cancer detection - formal version.pptx

7/26/2019 cancer detection - formal version.pptx

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Cancerdetection

By : gierminsahagun

11337710


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This exampledemonstrates using a

neural network todetect cancer from

mass spectrometrydata on protien

proles.


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What is cancer ?

Is a term used fordiseases in whichabnormal cells divide

without control andare able to invadeother tissues.

Cancer cells can

spread to other partsof the body throughthe bloodand lymph systems


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"andom facts aboutcancer

&he ma'ority of cancer sur(i(ors )#"$* werediagnosed + or more years ago.

,early half )"#$* of cancer sur(i(ors are 70years of age or older

&obacco use is the cause of about 22$ ofcancer deaths.

Another 10$ is due to obesity- a %oor diet- lac

of %hysical acti(ity- and drining alcohol. /ther factors include certain infections-

e%osure to ioniing radiation- anden(ironmental %ollutants.


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Introductio

n

#erum proteomic pattern diagnosticscan be used to di$erentiate samplesfrom patients with and without

disease. %role patterns aregenerated using surface&enhancedlaser desorption and ioni'ation(#)*!I+ protein mass spectrometry.

This technology has the potential toimprove clinical diagnostics tests forcancer pathologies.


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The %roblem, Cancer !etection

&he goal is to build a classier that candistinguish between cancer and control%atients from the mass s%ectrometry data.

&he methodology followed in this eam%leis to select a reduced set of measurementsor features that can be used todistinguish between cancer and control

%atients using a classier. &hese features will be ion intensity le(els at

s%ecic mass4charge (alues.


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-ormatti

ng the

!ata


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&o recreate the datain ovariandataset.matused in this

eam%le- download and uncom%ress theraw mass5s%ectrometry data from the 6A5,C web site. Create the datale /varianCancer010Cdataset.matby

either running scri%t msse2processinginBioinformatics &oolbo )&8* or by followingthe ste%s in theeam%lebiodistcompdemo)Batch%rocessing with %arallel com%uting*. &henew le contains (ariables 9- 8 and gr%.


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)ach column in 3 representsmeasurements taken from apatient. There

are 456 columnsin 3 representing 456patients7 out of which 545 are

ovarian cancer patientsand 89 are normal patients.


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)ach row in 3 represents the

ion intensity level at a specicmass&charge value indicatedin :;. There are 59


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The variable grp holds theindex information as to

which of these samplesrepresent cancer patientsand which ones represent

normal patients.


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"anking

=ey

-eatures


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"anking =ey-eatures

&his is a ty%ical classication%roblem in which the number offeatures is much larger than the

number of obser(ations- but in whichno single feature achie(es a correctclassication- therefore we need to

nd a classier which a%%ro%riatelylearns how to weight multi%lefeatures and at the same time

%roduce a generalied ma%%ing5


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A sim%le a%%roach for ndingsignicant features is to assume

that each 84 (alue isinde%endent and com%ute a two5way t5test. rankfeaturesreturnsan inde to the most signicant84 (alues- for instance 100

indices raned by the absolute(alue of the test statistic.


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&o nish recreating the datafrom ovariandataset.mat- loadthe /varianCancer010Cdataset.mat

andrankfeaturesfrom Bioinformatics&oolbo to choose 100 highest ranedmeasurements as in%uts .

ind ;ranfeatures)9-gr%-=/,


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The preprocessing steps from the

script and example listed above areintended to demonstrate a

representative set of possible pre&

processing and feature selectionprocedures. >sing di$erent steps orparameters may lead to di$erentand possibly improved results of

this example.


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Classication >sing a

-eed -orward euraletwork

,ow that you ha(e identied some

signicant features- you can use thisinformation to classify the cancerand normal sam%les.


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setdemorandstream)#72!!0+1*

Dince the neural networ is initialiedwith random initial weights- the

results after training the networ(ary slightly e(ery time the eam%leis run. &o a(oid this randomness- the

random seed is set to re%roduce thesame results e(ery time. Eowe(erthis is not necessary for your owna%%lications.


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A 15hidden layer feed forward neuralnetwor with + hidden layer neurons is

created and trained. &he in%ut and target sam%les are

automatically di(ided into training-(alidation and test sets. &he training set isused to teach the networ.

&raining continues as long as the networcontinues im%ro(ing on the (alidation set.

&he test set %ro(ides a com%letelyinde%endent measure of networ accuracy.


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net @ patternnet(9+A

view(net+

The input and output have si'esof < because the network has notyet been congured to match our

input and target data. This willhappen when the network istrained.


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ow the network is ready to be trained.The samples are automatically divided intotraining7 validation and test sets.

The training set is used to teach thenetwork. Training continues as long as thenetwork continues improving on thevalidation set.

The test set provides a completelyindependent measure of network accuracy.

The Training Tool shows the networkbeing trained and the algorithms used totrain it.

It also displays the training state duringtraining and the criteria which stoppedtraining will be highlighted in green.


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net-tr ; train)net--t*@

The buttons at the bottom openuseful plots which can be openedduring and after training. *inks

next to the algorithm names andplot buttons open

documentation on thosesubBects.


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&he trained neural networ can

now be tested with the testingsam%les we %artitioned from themain dataset.

&he testing data was not used intraining in any way and hence%ro(ides an out5of5sam%le

dataset to test the networ on.&his will gi(e us a sense of how

well the networ will do whentested with data from the real


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testI ; ):-tr.testnd*@ test& ;

t):-tr.testnd*@ test9 ; net)testI*@testClasses ; test9 J 0.+

The network outputs will be in

the range < to 57 so wethreshold them to get 5s and


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/ne measure of how well the neural networ has t thedata is the confusion %lot. Eere the confusion matri is%lotted across all sam%les.

&he confusion matri shows the %ercentages of correct andincorrect classications. Correct classications are thegreen sHuares on the matrices diagonal.

ncorrect classications form the red sHuares.

f the networ has learned to classify %ro%erly- the%ercentages in the red sHuares should be (ery small-indicating few misclassications.

f this is not the case then further training- or training anetwor with more hidden neurons- would be ad(isable.

%lotconfusion)test&-test9*


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Another measure of how well the neural

network has fit data is the receiver operating

characteristic plot. This shows how the false positive and true

positive rates relate as the thresholding of

outputs is varied from 0 to 1. The farther left and up the line is, the fewer

false positives need to be accepted in order to

get a high true positive rate.

The best classifiers will have a line going

from the bottom left corner, to the top left

corner, to the top right corner, or close to that.


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Class 1 indicate cancer %atiencts-

class 2 normal %atients.

plotroc(testT7test3+


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This example illustrated how

neural networks can be usedas classiers for cancerdetection.

/ne can also experimentusing techni2ues likeprincipal component analysisto reduce the dimensionalityof the data to be used forbuilding neural networks to


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"eferences

1 &.F. Conrads- et al.- Eigh5resolutionserum %roteomic features for o(ariandetection- >ndocrine5=elated Cancer- 11-

200"- %%. 1#3517!. 2 >.6. Fetricoin- et al.- ?se of %roteomic

%atterns in serum to identify o(ariancancer- Gancet- 3+)30#*- 2002- %%. +725

+77.

cancer detection - formal version.pptx

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