Transcript Convolutional LSTM Networks for Subcellular Localization of Proteins

Convolutional LSTM
Networks for Subcellular
Localization of Proteins
Søren Kaae Sønderby, Casper Kaae Sønderby,
Henrik Nielsen*, and Ole Winther
*Center
for Biological Sequence Analysis
Department of Systems Biology
Technical University of Denmark
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Protein sorting in eukaryotes
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Feed-forward Neural Networks
Problems for sequence
analysis:
• No builtin concept of
sequence
• No natural way of
handling sequences of
varying length
• No mechanism for
handling long range
correlations (beyond
input window size)
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LSTM networks
An LSTM (Long Short
Term Memory) cell
LSTM networks
• are easier to train than
other types of recurrent
neural networks
• can process very long
time lags of unknown size
between important events
• are used in speech
recognition, handwriting
recognition, and machine
translation
xt: input at time t
ht-1: previous output
i : input gate,
f : forget gate,
o: output gate,
g: input modulation gate,
c: memory cell.
The blue arrow
head refers to
ct−1.
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“Unrolled” LSTM network
Each square represents a layer
of LSTM cells at a particular
time (1, 2, ... t).
The target y is presented at the
final timestep.
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Regular LSTM networks
Bidirectional: one
target per position
Double unidirectional:
one target per
sequence
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Attention LSTM networks
Bidirectional, but
with one target per
sequence.
Align weights
determine where in
the sequence the
network directs its
attention.
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Convolutional Neural Networks
A convolutional layer in a neural network consists of small
neuron collections which look at small portions of the input
image, called receptive fields.
Often used in image processing, where they can handle
translation invariance.
First layer
convolutional
filters learned in
an image
processing
network, note that
many filters are
edge detectors or
color detectors
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Our basic model
Target prediction at t=T
Soft
max
FFN
t
t+1
LSTM
Conv.
xt
1D convolution
(variable width)
T
LSTM
Conv.
xt+1
……
LSTM
Conv.
xT
Conv.
weights
A
Y
K
P
xt-2
xt-1
xt
xt+1
W
xt+2
Note that conv. weights are shared across
sequence steps for the convolutional filters
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Our model, with attention
Encoder
Decoder
Soft
max
Att. Weighting
over sequence
positions
𝛼t
t
𝛼t+1
Atten
tion
Atten
tion
ht
ht+1
𝛼T
Atten
tion
FFN
Weighted
hidden average
T
t+1
LSTM
LSTM
Conv.
Conv.
xt
Target
prediction
xt+1
……
LSTM
Conv.
xT
……
hT
Vectors containing the
activations in each LSTM
unit at each time step
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Our model, specifications
– Input encoding: Sparse, BLOSUM80, HSDM and
profile (R1×80)
– Conv. filter sizes: 1, 3, 5, 9, 15, 21 (10 of each)
– LSTM layer: 1×200 units
– Fully connected FFN layer: 1×200 units
– Attention model: Wa (R200×400), va (R1×200)
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MultiLoc architecture
MultiLoc is an SVM-based based predictor using only
sequence as input
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MultiLoc2 architecture
MultiLoc2 corresponds to MultiLoc + PhyloLoc + GOLoc.
Thus, its input is not only sequence, but also metadata
derived from homology searches.
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SherLoc2 architecture
SherLoc2 corresponds to MultiLoc2 + EpiLoc
EpiLoc = a prediction system based on features derived from PubMed
abstracts found through homology searches
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Results: performance
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Learned Convolutional Filters
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Learned Attention Weights
𝛼1 . . . . . . . . . 𝛼t . . . . . . . . . . 𝛼T
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t-SNE plot of LSTM representation
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Contributions
1. We show that LSTM networks combined with convolutions
are efficient for predicting subcellular localization of proteins
from sequence.
2. We show that convolutional filters can be used for amino
acid sequence analysis and introduce a visualization
technique.
3. We investigate an attention mechanism that lets us
visualize where the LSTM network focuses.
4. We show that the LSTM network effectively extracts a fixed
length representation of variable length proteins.
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Acknowledgments
Thanks to:
•
Søren & Casper Kaae Sønderby,
for doing the actual implementation and training
•
Ole Winther
for supervising Søren & Casper
•
Søren Brunak
for introducing me to the world of neural networks
•
The organizers
for accepting our paper
•
You
for listening!
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