weight each observation by the energy
of its reconstruction 𝐸(𝒗 𝑛
(𝑘), 𝒉 𝑛
(𝑘)
).
15/25
BoostedContrastive Divergence (2/2)

45. • If we assign the same weight to all the data, the performance ofGibbs
sampling would degrade in the regions that are hardly observed.
• Whenever sampling, we therefore re-weight each observation by the energy
of its reconstruction 𝐸(𝒗 𝑛
(𝑘), 𝒉 𝑛
(𝑘)
).
15/25
BoostedContrastive Divergence (2/2)
Relative locations of samples
and corresponding Markov
chains by CD
hardly
observed
regions

46. • If we assign the same weight to all the data, the performance ofGibbs
sampling would degrade in the regions that are hardly observed.
• Whenever sampling, we therefore re-weight each observation by the energy
of its reconstruction 𝐸(𝒗 𝑛
(𝑘), 𝒉 𝑛
(𝑘)
).
15/25
BoostedContrastive Divergence (2/2)
Relative locations of samples
and corresponding Markov
chains by the proposed
Relative locations of samples
and corresponding Markov
chains by CD
hardly
observed
regions

47. • If we assign the same weight to all the data, the performance ofGibbs
sampling would degrade in the regions that are hardly observed.
• Whenever sampling, we therefore re-weight each observation by the energy
of its reconstruction 𝐸(𝒗 𝑛
(𝑘), 𝒉 𝑛
(𝑘)
).
15/25
BoostedContrastive Divergence (2/2)
Relative locations of samples
and corresponding Markov
chains by PT
Relative locations of samples
and corresponding Markov
chains by the proposed
Relative locations of samples
and corresponding Markov
chains by CD
hardly
observed
regions

48. • For biological sequences, 1-hot encoding is widely used (Baldi & Brunak, 2001).
• A,C,G, andT are encoded by 1000, 0100, 0010, and 0001, respectively.
• In encoded binary vectors, 75% of the elements are zero.
Categorical Gradient
16/25

49. • For biological sequences, 1-hot encoding is widely used (Baldi & Brunak, 2001).
• A,C,G, andT are encoded by 1000, 0100, 0010, and 0001, respectively.
• In encoded binary vectors, 75% of the elements are zero.
• To resolve sparsity of 1-hot encoding vectors, we devise a new regularization
technique that incorporates prior knowledge on the sparsity.
Categorical Gradient
16/25

50. • For biological sequences, 1-hot encoding is widely used (Baldi & Brunak, 2001).
• A,C,G, andT are encoded by 1000, 0100, 0010, and 0001, respectively.
• In encoded binary vectors, 75% of the elements are zero.
• To resolve sparsity of 1-hot encoding vectors, we devise a new regularization
technique that incorporates prior knowledge on the sparsity.
Categorical Gradient
sparsity term
16/25
reconstruction with and w/o
the sparsity term

51. • For biological sequences, 1-hot encoding is widely used (Baldi & Brunak, 2001).
• A,C,G, andT are encoded by 1000, 0100, 0010, and 0001, respectively.
• In encoded binary vectors, 75% of the elements are zero.
• To resolve sparsity of 1-hot encoding vectors, we devise a new regularization
technique that incorporates prior knowledge on the sparsity.
Categorical Gradient
sparsity term
16/25
reconstruction with and w/o
the sparsity term
derived from
the sparsity term

52. ProposedTraining Algorithm
categorical gradient
boosted CD
17/25

53. • Motivation
• Preliminary
• Boosted contrastive divergence
• Categorical restricted Boltzmann machine
• Experiment results
• Conclusion
Outline
18/25

54. • Data preparation:
• Real human DNA sequences with known boundary information.
Results
Effects of
categorical gradient
Effects of boosting
Effects on the
splicing prediction
19/25

55. • Data preparation:
• Real human DNA sequences with known boundary information.
Results
Effects of
categorical gradient
Effects of boosting
Effects on the
splicing prediction
CGTAGCAGCGATACGTACCGATCGTCACTATCATCGAGGTACGAGAGATCGATCGGCAACG
19/25

