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Neural Network Applications
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Problems
Input transformation
Network Architectures
Assessing Performance
Lecture 10, CS567
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Problems
• Deducing the genetic code
• Predicting genes
• Predicting signal peptide cleavage sites
Lecture 10, CS567
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Deducing the genetic code
• Problem: Given a codon, predict corresponding amino acid
• Of didactic value
– Trivial mapping table, after-the-fact
• Perfect classification problem, rather than prediction
– With minimal network
• Learning issues
– ‘Similar’ codons code for ‘similar’ amino acids
– Abundance of amino acids proportional to code
redundancy (this and previous point undermine effect of
mutations)
– Third base ‘wobble’
– N:1 mapping between codon and amino acid
Lecture 10, CS567
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The genetic code
T
C
A
G
TTT Phe (F)
TTC "
T TTA Leu (L)
TTG "
TCT Ser (S)
TCC "
TCA "
TCG "
TAT Tyr (Y)
TAC
TAA Ter
TAG Ter
TGT Cys (C)
TGC
TGA Ter
TGG Trp (W)
CTT Leu (L)
CTC "
C CTA "
CTG "
CCT Pro (P)
CCC "
CCA "
CCG "
CAT His (H)
CAC "
CAA Gln (Q)
CAG "
CGT Arg (R)
CGC "
CGA "
CGG "
ATT Ile (I)
ATC "
A ATA "
ATG Met (M)
ACT Thr (T)
ACC "
ACA "
ACG "
AAT Asn (N)
AAC "
AAA Lys (K)
AAG "
AGT Ser (S)
AGC "
AGA Arg (R)
AGG "
GTT Val (V)
GTC "
G GTA "
GTG "
GCT Ala (A)
GCC "
GCA "
GCG "
GAT Asp (D)
GAC "
GAA Glu (E)
GAG "
GGT Gly (G)
GGC "
GGA "
GGG "
http://molbio.info.nih.gov/molbio/gcode.html
Lecture 10, CS567
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Network Architecture
• Orthogonal coding (4X3)
•  2 hidden neurons (Is this a linear or non-linear
problem?)
• 20 output neurons
– Winner takes all
• Total of  86 parameters (How?)
• FFBP
Lecture 10, CS567
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Deducing the genetic code (Fig 6.7)
Lecture 10, CS567
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Deducing the
genetic code
(Fig 6.8)
Lecture 10, CS567
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Improving classification error
• Training rate high for misclassified codons,
low otherwise (in addition to iteration
dependence)
• Balanced cycles (Balanced in terms of
amino acids, not codons)
• Adaptive training
– Present mis-classified examples more often
Lecture 10, CS567
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Is it a gene or not a gene?
• Approaches depend on
– Bias at junctions of coding and non-coding regions
• Donor (5’ end of intron) and acceptor sites (3’ end of intron) have
biases in composition (GT [junk]+ C/U+ AG)
– Bias in composition of coding regions (but not of noncoding regions, eg, introns)
• Exons are “regular guys”, introns are “freshman dorm rooms”
• Seen as GC bias, codon usage frequency and codon bias
– Inverse relationship between the two (splice site strength
and regularity within exons)
• “Food exit sign on highway doesn’t need prominent restaurant
signs”
• “Stretch of prominent restaurant signs doesn’t need a sign
indicating food”
Lecture 10, CS567
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Regularity within coding regions (Fig 6.11)
Bacteria
C. elegans
Lecture 10, CS567
Mammals
A. thaliana
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Predicting Exons: The holy GRAIL
• Neural networks for gene prediction
– Input representation/transformation key
– NN per se trivial: MLP with single hidden layer and single
output neuron
– Input = Coding region candidate, transformed to
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6mer (di-codon) score of candidate region
6mer (di-codon) score of flanking regions
GC composition of candidate region
GC composition of flanking region
Markov model score
Length of candidate
Splice site score
Lecture 10, CS567
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Signal peptide (SignalP) prediction
• Signal peptides are N-terminal subsequences in
proteins that are “export tags” including a “dotted
line” (cleavage site) indicating point of detachment
– Coding is species specific
• Problem analogous to exon/intron delineation
– Distinguish between signalP and rest of protein
– Find junction between signalP and rest of protein
Lecture 10, CS567
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Signal peptide (SignalP) prediction
• Two kinds of network that output, for each position,
– S-score: Probability of classification as signal peptide
– C-score: Probability of being the junction
• Key is post-processing – using S and C scores to come up
with final prediction
• C-score prediction: Based on Asymmetric windows (why?)
• S-score prediction: Based on Symmetric windows (why?)
• Y-score = (CidSi)1/2 where dSi = Average difference in Si
in windows of size d flanking position i
Lecture 10, CS567
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Signal peptide (SignalP) prediction (Fig 6.5)
S
S
Lecture 10, CS567
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