Convolutional and LSTM Neural Networks

Vanessa Jurtz

January 12, 2016

Contents

I Neural networks and GPUs

I Lasagne

I Peptide binding to MHC class II molecules

I Convolutional Neural Networks (CNN)

I Recurrent and LSTM Neural Networks

I Hyper-parameter optimization

I Training CNN and LSTM ensembles

Neural networks and GPUs

I A CPU (central processing unit)has few cores with lots of cachememory

I A GPU (graphics processing unit)has hundreds of cores but eachwith little memory

I GPUs are very good for lots ofsmall operations that can beheavily parallelized, like matrixmultiplication

I training a neural network requireslots of matrix multiplications!

Lasagne

Why use Lasagne?

I python

I it is highly flexible as you will see in the afternoon exercise

I code can be run on a CPU or GPU

I my collaborators are using it too

Lasagne Documentation

Peptide binding to MHC class II molecules

Figure adapted from http://events.embo.org/12-antigen/

I MHC II only expressed inprofessional antigenpresenting cells (dentriticcells, macrophages, B cellsetc)

I only a small fraction ofpeptides bind MHC II andare potential T cellepitopes

I polygenic and highlypolymorphic

Peptides binding to MHCII molecules

Nielsen et al. 2010 ”MHC Class II epitope predictivealgorithms” Immunology

Peptides binding HLA-DR3 adapted from KennethMurphy ”Janeway’s Immunobiology ” 8th edition fig4.21 Garland Science

Why is this a hard problem?

I binding peptides have different lengths

I the binding core can lie anywhere within a peptide

I large number of different MHC class II molecules

CNN - Convolutional Neural Networks

I convolutional filter hasspecified input size

I slides over input sequencefeeding into a differenthidden neuron in eachposition

I weights of the filter arefixed as it slides over inputand updated inbackpropagation

I usually multiple filters ofthe same and/or differentsize are used

CNN - Convolutional Neural Networks

Why could this be useful forpredicting peptide binding toMHC class II?

I use filters of size 9 AA tofocus on binding core

I integrate informationabout all 9-mers

CNN - Convolutional Neural Networks

I CNNs are known to work well for image analysis

I CIFAR example

http://deeplearning.net/tutorial/lenet.html

CNN - Convolutional Neural Networks

A biological example: subcellular localization of proteins

Sønderby et al. 2015 ”Convolutional LSTM Networks for Subcellular Localization of Proteins”http://arxiv.org/abs/1503.01919

Recurrent Neural Networks

I have connections betweenthe neurons in the hiddenlayer

I have a memory ”learnwhat to store and what toignore” (Graves, 2015)

Graves 2015 ”Supervised Sequence Labelling with Recurrent Neural Networks”

LSTM memory blocks

Instead of a hidden neuron we use an LSTM memory block:

Graves 2015 ”Supervised Sequence Labelling with Recurrent Neural Networks”

LSTM Networks

Why make it more complicated?

Graves 2015 ”Supervised Sequence Labelling with Recurrent Neural Networks”

LSTM memory blocks allow tostore and access informationover longer periods of time

LSTM Networks

Input and output can be of variable length!

http://karpathy.github.io/2015/05/21/rnn-effectiveness/

Examplepeptide MHCclass II binding:many to one

LSTM Networks

I LSTM networks are known to work well for natural languageprocessing

I check out Andrej Karpathys blog

I to read up: Graves 2015

LSTM Networks

Another biological application: protein secondary structure prediction

http://chemistry.berea.edu/~biochemistry/

Anthony/Aequorin/Aequorin3.php Sønderby et al. 2015 ”Convolutional LSTM Networksfor Subcellular Localization of Proteins”http://arxiv.org/abs/1503.01919

Hyper-parameter optimization

Parameters:

I weights

Hyper-parameters:

I weight initialization

I weight update

I activation function

I data encoding

I dropout

I learning rate

I number of CNN filters / number of LSTM memory blocks

I hidden layer size

Bergstra et al 2012 ”Random Search for Hyper-ParameterOptimization” Journal of Machine Learning Research

Hyper-parameter optimization- architectures

CNN:

Peptide

Convolutional layer MHC II pseudo sequence

Fully connected layer

Output

filter size varied (9 AA,9+3 AA)

LSTM:

Hyper-parameter optimization- results

CNN:

1 2 4 6 8 10 20 50 70 100number of trials

0.4

0.5

0.6

0.7

0.8

0.9

AU

C

Best performance per number of trials

LSTM:

1 2 4 6 8 10 20 50 100 150 200number of trials

0.4

0.5

0.6

0.7

0.8

0.9

AU

C

Best performance per number of trials

single network performance (10% best nets for CNN +LSTM, entire ensemble of NetMHCIIpan-3.0)

CNN LSTM NetMHCIIpan-3.00.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

PC

C

validationevaluation

Training CNN and LSTM ensembles

CNN ensemble of 10 best hyper parameter settings + 5 seeds each

Training CNN and LSTM ensembles

LSTM ensemble of 2 best hyper parameter settings + 2 seeds each

Future work

I combine CNN and LSTM in one network

I make an ensemble of different network architectures: CNN,LSTM, feed forward

I try to visualize what the networks learn

I try to find a way to extract/visualize the binding core

Aknowledgements

I Casper Sønderby

I Søren Sønderby

I Ole Winther

I Morten Nielsen