56. • Data preparation:
• Real human DNA sequences with known boundary information.
Results
Effects of
categorical gradient
Effects of boosting
Effects on the
splicing prediction
CGTAGCAGCGATACGTACCGATCGTCACTATCATCGAGGTACGAGAGATCGATCGGCAACG
true acceptor 1 true donor 1 true acceptor 2 non-canonical true donor
19/25

57. • Data preparation:
• Real human DNA sequences with known boundary information.
Results
Effects of
categorical gradient
Effects of boosting
Effects on the
splicing prediction
CGTAGCAGCGATACGTACCGATCGTCACTATCATCGAGGTACGAGAGATCGATCGGCAACG
true acceptor 1 true donor 1 true acceptor 2 non-canonical true donor
false acceptor 1false donor 1
19/25

58. • Data preparation:
• Real human DNA sequences with known boundary information.
• GWH dataset: 2-class (boundary or not).
• UCSC dataset: 3-class (acceptor, donor, or non-boundary).
Results
Effects of
categorical gradient
Effects of boosting
Effects on the
splicing prediction
CGTAGCAGCGATACGTACCGATCGTCACTATCATCGAGGTACGAGAGATCGATCGGCAACG
true acceptor 1 true donor 1 true acceptor 2 non-canonical true donor
false acceptor 1false donor 1
19/25

59. • The proposed method shows the best performance in terms of reconstruction
error for both training and testing.
• Compare to the softmax approach, the proposed regularized RBM succeeds in
achieving lower error by slightly sacrificing the probability sum constraint.
Results: Effects ofCategorical Gradient
Data: chromosome 19 in
GWH-donor
Sequence Length: 200nt
(800 dimension)
# of iterations: 500
Learning rate: 0.1
L2-decay: 0.001
over-fitted best
20/25

60. • For simulating a class-
imbalance situation
• we randomly
dropped samples
with different drop
rates for different
classes.
Results: Effects of Boosting

61. • For simulating a class-
imbalance situation
• we randomly
dropped samples
with different drop
rates for different
classes.
Results: Effects of Boosting
Description
Training
cost
Noise
handling
Class-imbalance
handling
CD (Hinton,
Neural Comp. 2002)
Standard and
widely used
- - -
Persistent CD
(Tieleman, ICML 2008)
Use of a single
Markov chain
- -
Parallel tempering
(Cho et al., IJCNN 2010)
Simultaneous Markov
chains generation
Proposed boosted CD Reweighting samples -

62. Results: Improved Performance and Robustness
2-class classification performance 3-class classification Runtime
22/25

63. Results: Improved Performance and Robustness
2-class classification performance 3-class classification Runtime
Insensitivity to sequence lengths
22/25

64. Results: Improved Performance and Robustness
2-class classification performance 3-class classification Runtime
Insensitivity to sequence lengths Robustness to negative samples
22/25

65. exon intron
• (Important biological finding) non-canonical splicing can arise if:
• Introns containGCA or NAA sequences at their boundaries.
• Exons include contiguousA’s around the boundaries.
Results: Identification of Non-Canonical Splice Sites
We used 162,951
examples excluding
canonical splice sites.
23/25

66. • We proposed a new RBM training method called boosted CD with categorical
gradients that improves conventionalCD for class-imbalanced data.
• Significant boosts in splicing prediction in terms of accuracy and runtime.
• Increased robustness to high-dimensional class-imbalanced data.
• The proposed scheme shows the ability to detect subtle non-canonical
splicing signals that often could not be identified by traditional methods.
• Future work: additional validation using various class-imbalance datasets.
24/25
Conclusion

67. • Our lab members
• Financial supports
• ICML 2015 travel scholarship
Acknowledgements
June 2, 2015
25/25

68. • Our lab members
• Financial supports
• ICML 2015 travel scholarship
Acknowledgements
June 2, 2015
25/25

69. • The proposed DBN showed xx% higher performance in terms of the F1-score.
• RNN is appropriate for sequence modeling. However, splicing signals are often
too far from the boundaries and hard to maintain splicing information.
Backup:Comparison with Recurrent Neural Networks (RNNs)
To be placed
Backup/25