Neural Networks in Bioinformatics

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Neural Networks in Bioinformatics
I-Fang Chung ifchung_at_ym.edu.tw Institute of
Bioinformatics, YM 4-27-2006
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Experience and Education

• 1989-2000 Electrical and Control Engineering in
  NCTU
• 2000-2003 (Postdoc) ECE Laboratory of
  Intelligent Control
• 2003-2004 (Postdoc) Laboratory of DNA Information
  Analysis of Human Genome Center, Institute of
  Medical Science, Tokyo University
• 2004-now Institute of Bioinformatics, Yang-Ming

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Outline

• Motivation
• To solve one problem in bioinformatics
• Identification of RNA-Interacting Residues in
  Protein
• Current projects

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Neural Networks

• Neural networks are constructed to resemble the
  behavior of human brains (neurons)
• Characterizes the ability to learn, recall, and
  generalize from training patterns

x1
Weights
wi1
x2
wi2
yi
a(.)
neti
Output path
xm
wim
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Neural Networks (contd)

• Good at tasks such as pattern matching,
  classification, function approximation, and data
  clustering
• Good at tasks in bioinformatics such as coding
  region recognition, protein structure prediction,
  gene clustering

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Basic Principles of Discrimination

• Each object associated with a class label (or
  response) Y ? 1, 2, , K and a feature vector
  (vector of predictor variables) of G
  measurements X (X1, , XG)
• Aim predict Y from X.

Predefined Class 1,2,K
K
1
2
Objects
Y Class Label 2 X Feature vector
colour, shape
Classification rule ?
X red, square Y ?
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Example
Learning set
?
Bad prognosis recurrence lt 5yrs
Good Prognosis recurrence gt 5yrs
Good Prognosis Matesis gt 5
Predefine classes Clinical outcome
Objects Array Feature vectors Gene expression
new array
Reference L vant Veer et al (2002) Gene
expression profiling predicts clinical outcome of
breast cancer. Nature, Jan.
Classification rule
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Design Issues
Human brain
Domain knowledge, e.g. biology (molecule,
chemistry)
Problem definition (desired input/output mapping)
Output encoding
Neural Network
Applications
Molecular Structure
Sequence discrimination Feature
detection Classification Structure prediction
DNAATGCGCTC ProteinMASSTFYI

Pre-Processing
Post-Processing


Training Data Sets Testing Data Sets
System Evaluation
Network Architecture Learning Algorithm Parameter
adjustment
Feature representation (knowledge
extraction) Input encoding
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Prediction of Protein 2nd Structures
Adopted from Qian and Sejnowski, 1988
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Sliding Window
Chain_1
2-D info
Chain_2
Chain_3

Amino Acids

• Sliding window concept
• Considering a piece of strings as inputs
• Only looking at central position in a piece of
  strings to detect what kind of 2-D info. happens

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Binary Bit Encoding Method
000001000000000000000

• Input encoding for each input pattern
• Unary encoding scheme for protein sequence
• 21 binary bits for 20 kinds of amino acid type (1
  bit for overlapped terminal)
• Input layer with multiple Input patterns
• A window size w of consecutive residues been
  considered.
• 21 w units for sequence only
• Output layer with 3 units
• To describe what kind of 2-D info. Happens (1,
  0, 0 for helix, 0, 1, 0 for sheet, 0, 0, 1
  for coil)
• One hidden layer for non-linear 2-class pattern
  classification

w
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More Complex NN Structure PHD
Multiple sequence Alignment, it is a way to
compare multiple sequence, the result is called
alignment profile.
breakthroughuse evolutionary information in MSA
instead of single sequence
Adopted from Rost and Sander, 1993
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Outline

• Motivation
• To solve one problem in bioinformatics
• Identification of RNA-Interacting Residues in
  Protein
• Current projects

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Identification of RNA-Interacting Residues in
Protein

• Task
• Predicting putative RNA-interacting sites within
  a protein chain
• Given a protein sequence? Finding the
  RNA-binding positions (residues)
• Method
• Using feedforward neural network based on
  sequence profiles
• Analyzing and qualifying a large set of the
  network weights trained on sequence profiles

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Data Generation

• Source Protein Data Bank (PDB)
• Collect Protein-RNA complexes, resolved by X-ray
  with 3.0Å
• Remove redundant protein structures with sequence
  identity over 70
• 86 non-homologous protein chains (21990 residues)
• Residues in interaction sites
• The closest distance between atoms of the protein
  and the partner RNA is less than 7Å.
• hydrogen bonds, stacking, electrostatic,
  hydrophobic, and van der Waals, interactions
  considered
• Residues in interaction sites 21.7 (4782)

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Classifier
Chain_1
interaction site or not
Chain_2
Chain_3
Amino acids

2D info.
Appearance probability
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PSSM

• Position Specific Iterative BLAST (PSI BLAST)
• A strong measure of residue conservation in a
  given location
• Position specific scoring matrix (PSSM)
• A 20-dimensional vector representing
  probabilities of conservation against mutations
  to 20 different amino acids including itself
• The position of the important function of protein
  will be kept in the course of evolving

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Experimental Results (contd)

• Agreement with structural studies of protein-RNA
  interactions
• Arg, Lys, Ser, Thr, Asp and Glu prefer to be in
  hydrogen bonding
• Phe and Ser are frequently located in van der
  Waals interacting and stacking interacting
• Some conflicting situations
• Ala, Leu and Val known to less preferred types in
  interactions
• Asn typically though of one of the most preferred
  amino acid types in hydrogen bonding

Adopted from Jeong and Miyano, 2006
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Saliency Factor

• Objective Define a matrix to represent the
  importance of the presence of specific residues
  at specific positions
• Step1 Normalization of weight xij for each
  input unit aij

M the window size, 1 i M N the of
distinct residue symbols, 1 j N H
the of hidden units, 1 k H
Adopted from Jeong and Miyano, 2006
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Saliency Factor (contd)

• Weight conservation the amount of weight
  information represent at each position i in
  the given window, defined as the difference
  between the maximum entropy and the entropy of
  the observed weight distribution
• Saliency factor of residue j at window position
  i
• New input

M the window size, 1 i M N the of
distinct residue symbols, 1 j N H
the of hidden units, 1 k H
Adopted from Jeong and Miyano, 2006
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Notations

• Four kinds of measuring parameters are defined
• True Positive (TP) the number of accurately
  predicted interaction sites
• True Negative (TN) the number of accurately
  predicted not-interaction sites
• False Positive (FP) the number of inaccurately
  predicted interaction sites
• False Negative (FN) the number of inaccurately
  predicted not-interaction sites
• Examples (1 positive, 0 negative)
  0101000010011001111000 ?
  Observed
  1100001110001111110011 ? Predicted

TN
TP
FP
FN
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Measuring Performance

• Total accuracy
• Percentage of all correctly predicted interaction
  and not-interaction sites
• Accuracy (Specificity)
• To measure the probability that how many of the
  predicted interaction sites are correct
• Coverage (Sensitivity)
• To measure the probability that how many of the
  correct interaction sites are predicted
• Mattews correlation coefficient (MCC)
• Takes into account both under- and
  over-predictions
• ranges between 1 (perfect prediction) and -1
  (completely wrong prediction)

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Receiver Operating Characteristic (ROC) Curve
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Experimental Results
Adopted from Jeong and Miyano, 2006
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Experimental Results (contd)
Adopted from Jeong and Miyano, 2006
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Experimental Results (contd)
underpredicted
interaction
overpredicted
not-interaction
Adopted from Jeong and Miyano, 2006
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References

• E. Jeong, I F. Chung, and S. Miyano, Prediction
  of Residues in Protein-RNA Interaction Sites by
  Neural Networks, Proc. of the 14th International
  Conference on Genome Informatics, pp. 506-507,
  2003.
• E. Jeong, I F. Chung, and S. Miyano, A Neural
  Network Method for Identification of
  RNA-Interacting Residues in Protein, Proc. of
  the 4th International Workshop on Bioinformatics
  and Systems Biology, pp. 105-116, 2004.
• E. Jeong and S. Miyano, A weighted profile based
  method for protein-RNA interacting residue
  prediction, Trans. on Comput. Syst. Biol., IV,
  LNBI 3939, pp. 123 - 139, 2006.

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Current Projects

• To discover the relationship between protein
  sequence and protein structure
• To identification of RNA-interacting residues in
  protein
• To perform protein metal binding residue
  prediction
• To predict the phosphorylation sites
• Microarray data analysis
• Significant gene selection, clustering,
  classification
• Prediction of the polymorphic short tandem
  repeats

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Mini-Workshop Knowledge Discovery Techniques
for Bioinformatics
Dr. Limsoon Wong
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Hierarchy of Protein Structure
2nd structure prediction
3rd structure prediction
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Protein Secondary Structures
Anti-parallel beta sheet
Alpha helix
loop
Parallel beta sheet